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1
Liu, Jun, Xiaohui Lian, and Chang Liu. "Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network." Future Internet 13, no. 5 (May 13, 2021): 128. http://dx.doi.org/10.3390/fi13050128.
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In Space–Air–Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading, which caused by the complex and variable network offloading environment and a large amount of offloading tasks, a computation offloading decision scheme based on Markov and Deep Q Networks (DQN) is proposed. First, we select the optimal offloading network based on the characteristics of the movement of the task offloading process in the network. Then, the task offloading process is transformed into a Markov state transition process to build a model of the computational offloading decision process. Finally, the delay and energy consumption weights are introduced into the DQN algorithm to update the computation offloading decision process, and the optimal offloading decision under the low cost is achieved according to the task attributes. The simulation results show that compared with the traditional Lyapunov-based offloading decision scheme and the classical Q-learning algorithm, the delay and energy consumption are respectively reduced by 68.33% and 11.21%, under equal weights when the offloading task volume exceeds 500 Mbit. Moreover, compared with offloading to edge nodes or backbone nodes of the network alone, the proposed mixed offloading model can satisfy more than 100 task requests with low energy consumption and low delay. It can be seen that the computation offloading decision proposed in this paper can effectively reduce the delay and energy consumption during the task computation offloading in the Space–Air–Ground Integrated Network environment, and can select the optimal offloading sites to execute the tasks according to the characteristics of the task itself.
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Yao, Bingxin, Bin Wu, Siyun Wu, Yin Ji, Danggui Chen, and Limin Liu. "An Offloading Algorithm based on Markov Decision Process in Mobile Edge Computing System." International Journal of Circuits, Systems and Signal Processing 16 (January 5, 2022): 115–21. http://dx.doi.org/10.46300/9106.2022.16.15.
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In this paper, an offloading algorithm based on Markov Decision Process (MDP) is proposed to solve the multi-objective offloading decision problem in Mobile Edge Computing (MEC) system. The feature of the algorithm is that MDP is used to make offloading decision. The number of tasks in the task queue, the number of accessible edge clouds and Signal-Noise-Ratio (SNR) of the wireless channel are taken into account in the state space of the MDP model. The offloading delay and energy consumption are considered to define the value function of the MDP model, i.e. the objective function. To maximize the value function, Value Iteration Algorithm is used to obtain the optimal offloading policy. According to the policy, tasks of mobile terminals (MTs) are offloaded to the edge cloud or central cloud, or executed locally. The simulation results show that the proposed algorithm can effectively reduce the offloading delay and energy consumption.
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Thakur, Pawan Kumar, and Amandeep Verma. "Process Batch Offloading Method for Mobile-Cloud Computing Platform." Journal of Cases on Information Technology 17, no. 3 (July 2015): 1–13. http://dx.doi.org/10.4018/jcit.2015070101.
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Mobile cloud applications transfers the computational power and data storage outside the mobile device and into the mobile cloud, getting mobile computing and mobile applications to not handheld devices users but a wider choice of mobile subscribers. Process offloading is the technique in which some part of the application is transferred into the mobile cloud for execution. Many applications like GPS, face recognition, video editing etc. consumes more battery of the mobile devices. By offloading the power hunger part to the cloud is one of the approach to elongate the battery lifetime of mobile devices. The major goal of the proposed model is to combine the similar processes into single batch and offload the batch into cloud rather than offloading a single process into cloud. A MPCEPGM (Multilevel Process Cost Evaluation with Process Group Merging) algorithm is proposed for application partitioning and offloading to the cloud. MPCEPGM will predict the overall execution cost of the whole batch or process tree as single entity. This will help the mobile offloading procedure to organize the processes according to their delivery time. Proposed model is energy efficient to deliver the data effectively to the mobile cloud. The performance of the proposed system is assessed on the basis of total execution time and communication cost using Matlab simulations.
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Pereira, Felipe R., Carlos H. Fucatu, and Andrey AssumpçÃo. "Evaluation of the FPSO Polvo Offloading Process." IFAC Proceedings Volumes 42, no. 18 (2009): 152–56. http://dx.doi.org/10.3182/20090916-3-br-3001.0070.
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Kelle, Holger, Mikhail Santosa, and Anne Barthelemy. "Project integrated LNG offloading availability assessment for FLNG." APPEA Journal 54, no. 2 (2014): 542. http://dx.doi.org/10.1071/aj13115.
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This extended abstract explains a combined heuristic, analytical, and probabilistic process to evaluate LNG offshore offloading availability in combination with facility uptime and commercial drivers such as LNG sales/supply contracts. The heuristic assessment is informed by facility operators’, LNGC masters’, and tug operators’ experiences in offshore offloading and berthing operations. The analytical process includes assessment of met-ocean, mooring, manoeuvrability simulation, model testing and event forecasting methods. Gaps about uncertainties for future predictions are filled by probabilistic Monte-Carlo simulations. The heuristic, analytical, and probabilistic approach, combined with commercial drivers, is put together into uptime assessment to forecast the techno-commercial performance of the facility. The uptime assessment enables: confidence on achievable LNG throughput, the best for facility configuration and size, the best for facility location and facility’s operational expenditures; contractual viability—for LNG supplier and gas off-taker; and, key to terminal performance guarantee to gas off-takers. This process has been developed within INTECSEA during the past six years and has been applied to more than 15 LNG offshore offloading facilities at varying geographical locations. This extended abstract explores the key drivers and describes the effect on those key drivers due to varying location, varying technology, or LNG sales/supply contracting strategy. The key drivers include: achievable LNG throughput, uptime, downtime, demurrage, cargo cancellation, facility downturn, and partial LNG offloading. The process described is specific to side-by-side offloading operations; however, it can also be adapted to standard jetty offloading operations and tandem offloading operations.
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Bai, Wenle, Zhongjun Yang, Jianhong Zhang, and Rajiv Kumar. "Randomization-Based Dynamic Programming Offloading Algorithm for Mobile Fog Computing." Security and Communication Networks 2021 (August 30, 2021): 1–9. http://dx.doi.org/10.1155/2021/4348511.
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Offloading to fog servers makes it possible to process heavy computational load tasks in local devices. However, since the generation problem of offloading decisions is an N-P problem, it cannot be solved optimally or traditionally, especially in multitask offloading scenarios. Hence, this paper has proposed a randomization-based dynamic programming offloading algorithm, based on genetic optimization theory, to solve the offloading decision generation problem in mobile fog computing. The algorithm innovatively designs a dynamic programming table-filling approach, i.e., iteratively generates a set of randomized offloading decisions. If some in these sets improve the decisions in the DP table, then they will be merged into the table. The iterated DP table is also used to improve the set of decisions generated in the iteration to obtain the optimal offloading approximate solution. Extensive simulations show that the proposed DPOA can generate decisions within 3 ms and the benefit is especially significant when users are in multitask offloading scenarios.
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Dash, Sanjit Kumar, Aiswaryalaxmi Pradhan, Sasmita Mishra, and Jibitesh Mishra. "Lightweight Opportunistic Mobile Data Offloading." International Journal of Mobile Devices, Wearable Technology, and Flexible Electronics 9, no. 1 (January 2018): 1–15. http://dx.doi.org/10.4018/ijmdwtfe.2018010101.
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Current cellular networks are overloaded due to the increasing number of smartphones and demands for bandwidth-eager multimedia content. Upgrading the existing infrastructure of the cellular system is the most straight forward solution to meet the growing demand. Apart from this, offloading mobile data through Wi-Fi can be a feasible solution. Mobile offloading via Wi-Fi is the latest emerging trend in research and industry. In this article, the authors have proposed a framework for mobile data offloading for both cellular and Wi-Fi networks. The authors have introduced a daemon process-based approach to make the entire process lightweight by using a suitable offloading decision algorithm. This article then formulates a mathematical model to evaluate its feasibility and accuracy for achieving optimum performance.
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Liu, Bin, Qi Zhu, Weiqiang Tan, and Hongbo Zhu. "Congestion-Optimal WiFi Offloading with User Mobility Management in Smart Communications." Wireless Communications and Mobile Computing 2018 (August 1, 2018): 1–15. http://dx.doi.org/10.1155/2018/9297536.
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We study the WiFi offloading problem in smart communications and adaptively seek for the optimal offloading strategies with the consideration of the mobility management and the dynamical nature of network state. With users mobility management, we formulate the offloading ratio optimization problem based on Markov process. Then, we propose a novel Congestion-Optimal WiFi Offloading (COWO) algorithm based on subgradient method, which aims to obtain the optimal offloading ratio for each access point (AP) to maximize the throughput and minimize the network congestion. Due to the computational complexity of subgradient method, we further improve the COWO algorithm by the equivalent transformation. By viewing all the APs as one virtual WiFi network, we try to optimize the identical offloading ratio for virtual WiFi network and develop a Virtualized Congestion-Optimal WiFi Offloading (VCOWO) algorithm with lower complexity. Under the equivalent conditions, the performance of the VCOWO algorithm could well approximate the optimal results obtained by the COWO algorithm. It is found that the VCOWO algorithm could obtain the upper bound of multiple APs WiFi offloading performance. Moreover, we investigate the impacts of user mobility on the WiFi offloading performance. Simulation results show that the proposed algorithm could achieve higher throughput with lower network congestion compared with other current offloading schemes.
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Kou, Jinfeng, Yang Xiao, and Dong Wang. "An Economic User-Centric WiFi Offloading Algorithm for Heterogeneous Network." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/341292.
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An economic user-centric WiFi offloading algorithm is proposed to satisfy the major concerns of wireless users, who wish to have better network performance with even less network expense. Thus in this paper both system throughput and network expense are considered, and the goal of the proposed offloading algorithm is to obtain an optimal offloading ratio, which can both maximize the system throughput and minimize the network expense. Firstly, a practical system model is set up on the basis of a typical scenario of heterogeneous network. In this model, the average throughput of both cellular network and WiFi network is analyzed carefully. Then an economic user-centric WiFi offloading algorithm is proposed with an evaluation function to evaluate the system, and the optimal offloading ratio can be obtained by minimizing the evaluation function. At last, numerical results represent a direct calculating process of the optimal offloading ratio. These results in return validate the efficiency of the proposed offloading algorithm as well.
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Wang, Qian, Juan Fang, Bei Gong, Xiaojiang Du, and Mohsen Guizani. "An Intelligent Data Uploading Selection Mechanism for Offloading Uplink Traffic of Cellular Networks." Sensors 20, no. 21 (November 4, 2020): 6287. http://dx.doi.org/10.3390/s20216287.
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Wi-Fi uploading is considered an effective method for offloading the traffic of cellular networks generated by the data uploading process of mobile crowd sensing applications. However, previously proposed Wi-Fi uploading schemes mainly focus on optimizing one performance objective: the offloaded cellular traffic or the reduced uploading cost. In this paper, we propose an Intelligent Data Uploading Selection Mechanism (IDUSM) to realize a trade-off between the offloaded traffic of cellular networks and participants’ uploading cost considering the differences among participants’ data plans and direct and indirect opportunistic transmissions. The mechanism first helps the source participant choose an appropriate data uploading manner based on the proposed probability prediction model, and then optimizes its performance objective for the chosen data uploading manner. In IDUSM, our proposed probability prediction model precisely predicts a participant’s mobility from spatial and temporal aspects, and we decrease data redundancy produced in the Wi-Fi offloading process to reduce waste of participants’ limited resources (e.g., storage, battery). Simulation results show that the offloading efficiency of our proposed IDUSM is (56.54×10−7), and the value is the highest among the other three Wi-Fi offloading mechanisms. Meanwhile, the offloading ratio and uploading cost of IDUSM are respectively 52.1% and (6.79×103). Compared with other three Wi-Fi offloading mechanisms, it realized a trade-off between the offloading ratio and the uploading cost.
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Dai, Zuojun, Ying Zhou, Hui Tian, and Nan Ma. "Task-Offloading and Resource Allocation Strategy in Multidomain Cooperation for IIoT." Processes 11, no. 1 (January 2, 2023): 132. http://dx.doi.org/10.3390/pr11010132.
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This study proposes a task-offloading and resource allocation strategy in multidomain cooperation (TARMC) for the industrial Internet of Things (IIoT) to resolve the problem of the non-uniform distribution of task computation among various cluster domain networks in the IIoT and the solidification of traditional industrial wireless network architecture, which produces low efficiency of static resource allocation and high delay in closed-loop data processing. Based on the closed-loop process of task interaction of intelligent terminals in wireless networks, the proposed strategy constructs a network model of multidomain collaborative task-offloading and resource allocation in IIoT for flexible and dynamic resource allocation among intelligent terminals, edge servers, and cluster networks. Considering the partial offloading mechanism, various tasks were segmented into multiple subtasks marked at bit-level per demand, which enabled local and edge servers to process all subtasks in parallel. Moreover, this study established a utility function for the closed-loop delay and terminal energy consumption of task processing, which transformed the process of multidomain collaborative task-offloading and resource allocation into the problem of task computing revenue. Furthermore, an improved Cuckoo Search algorithm was developed to derive the optimal offloading position and resource allocation decision through an alternating iterative method. The simulation results revealed that TARMC performed better than strategies.
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Pang, Shanchen, Huanhuan Sun, Min Wang, Shuyu Wang, Sibo Qiao, and Neal N. Xiong. "An Efficient Computing Offloading Scheme Based on Privacy-Preserving in Mobile Edge Computing Networks." Wireless Communications and Mobile Computing 2022 (June 14, 2022): 1–15. http://dx.doi.org/10.1155/2022/5152598.
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Computation offloading is an important technology to achieve lower delay communication and improve the experience of service (EoS) in mobile edge computing (MEC). Due to the openness of wireless links and the limitation of computing resources in mobile computing process, the privacy of users is easy to leak, and the completion time of tasks is difficult to guarantee. In this paper, we propose an efficient computing offloading algorithm based on privacy-preserving (ECOAP), which solves the privacy problem of offloading users through the encryption technology. To avoid the algorithm falling into local optimum and reduce the offloading user energy consumption and task completion delay in the case of encryption, we use the improved fast nondominated sorting genetic algorithm (INSGA-II) to obtain the optimal offloading strategy set. We obtain the optimal offloading strategy by using the methods of min-max normalization and simple additive weighting based on the optimal offloading strategy set. The ECOAP algorithm can preserve user privacy and reduce task completion time and user energy consumption effectively by comparing with other algorithms.
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Shen, Hui, Yujing Jiang, Fangming Deng, and Yun Shan. "Task Unloading Strategy of Multi UAV for Transmission Line Inspection Based on Deep Reinforcement Learning." Electronics 11, no. 14 (July 12, 2022): 2188. http://dx.doi.org/10.3390/electronics11142188.
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Due to the limitation of the computing power and energy resources, an unmanned aerial vehicle (UAV) team usually offloads the inspection task to the cloud for processing when performing emergency fault inspection, which will lead to low efficiency of transmission line inspection. In order to solve the above problems, this paper proposes a task offloading strategy based on deep reinforcement learning (DRL), aiming for the application of a multi-UAV and single-edge server. First, a “device-edge-cloud” collaborative offloading architecture is constructed in the UAV edge environment. Secondly, the problem of offloading power line inspection tasks is classified as an optimization problem to obtain the minimum delay under the constraints of edge server computing and communication resources. Finally, the problem is constructed as a Markov decision, and a deep Q-network (DQN) is used to obtain the minimum delay of the system. In addition, an experience replay mechanism and a greedy algorithm are introduced in the learning process to improve the offloading accuracy. The experimental results show that the proposed offloading strategy in this paper saves 54%, 37% and 26% of the task completion time, respectively, compared with local offloading, cloud offloading and random offloading. It effectively reduces the UAV inspection delay and improves the transmission line inspection efficiency.
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Chen, Shuang, Ying Chen, Xin Chen, and Yuemei Hu. "Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks." Complexity 2020 (May 4, 2020): 1–14. http://dx.doi.org/10.1155/2020/7016307.
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With the explosion of data traffic, mobile edge computing (MEC) has emerged to solve the problem of high time delay and energy consumption. In order to cope with a large number of computing tasks, the deployment of edge servers is increasingly intensive. Thus, server service areas overlap. We focus on mobile users in overlapping service areas and study the problem of computation offloading for these users. In this paper, we consider a multiuser offloading scenario with intensive deployment of edge servers. In addition, we divide the offloading process into two stages, namely, data transmission and computation execution, in which channel interference and resource preemption are considered, respectively. We apply the noncooperative game method to model and prove the existence of Nash equilibrium (NE). The real-time update computation offloading algorithm (RUCO) is proposed to obtain equilibrium offloading strategies. Due to the high complexity of the RUCO algorithm, the multiuser probabilistic offloading decision (MPOD) algorithm is proposed to improve this problem. We evaluate the performance of the MPOD algorithm through experiments. The experimental results show that the MPOD algorithm can converge after a limited number of iterations and can obtain the offloading strategy with lower cost.
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Shan, Nanliang, Yu Li, and Xiaolong Cui. "A Multilevel Optimization Framework for Computation Offloading in Mobile Edge Computing." Mathematical Problems in Engineering 2020 (June 27, 2020): 1–17. http://dx.doi.org/10.1155/2020/4124791.
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Mobile edge computing is a new computing paradigm that can extend cloud computing capabilities to the edge network, supporting computation-intensive applications such as face recognition, natural language processing, and augmented reality. Notably, computation offloading is a key technology of mobile edge computing to improve mobile devices’ performance and users’ experience by offloading local tasks to edge servers. In this paper, the problem of computation offloading under multiuser, multiserver, and multichannel scenarios is researched, and a computation offloading framework is proposed that considering the quality of service (QoS) of users, server resources, and channel interference. This framework consists of three levels. (1) In the offloading decision stage, the offloading decision is made based on the beneficial degree of computation offloading, which is measured by the total cost of the local computing of mobile devices in comparison with the edge-side server. (2) In the edge server selection stage, the candidate is comprehensively evaluated and selected by a multiobjective decision based on the Analytic Hierarchy Process based on Covariance (Cov-AHP) for computation offloading. (3) In the channel selection stage, a multiuser and multichannel distributed computation offloading strategy based on the potential game is proposed by considering the influence of channel interference on the user’s overall overhead. The corresponding multiuser and multichannel task scheduling algorithm is designed to maximize the overall benefit by finding the Nash equilibrium point of the potential game. Amounts of experimental results show that the proposed framework can greatly increase the number of beneficial computation offloading users and effectively reduce the energy consumption and time delay.
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Elgendy, Ibrahim A., Souham Meshoul, and Mohamed Hammad. "Joint Task Offloading, Resource Allocation, and Load-Balancing Optimization in Multi-UAV-Aided MEC Systems." Applied Sciences 13, no. 4 (February 17, 2023): 2625. http://dx.doi.org/10.3390/app13042625.
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Due to their limited computation capabilities and battery life, Internet of Things (IoT) networks face significant challenges in executing delay-sensitive and computation-intensive mobile applications and services. Therefore, the Unmanned Aerial Vehicle (UAV) mobile edge computing (MEC) paradigm offers low latency communication, computation, and storage capabilities, which makes it an attractive way to mitigate these limitations by offloading them. Nevertheless, the majority of the offloading schemes let IoT devices send their intensive tasks to the connected edge server, which predictably limits the performance gain due to overload. Therefore, in this paper, besides integrating task offloading and load balancing, we study the resource allocation problem for multi-tier UAV-aided MEC systems. First, an efficient load-balancing algorithm is designed for optimizing the load among ground MEC servers through the handover process as well as hovering UAVs over the crowded areas which are still loaded due to the fixed location of the ground base stations server (GBSs). Moreover, we formulate the joint task offloading, load balancing, and resource allocation as an integer problem to minimize the system cost. Furthermore, an efficient task offloading algorithm based on deep reinforcement learning techniques is proposed to derive the offloading solution. Finally, the experimental results show that the proposed approach not only has a fast convergence performance but also has a significantly lower system cost when compared to the benchmark approaches.
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Chu, Xiao, and Ze Leng. "Multiuser Computing Offload Algorithm Based on Mobile Edge Computing in the Internet of Things Environment." Wireless Communications and Mobile Computing 2022 (March 3, 2022): 1–9. http://dx.doi.org/10.1155/2022/6107893.
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As traditional cloud computing is not efficient enough to support large-scale computational task execution in IoT environments, a task offloading and resource allocation algorithm for mobile edge computing (MEC) is proposed in this paper. First, a multiuser computation offloading model is constructed, including a communication model and computation offloading model, which is transformed into the minimization of users’ time delay and energy consumption (i.e., total system overhead) in the MEC system. Then, the task offloading model is formulated into a Markov decision process, and an offloading strategy based on a deep Q network (DQN) is designed to dynamically make fine tunings on the offloading proportion of each user so as to realize a low-cost MEC system. The proposed algorithm is analyzed based on the constructed simulation platform. The simulation results show that when the number of user terminals is 40, the average delay of the proposed algorithm does not exceed 0.9 s, and the average energy consumption tends to 65 J, which is better than the comparison method. Therefore, the proposed algorithm has certain application prospects.
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Tu, Youpeng, Haiming Chen, Linjie Yan, and Xinyan Zhou. "Task Offloading Based on LSTM Prediction and Deep Reinforcement Learning for Efficient Edge Computing in IoT." Future Internet 14, no. 2 (January 18, 2022): 30. http://dx.doi.org/10.3390/fi14020030.
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In IoT (Internet of Things) edge computing, task offloading can lead to additional transmission delays and transmission energy consumption. To reduce the cost of resources required for task offloading and improve the utilization of server resources, in this paper, we model the task offloading problem as a joint decision making problem for cost minimization, which integrates the processing latency, processing energy consumption, and the task throw rate of latency-sensitive tasks. The Online Predictive Offloading (OPO) algorithm based on Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) networks is proposed to solve the above task offloading decision problem. In the training phase of the model, this algorithm predicts the load of the edge server in real-time with the LSTM algorithm, which effectively improves the convergence accuracy and convergence speed of the DRL algorithm in the offloading process. In the testing phase, the LSTM network is used to predict the characteristics of the next task, and then the computational resources are allocated for the task in advance by the DRL decision model, thus further reducing the response delay of the task and enhancing the offloading performance of the system. The experimental evaluation shows that this algorithm can effectively reduce the average latency by 6.25%, the offloading cost by 25.6%, and the task throw rate by 31.7%.
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Abbas, Ziaul Haq, Zaiwar Ali, Ghulam Abbas, Lei Jiao, Muhammad Bilal, Doug-Young Suh, and Md Jalil Piran. "Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach." Sensors 21, no. 10 (May 19, 2021): 3523. http://dx.doi.org/10.3390/s21103523.
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In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy consumption of UEs. We use deep learning to find, simultaneously, the best partitioning of a single task with the best offloading policy. The deep neural network (DNN) is trained through a comprehensive dataset, generated from our mathematical model, which reduces the time delay and energy consumption of the overall process. Due to the complexity and computation of the mathematical model in the algorithm being high, due to trained DNN the complexity and computation are minimized in the proposed work. We propose a comprehensive cost function, which depends on various delays, energy consumption, radio resources, and computation resources. Furthermore, the cost function also depends on energy consumption and delay due to the task-division-process in partial offloading. None of the literature work considers the partitioning along with the computational offloading policy, and hence, the time and energy consumption due to task-division-process are ignored in the cost function. The proposed work considers all the important parameters in the cost function and generates a comprehensive training dataset with high computation and complexity. Once we get the training dataset, then the complexity is minimized through trained DNN which gives faster decision making with low energy consumptions. Simulation results demonstrate the superior performance of the proposed technique with high accuracy of the DNN in deciding offloading policy and partitioning of a task with minimum delay and energy consumption for UE. More than 70% accuracy of the trained DNN is achieved through a comprehensive training dataset. The simulation results also show the constant accuracy of the DNN when the UEs are moving which means the decision making of the offloading policy and partitioning are not affected by the mobility of UEs.
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Huang, Xiaoge, Xuesong Deng, Chengchao Liang, and Weiwei Fan. "Blockchain-Enabled Task Offloading and Resource Allocation in Fog Computing Networks." Wireless Communications and Mobile Computing 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/7518534.
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To address the data security and user privacy issues in the task offloading process and resource allocation of the fog computing network, a blockchain-enabled fog computing network task offloading model is proposed in this paper. Furthermore, to reduce the network utility which is defined as the total energy consumption of the fog computing network and the total delay of the blockchain network, a blockchain-enabled fog computing network task offloading and resource allocation algorithm (TR-BFCN) is proposed to jointly optimize the task offloading decision and resource allocation. Finally, the original nonconvex optimization problem is converted into two suboptimization problems, namely, task offloading decisions and computational resource allocations. Moreover, a two-stage Stackelberg game model is designed to obtain the optimal amount of purchased resource and the optimal resource pricing. Simulation results show that the proposed TR-BFCN algorithm can effectively reduce the network utility compared with other algorithms.
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Chen, Jiadi, and Wenbo Wang. "A Novel Execution Mode Selection Scheme for Wireless Computing." International Journal of Antennas and Propagation 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/106053.
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Computation offloading is an effective way to alleviate the resource limited problem of mobile devices. However, the offloading is not an always advantageous strategy for under some circumstances the overhead in time and energy may turn out to be greater than the offloading savings. Therefore, an offloading decision scheme is in demand for mobile devices to decide whether to offload a computation task to the server or to execute it in a local processor. In this paper, the offloading decision problem is translated into a dynamic execution mode selection problem, the objective of which is to minimize the task execution delay and reduce the energy consumption of mobile devices. A novel execution mode adjustment mechanism is introduced to make the execution process more flexible for real-time environment variation. Numerical results indicate that the proposed scheme can significantly reduce the task execution delay in an energy-efficient way.
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Liu, Xiaowei, Shuwen Jiang, and Yi Wu. "A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems." Applied Sciences 12, no. 21 (November 6, 2022): 11260. http://dx.doi.org/10.3390/app122111260.
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With the internet developing rapidly, mobile edge computing (MEC) has been proposed to offer computational capabilities to tackle the high latency caused by innumerable data and applications. Due to limited computing resources, the innovation of computation offloading technology for an MEC system remains challenging, and can lead to transmission delays and energy consumption. This paper focuses on a task-offloading scheme for an MEC-based system where each mobile device is an independent agent and responsible for making a schedule based on delay-sensitive tasks. Nevertheless, the time-varying network dynamics and the heterogeneous features of real-time data tasks make it difficult to find an optimal solution for task offloading. Existing centralized-based or distributed-based algorithms require huge computational resources for complex problems. To address the above problem, we design a novel deep reinforcement learning (DRL)-based approach by using a parameterized indexed value function for value estimation. Additionally, the task-offloading problem is simulated as a Markov decision process (MDP) and our aim is to reduce the total delay of data processing. Experimental results have shown that our algorithm significantly promotes the users’ offloading performance over traditional methods.
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Shevchenko, Rostyslav S., Stanislav I. Shevchenko, Dmytro S. Pryimenko, Oksana S. Tsyganenko, and Vladimir M. Feskov. "COMPARATIVE CHARACTERISTICS OF TREATMENT RESULTS USING THE OFFLOADING MEANS IN PATIENTS WITH PURULENT-NECROTIC COMPLICATIONS OF DIABETIC FOOT SYNDROME." Wiadomości Lekarskie 74, no. 8 (2021): 1891–93. http://dx.doi.org/10.36740/wlek202108119.
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The aim: To conduct a comparative analysis of clinical data, laboratory results, and pain intensity in patients using developed by us device for offloading the foot and plaster splint during inpatient treatment of purulent-necrotic complications of diabetic foot syndrome. Materials and methods: We examined 76 patients with purulent-necrotic complications of diabetic foot syndrome, who used a plaster splint and a device for offloading the foot. We evaluated the clinical indicators of the healing process, laboratory data and pain intensity. Results: Comparing the results of using the device for offloading the foot and the plaster splint showed that the developed by us device had a positive effect on the dynamics of the healing process: the edema disappeared on average 1.5 days earlier; the redness vanished on average 2.5 days earlier. We observed granulation and epithelialization significantly earlier (p <0.05) than in the group where the plaster splint was used. The number of recorded surgical interventions was statistically significant and less (p <0.05) in the group where our device was used. Low pain rates were in patients using a device for offloading the foot due to minimal contact of the wound surface with the floor. Conclusions: According to the results of comparative using the device for offloading the foot and plaster splint, we found out that using the device for offloading the foot allows creating statistically significant better conditions to accelerate wound healing in patients and reduce the duration of inpatient treatment.
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Wang, Suzhen, Zhongbo Hu, Yongchen Deng, and Lisha Hu. "Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems." Applied Sciences 12, no. 12 (June 17, 2022): 6154. http://dx.doi.org/10.3390/app12126154.
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Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing only consider the resource allocation between terminals and edge servers, ignoring the huge computing resources in the cloud center. In order to sufficiently utilize the cloud and edge server resources, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme under Cloud-Edge collaboration. We consider the heterogeneity of mobile devices and inter-channel interference, and we establish the task offloading decision of multiple end-users as a game-theory-based task migration model with the objective of maximizing system utility. In addition, we propose an improved game-theory-based particle swarm optimization algorithm to obtain task offloading strategies. Experimental results show that the proposed scheme outperforms other schemes with respect to latency and energy consumption, and it scales well with increases in the number of mobile devices.
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Manogaran, Gunasekaran, Bharat S. Rawal, Houbing Song, Huihui Wang, Chinghsien Hsu, Vijayalakshmi Saravanan, Seifedine Nimer Kadry, and P. Mohamed Shakeel. "Optimal Energy-Centric Resource Allocation and Offloading Scheme for Green Internet of Things Using Machine Learning." ACM Transactions on Internet Technology 22, no. 2 (May 31, 2022): 1–19. http://dx.doi.org/10.1145/3431500.
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Resource allocation and offloading in green Internet of Things (IoT) relies on the multi-level heterogeneous platforms. The energy expenses of the platform determine the reliability of green IoT based services and applications. This manuscript introduces a decisive energy management scheme for optimal resource allocation and offloading along with energy constraints. This scheme handles both the allocation and energy-cost in a balanced manner through deterministic task offloading. In particular, resource allocation solution for non-delay tolerant green IoT applications is focused by confining the failures of discrete tasks through neural learning. The dropout process augmented with the learning process improves the feasible conditions for resource handling and task offloading among the active IoT service providers. Through extensive simulations the performance of the proposed scheme is analyzed and energy consumption, failure rate, processing, and completion time metrics are used for a comparative study. Further, the optimal utilization and on-demand dissipation of such stored resources help to improve the sustainability of green power and communication technologies in the smart city environment.
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Heidari, Arash, Mohammad Ali Jabraeil Jamali, Nima Jafari Navimipour, and Shahin Akbarpour. "Deep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green IoT-Edge Scenarios." Applied Sciences 12, no. 16 (August 17, 2022): 8232. http://dx.doi.org/10.3390/app12168232.
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The number of Internet of Things (IoT)-related innovations has recently increased exponentially, with numerous IoT objects being invented one after the other. Where and how many resources can be transferred to carry out tasks or applications is known as computation offloading. Transferring resource-intensive computational tasks to a different external device in the network, such as a cloud, fog, or edge platform, is the strategy used in the IoT environment. Besides, offloading is one of the key technological enablers of the IoT, as it helps overcome the resource limitations of individual objects. One of the major shortcomings of previous research is the lack of an integrated offloading framework that can operate in an offline/online environment while preserving security. This paper offers a new deep Q-learning approach to address the IoT-edge offloading enabled blockchain problem using the Markov Decision Process (MDP). There is a substantial gap in the secure online/offline offloading systems in terms of security, and no work has been published in this arena thus far. This system can be used online and offline while maintaining privacy and security. The proposed method employs the Post Decision State (PDS) mechanism in online mode. Additionally, we integrate edge/cloud platforms into IoT blockchain-enabled networks to encourage the computational potential of IoT devices. This system can enable safe and secure cloud/edge/IoT offloading by employing blockchain. In this system, the master controller, offloading decision, block size, and processing nodes may be dynamically chosen and changed to reduce device energy consumption and cost. TensorFlow and Cooja’s simulation results demonstrated that the method could dramatically boost system efficiency relative to existing schemes. The findings showed that the method beats four benchmarks in terms of cost by 6.6%, computational overhead by 7.1%, energy use by 7.9%, task failure rate by 6.2%, and latency by 5.5% on average.
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Lakhan, Abdullah, Ali Hassan Sodhro, Arnab Majumdar, Pattaraporn Khuwuthyakorn, and Orawit Thinnukool. "A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks." Sensors 22, no. 6 (March 19, 2022): 2379. http://dx.doi.org/10.3390/s22062379.
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Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.
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Dai, Qinglong, Jin Qian, Guangjun Qin, Jianwu Li, and Jun Zhao. "A Latency-Aware Offloading Strategy over Fiber-Wireless (FiWi) Infrastructures for Tactile Internet Services." Applied Sciences 12, no. 13 (June 24, 2022): 6417. http://dx.doi.org/10.3390/app12136417.
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With the emergence of the tactile internet, low-latency, even, real-time data transmission is indispensable for human-agent–robot teamwork. Offloading is considered a feasible approach. Determining the offloading solution according to the dynamic network circumstance is attractive. In this paper, we investigate the resource management issue in a three-tier, heterogeneous, fiber-wireless (FiWi) network with offloading. Based on the model of the wireless link, the fiber link, the data rate, and the offloading, a mixed-integer, non-linear problem is formulated to obtain the minimum total latency for tactile internet services. Through constraint relaxation, MINLP is converted to a linear problem (LP). A Lagrange multiplier method with Karush–Kuhn–Tucker (KKT) conditions is used to solve LP. Using the numerical simulation, the superiority of our work is evaluated and compared with the previous work.
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Shalini Lakshmi, A. J., and M. Vijayalakshmi. "A predictive context aware collaborative offloading framework for compute-intensive applications." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 77–88. http://dx.doi.org/10.3233/jifs-182906.
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The resourceful mobile devices with augmented capabilities around human pave the way for utilizing it as delegators for resource-constrained devices to run compute-intensive applications. Such collaborative resource sharing policy among mobile devices throws challenges like identifying competent alternatives for offloading and diminishing time consumption of pre-offload process to accomplish remarkable offloading. This paper presents a Mobile Cloud Computing framework with Predictive Context-Aware Collaborative Offloading Process (PCA-COP) that fixes these challenges through conductive alternative discovery. This context-aware discovery adapts a multi-criteria decision making model of Analytic Hierarchy Process (AHP) accompanied with Fuzzy categorization to rank the alternatives and classify them into Highly, Fairly, Less offload-suitable devices. Moreover, to make alternative selection optimal, a Dataset Curtailment enabled Artificial Neural Network (DCANN) prediction is incorporated on AHP-Fuzzy model, which truncates training dataset using Conditioned Stratified Sampling (CSS). The prototype framework is evaluated with mobile applications in the classroom under dynamic context environments.
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Peng, Biying, Taoshen Li, and Yan Chen. "DRL-Based Dependent Task Offloading Strategies with Multi-Server Collaboration in Multi-Access Edge Computing." Applied Sciences 13, no. 1 (December 23, 2022): 191. http://dx.doi.org/10.3390/app13010191.
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Many applications in Multi-access Edge Computing (MEC) consist of interdependent tasks where the output of some tasks is the input of others. Most of the existing research on computational offloading does not consider the dependency of the task and uses convex relaxation or heuristic algorithms to solve the offloading problem, which lacks adaptability and is not suitable for computational offloading in the dynamic environment of fast fading channels. Therefore, in this paper, the optimization problem is modeled as a Markov Decision Process (MDP) in multi-user and multi-server MEC environments, and the dependent tasks are represented by Directed Acyclic Graph (DAG). Combined with the Soft Actor–Critic (SAC) algorithm in Deep Reinforcement Learning (DRL) theory, an intelligent task offloading scheme is proposed. Under the condition of resource constraint, each task can be offloaded to the corresponding MEC server through centralized control, which greatly reduces the service delay and terminal energy consumption. The experimental results show that the algorithm converges quickly and stably, and its optimization effect is better than existing methods, which verifies the effectiveness of the algorithm.
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Hu, Chunyang, Jingchen Li, Haobin Shi, Bin Ning, and Qiong Gu. "Decentralized Offloading Strategies Based on Reinforcement Learning for Multi-Access Edge Computing." Information 12, no. 9 (August 25, 2021): 343. http://dx.doi.org/10.3390/info12090343.
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Using reinforcement learning technologies to learn offloading strategies for multi-access edge computing systems has been developed by researchers. However, large-scale systems are unsuitable for reinforcement learning, due to their huge state spaces and offloading behaviors. For this reason, this work introduces the centralized training and decentralized execution mechanism, designing a decentralized reinforcement learning model for multi-access edge computing systems. Considering a cloud server and several edge servers, we separate the training and execution in the reinforcement learning model. The execution happens in edge devices of the system, and edge servers need no communication. Conversely, the training process occurs at the cloud device, which causes a lower transmission latency. The developed method uses a deep deterministic policy gradient algorithm to optimize offloading strategies. The simulated experiment shows that our method can learn the offloading strategy for each edge device efficiently.
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Ajith, Anusree, and T. G. Venkatesh. "Mobile Data Offloading for Streaming-Class Traffic with QoS Guarantee." International Journal of Interdisciplinary Telecommunications and Networking 7, no. 4 (October 2015): 26–42. http://dx.doi.org/10.4018/ijitn.2015100103.
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Faced with the tremendous increase in the amount of data traffic and associated congestion, mobile network operators are moving towards Heterogeneous networks (HetNets), in the process of expanding network capacity. Offloading data traffic onto Wi-Fi in order to avoid congestion in the backbone is an important step in the evolution of HetNets. On-the-spot and delayed offloading have been widely studied in the literature. This paper proposes an offloading algorithm which has low computational complexity. The proposed algorithm offloads data based on a balking function which is dependent on present network condition. Using extensive simulations, the authors demonstrate that the proposed algorithm achieves reduction in mean transmission delay without sacrificing much on the offloading efficiency. This technique is more efficient and applicable to real-time traffic, like live streaming video and audio, which has short and stringent delay requirements or deadlines.
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Yang, Shicheng, Gongwei Lee, and Liang Huang. "Deep Learning-Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks." Sensors 22, no. 11 (May 27, 2022): 4088. http://dx.doi.org/10.3390/s22114088.
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This paper investigates the computation offloading problem in mobile edge computing (MEC) networks with dynamic weighted tasks. We aim to minimize the system utility of the MEC network by jointly optimizing the offloading decision and bandwidth allocation problems. The optimization of joint offloading decisions and bandwidth allocation is formulated as a mixed-integer programming (MIP) problem. In general, the problem can be efficiently generated by deep learning-based algorithms for offloading decisions and then solved by using traditional optimization methods. However, these methods are weakly adaptive to new environments and require a large number of training samples to retrain the deep learning model once the environment changes. To overcome this weakness, in this paper, we propose a deep supervised learning-based computational offloading (DSLO) algorithm for dynamic computational tasks in MEC networks. We further introduce batch normalization to speed up the model convergence process and improve the robustness of the model. Numerical results show that DSLO only requires a few training samples and can quickly adapt to new MEC scenarios. Specifically, it can achieve 99% normalized system utility by using only four training samples per MEC scenario. Therefore, DSLO enables the fast deployment of computation offloading algorithms in future MEC networks.
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Valentino, Rico, Woo-Sung Jung, and Young-Bae Ko. "A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks." Sensors 18, no. 11 (November 2, 2018): 3751. http://dx.doi.org/10.3390/s18113751.
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Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones’ tasks have become larger and more complex. As a result, swarms or clusters of drones are preferred, because they offer more coverage, flexibility, and reliability. However, drone systems have limited computing power and energy resources, which means that sometimes it is difficult for drones to finish their tasks on schedule. A solution to this is required so that drone clusters can complete their work faster. One possible solution is an offloading scheme between drone clusters. In this study, we propose an opportunistic computational offloading system, which allows for a drone cluster with a high intensity task to borrow computing resources opportunistically from other nearby drone clusters. We design an artificial neural network-based response time prediction module for deciding whether it is faster to finish tasks by offloading them to other drone clusters. The offloading scheme is conducted only if the predicted offloading response time is smaller than the local computing time. Through simulation results, we show that our proposed scheme can decrease the response time of drone clusters through an opportunistic offloading process.
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Huang, Binbin, Yangyang Li, Zhongjin Li, Linxuan Pan, Shangguang Wang, Yunqiu Xu, and Haiyang Hu. "Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing." Wireless Communications and Mobile Computing 2019 (December 23, 2019): 1–20. http://dx.doi.org/10.1155/2019/3816237.
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With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.
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Zaman, Sardar Khaliq uz, Ali Imran Jehangiri, Tahir Maqsood, Arif Iqbal Umar, Muhammad Amir Khan, Noor Zaman Jhanjhi, Mohammad Shorfuzzaman, and Mehedi Masud. "COME-UP: Computation Offloading in Mobile Edge Computing with LSTM Based User Direction Prediction." Applied Sciences 12, no. 7 (March 24, 2022): 3312. http://dx.doi.org/10.3390/app12073312.
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In mobile edge computing (MEC), mobile devices limited to computation and memory resources offload compute-intensive tasks to nearby edge servers. User movement causes frequent handovers in 5G urban networks. The resultant delays in task execution due to unknown user position and base station lead to increased energy consumption and resource wastage. The current MEC offloading solutions separate computation offloading from user mobility. For task offloading, techniques that predict the user’s future location do not consider user direction. We propose a framework termed COME-UP Computation Offloading in mobile edge computing with Long-short term memory (LSTM) based user direction prediction. The nature of the mobility data is nonlinear and leads to a time series prediction problem. The LSTM considers the previous mobility features, such as location, velocity, and direction, as input to a feed-forward mechanism to train the learning model and predict the next location. The proposed architecture also uses a fitness function to calculate priority weights for selecting an optimum edge server for task offloading based on latency, energy, and server load. The simulation results show that the latency and energy consumption of COME-UP are lower than the baseline techniques, while the edge server utilization is enhanced.
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Carpenter, Chris. "System Equilibrium Results in Zero Flaring During Offloading." Journal of Petroleum Technology 74, no. 04 (April 1, 2022): 48–51. http://dx.doi.org/10.2118/0422-0048-jpt.
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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 30655, “Effective Boiled-Off-Gas/Fuel-Gas Balance Between Floating Liquefaction Facilities and Carrier Resulting in Zero Flaring During Offloading,” by Kinfai Wong and Rose Sapinah Hashim, Petronas, and Yosuke Yamamoto, JGC, et al. The paper has not been peer reviewed. Copyright 2020 Offshore Technology Conference. Reproduced by permission. Achievement of zero flaring during liquefied natural gas (LNG) offloading operations is governed by many factors. The proper management of the balance of fuel gas and boiled-off gas (BOG) between the floating LNG (FLNG) facility and the LNG carrier (LNGC) is critical during offloading to ensure that the system reaches equilibrium, leading to zero flaring. The complete paper details a process study to identify potential causes of flaring during LNG offtake and corrective measures to accomplish zero flaring without any capital plant modification. The study is applicable only for offloading operations to LNGCs with spherical Type B LNG tanks. Introduction PFLNG Satu (referred to hereafter as PFLNG1) performs LNG offloading to an LNGC on a monthly basis. This is known as an offloading mode of operations, in which the LNGC is moored at the PFLNG1 facilities and LNG is transferred from storage tanks to the LNGC. The LNG in the storage tank is transferred to the LNGC by three liquid-offloading arms, while the vapor return from the LNGC is recovered by a single vapor-return arm. Multiple gas-flaring incidents were observed with PFLNG1’s fuel-gas system because of an imbalance in the fuel-gas flow in the system during offloading operations.
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Ali, Abid, Muhammad Munawar Iqbal, Harun Jamil, Faiza Qayyum, Sohail Jabbar, Omar Cheikhrouhou, Mohammed Baz, and Faisal Jamil. "An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing." Sensors 21, no. 13 (July 1, 2021): 4527. http://dx.doi.org/10.3390/s21134527.
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Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.
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Han, Yuelin, and Qi Zhu. "Joint Computation Offloading and Resource Allocation for NOMA-Enabled Multitask D2D System." Wireless Communications and Mobile Computing 2022 (July 28, 2022): 1–14. http://dx.doi.org/10.1155/2022/5349571.
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Due to the limited computing capacity of mobile device and high network-accessing delay, user in the area where mobile terminals are densely distributed (e.g., schools, malls, and hospitals) will experience high latency when processing multiple computation-intensive tasks. In this paper, a computation offloading scheme based on Device-to-Device (D2D) communication is proposed to deal with the problem that users have multiple tasks to process. Exploiting nonorthogonal multiple access (NOMA), user can offload tasks to multiple nearby devices that have available idle computing resource. We aim to minimize the user’s total cost including time latency, energy consumption, and offloading charge, which is formulated as a mixed integer nonlinear programming (MINLP) problem. We use decomposition approach to solve our problem and propose a two-layer optimization scheme named Multitask Joint Computation Offloading and Resource Allocation (MT-JCORA). In the inner layer, the NOMA-transmission time optimization problem in given task offloading decision is proved as a strictly convex problem. In the outer layer, we design a heuristic algorithm based on GA algorithm to obtain the optimal task offloading decision. Simulation results demonstrate that MT-JCORA can effectively reduce the total cost of user compared with related schemes.
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Wang, Shujuan, Hao Peng, and Dongchao Guo. "Resource- and Time-Efficient Computation Offloading in Vehicular Edge Computing: A Max-Min Fairness Oriented Approach." Mathematics 10, no. 20 (October 11, 2022): 3735. http://dx.doi.org/10.3390/math10203735.
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Nowadays, computation offloading has become a research focus since it has the potential to solve the challenges faced when dealing with computation-intensive applications in the Internet of Vehicles (IoVs), especially in the 5G or future network environment. However, major issues still exist and the performance of main metrics can be improved to better adapt to the practical scenarios. This paper focuses on achieving resource- and time-efficient computation offloading in IoVs by boosting the cooperation efficiency of vehicles. Firstly, a fuzzy logic-based pricing strategy is designed to evaluate the cooperation tendency and capability of each vehicle from multiple aspects. Vehicles are encouraged to participate in the offloading process even if they are in a disadvantageous position compared to other vehicles. Secondly, a Max-Min fairness-oriented approach is proposed to find the most suitable offloading decision, and vehicles with poor cooperation capabilities are guaranteed to be treated equally in the offloading. Finally, two heuristic algorithms are presented to solve the problem with applicable complexity and to suit the practical IoV environment. Extensive simulation results prove that the proposed approach achieves remarkable performance improvements in terms of delay, service cost and the resource utilization ratios of vehicles.
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Li, Shuyang, Xiaohui Hu, and Yongwen Du. "Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Unmanned-Aerial-Vehicle Assisted Edge Computing." Sensors 21, no. 19 (September 29, 2021): 6499. http://dx.doi.org/10.3390/s21196499.
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Computation offloading technology extends cloud computing to the edge of the access network close to users, bringing many benefits to terminal devices with limited battery and computational resources. Nevertheless, the existing computation offloading approaches are challenging to apply to specific scenarios, such as the dense distribution of end-users and the sparse distribution of network infrastructure. The technological revolution in the unmanned aerial vehicle (UAV) and chip industry has granted UAVs more computing resources and promoted the emergence of UAV-assisted mobile edge computing (MEC) technology, which could be applied to those scenarios. However, in the MEC system with multiple users and multiple servers, making reasonable offloading decisions and allocating system resources is still a severe challenge. This paper studies the offloading decision and resource allocation problem in the UAV-assisted MEC environment with multiple users and servers. To ensure the quality of service for end-users, we set the weighted total cost of delay, energy consumption, and the size of discarded tasks as our optimization objective. We further formulate the joint optimization problem as a Markov decision process and apply the soft actor–critic (SAC) deep reinforcement learning algorithm to optimize the offloading policy. Numerical simulation results show that the offloading policy optimized by our proposed SAC-based dynamic computing offloading (SACDCO) algorithm effectively reduces the delay, energy consumption, and size of discarded tasks for the UAV-assisted MEC system. Compared with the fixed local-UAV scheme in the specific simulation setting, our proposed approach reduces system delay and energy consumption by approximately 50% and 200%, respectively.
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Kaliappan, Vishnu Kumar, Aravind Babu Lalpet Ranganathan, Selvaraju Periasamy, Padmapriya Thirumalai, Tuan Anh Nguyen, Sangwoo Jeon, Dugki Min, and Enumi Choi. "Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization." Energies 15, no. 21 (November 5, 2022): 8273. http://dx.doi.org/10.3390/en15218273.
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Edge devices and their associated computing techniques require energy efficiency to improve sustainability over time. The operating edge devices are timed to swap between different states to achieve stabilized energy efficiency. This article introduces a Cognitive Energy Management Scheme (CEMS) by considering the offloading and computational states for energy efficacy. The proposed scheme employs state learning for swapping the computing intervals for scheduling or offloading depending on the load. The edge devices are distributed at the time of scheduling and organized for first come, first serve for offloading features. In state learning, the reward is allocated for successful scheduling over offloading to prevent device exhaustion. The computation is therefore swapped for energy-reserved scheduling or offloading based on the previous computed reward. This cognitive management induces device allocation based on energy availability and computing time to prevent energy convergence. Cognitive management is limited in recent works due to non-linear swapping and missing features. The proposed CEMS addresses this issue through precise scheduling and earlier device exhaustion identification. The convergence issue is addressed using rewards assigned to post the state transitions. In the transition process, multiple device energy levels are considered. This consideration prevents early detection of exhaustive devices, unlike conventional wireless networks. The proposed scheme’s performance is compared using the metrics computing rate and time, energy efficacy, offloading ratio, and scheduling failures. The experimental results show that this scheme improves the computing rate and energy efficacy by 7.2% and 9.32%, respectively, for the varying edge devices. It reduces the offloading ratio, scheduling failures, and computing time by 14.97%, 7.27%, and 14.48%, respectively.
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Li, Yanlong, Lei Liang, Jielin Fu, and Junyi Wang. "Multiagent Reinforcement Learning for Task Offloading of Space/Aerial-Assisted Edge Computing." Security and Communication Networks 2022 (May 2, 2022): 1–10. http://dx.doi.org/10.1155/2022/4193365.
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The task offloading in space-aerial-ground integrated network (SAGIN) has been envisioned as a challenging issue. In this paper, we investigate a space/aerial-assisted edge computing network architecture considering whether to take advantage of edge server mounted on the unmanned aerial vehicle and satellite for task offloading or not. By optimizing the energy consumption and completion delay, we formulate a NP-hard and non-convex optimization problem to minimize the computation cost, limited by the computation capacity and energy availability constraints. By formulating the problem as a Markov decision process (MDP), we propose a multiagent deep reinforcement learning (MADRL)-based scheme to obtain the optimal task offloading policies considering dynamic computation request and stochastic time-varying channel conditions, while ensuring the quality-of-service requirements. Finally, simulation results demonstrate the task offloading scheme learned from our proposed algorithm that can substantially reduce the average cost as compared to the other three single agent deep reinforcement learning schemes.
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Sun, Lin, and Qi Zhu. "WiFi Offloading Algorithm Based on Q-Learning and MADM in Heterogeneous Networks." Mobile Information Systems 2019 (December 27, 2019): 1–12. http://dx.doi.org/10.1155/2019/7575037.
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This paper proposes a WiFi offloading algorithm based on Q-learning and MADM (multiattribute decision making) in heterogeneous networks for a mobile user scenario where cellular networks and WiFi networks coexist. The Markov model is used to describe the changes of the network environment. Four attributes including user throughput, terminal power consumption, user cost, and communication delay are considered to define the user satisfaction function reflecting QoS (Quality of Service), and Q-learning is used to optimize it. Through AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) in MADM, the intrinsic connection between each attribute and the reward function is obtained. The user uses Q-learning to make offloading decisions based on current network conditions and their own offloading history, ultimately maximizing their satisfaction. The simulation results show that the user satisfaction of the proposed algorithm is better than the traditional WiFi offloading algorithm.
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Damigos, Gerasimos, Tore Lindgren, Sara Sandberg, and George Nikolakopoulos. "Performance of Sensor Data Process Offloading on 5G-Enabled UAVs." Sensors 23, no. 2 (January 12, 2023): 864. http://dx.doi.org/10.3390/s23020864.
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Recently, unmanned aerial vehicle (UAV)-oriented applications have been growing worldwide. Thus, there is a strong interest in using UAVs for applications requiring wide-area connectivity coverage. Such applications might be power line inspection, road inspection, offshore site monitoring, wind turbine inspections, and others. The utilization of cellular networks, such as the fifth-generation (5G) technology, is often considered to meet the requirement of wide-area connectivity. This study quantifies the performance of 5G-enabled UAVs when sensor data throughput requirements are within the 5G network’s capability and when throughput requirements significantly exceed the capability of the 5G network, respectively. Our experimental results show that in the first case, the 5G network maintains bounded latency, and the application behaves as expected. In the latter case, the overloading of the 5G network results in increased latency, dropped packets, and overall degradation of the application performance. Our findings show that offloading processes requiring moderate sensor data rates work well, while transmitting all the raw data generated by the UAV’s sensors is not possible. This study highlights and experimentally demonstrates the impact of critical parameters that affect real-life 5G-enabled UAVs that utilize the edge-offloading power of a 5G cellular network.
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Rocha, Paulo, Alisson Souza, Gilvan Maia, César Mattos, Francisco Airton Silva, Paulo Rego, Tuan Anh Nguyen, and Jae-Woo Lee. "Evaluating Link Lifetime Prediction to Support Computational Offloading Decision in VANETs." Sensors 22, no. 16 (August 12, 2022): 6038. http://dx.doi.org/10.3390/s22166038.
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In urban mobility, Vehicular Ad Hoc Networks (VANETs) provide a variety of intelligent applications. By enhancing automobile traffic management, these technologies enable advancements in safety and help decrease the frequency of accidents. The transportation system can now follow the development and growth of cities without sacrificing the quality and organisation of its services thanks to safety apps that include collision alerts, real-time traffic information, and safe driving applications, among others. Applications can occasionally demand a lot of computing power, making their processing impractical for cars with limited onboard processing capacity. Offloading of computation is encouraged by such a restriction. However, because vehicle mobility operations are dynamic, communication times (also known as link lifetimes) between nodes are frequently short. VANET applications and processes are impacted by such communication delays (e.g., the offloading decision when using the Computational Offloading technique). Making an accurate prediction of the link lifespan between vehicles is therefore challenging. The effectiveness of the communication time estimation is currently constrained by the link lifespan prediction methods used in the computational offloading process. This work investigates five machine learning (ML) algorithms to predict the link lifetime between nodes in VANETs in different scenarios. We propose the procedures required to carry out the link lifetime prediction method using existing ML techniques. The tactic creates datasets with the features the models need to learn and be trained. The SVR and XGBoost algorithms that were selected as part of the assessment process were trained. To make the prediction using the trained models, we modified the lifespan prediction function from an offloading approach. To determine the viability of applying link lifespan predictions from the models trained in the road and urban scenarios, we conducted a performance study. The findings indicate that compared to the conventional prediction strategy described in the literature, the suggested link lifetime prediction via regression approaches decreases prediction error rates. An offloading method from the literature is extended by the selected SVR. The task loss and recovery rates might be significantly reduced using the SVR. XGBoost outperformed its ML competitors in task recovery or drop rate by 70% to 80% in an assessed hypothesis compared to an offloading choice technique in the literature. With greater offloading rates from an application on the VANET, this effort is intended to give better efficiency in estimating this data using machine learning in various vehicular settings.
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Zhu, Dali, Ting Li, Haitao Liu, Jiyan Sun, Liru Geng, and Yinlong Liu. "Privacy-Aware Online Task Offloading for Mobile-Edge Computing." Wireless Communications and Mobile Computing 2021 (June 10, 2021): 1–16. http://dx.doi.org/10.1155/2021/6622947.
Full textAbstract:
Mobile edge computing (MEC) has been envisaged as one of the most promising technologies in the fifth generation (5G) mobile networks. It allows mobile devices to offload their computation-demanding and latency-critical tasks to the resource-rich MEC servers. Accordingly, MEC can significantly improve the latency performance and reduce energy consumption for mobile devices. Nonetheless, privacy leakage may occur during the task offloading process. Most existing works ignored these issues or just investigated the system-level solution for MEC. Privacy-aware and user-level task offloading optimization problems receive much less attention. In order to tackle these challenges, a privacy-preserving and device-managed task offloading scheme is proposed in this paper for MEC. This scheme can achieve near-optimal latency and energy performance while protecting the location privacy and usage pattern privacy of users. Firstly, we formulate the joint optimization problem of task offloading and privacy preservation as a semiparametric contextual multi-armed bandit (MAB) problem, which has a relaxed reward model. Then, we propose a privacy-aware online task offloading (PAOTO) algorithm based on the transformed Thompson sampling (TS) architecture, through which we can (1) receive the best possible delay and energy consumption performance, (2) achieve the goal of preserving privacy, and (3) obtain an online device-managed task offloading policy without requiring any system-level information. Simulation results demonstrate that the proposed scheme outperforms the existing methods in terms of minimizing the system cost and preserving the privacy of users.
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Swami, Pragya, Vimal Bhatia, Satyanarayana Vuppala, and Tharmalingam Ratnarajah. "On User Offloading in NOMA-HetNet Using Repulsive Point Process." IEEE Systems Journal 13, no. 2 (June 2019): 1409–20. http://dx.doi.org/10.1109/jsyst.2018.2874310.
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Wang, Hong. "Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing." Journal of Robotics 2021 (December 29, 2021): 1–9. http://dx.doi.org/10.1155/2021/3965689.
Full textAbstract:
Aiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introducing the unmanned aerial vehicle (UAV) cluster. First, for the scenario of the UAV cluster serving multiple ground terminals, a collaborative task offloading model is formulated to offload the tasks to UAVs or the base station selectively. Then, an objective function and related constraints are put forward to minimize the time delay and energy consumption by analysis of those in the communication and computing process in the system while considering many factors. Then, the improved genetic algorithm is introduced to solve the optimization problem, obtaining the optimal collaborative task offloading strategy. To verify the performance of the proposed method, simulations are conducted on MATLAB. Simulation results showed that the joint utilization of UAV and MEC improves the offloading efficiency of the proposed strategy. When the number of UAVs is 12, the total utility is up to 1.83 and the task completion time does not exceed 110 ms. In this case, the task can be reasonably offloaded to UAVs or accomplished locally.
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Elhosuieny, Abdulrahman, Mofreh Salem, Amr Thabet, and Abdelhameed Ibrahim. "ADOMC-NPR Automatic Decision-Making Offloading Framework for Mobile Computation Using Nonlinear Polynomial Regression Model." International Journal of Web Services Research 16, no. 4 (October 2019): 53–73. http://dx.doi.org/10.4018/ijwsr.2019100104.
Full textAbstract:
Nowadays, mobile computation applications attract major interest of researchers. Limited processing power and short battery lifetime is an obstacle in executing computationally-intensive applications. This article presents a mobile computation automatic decision-making offloading framework. The proposed framework consists of two phases: adaptive learning, and modeling and runtime computation offloading. In the adaptive phase, curve-fitting (CF) technique based on non-linear polynomial regression (NPR) methodology is used to build an approximate time-predicting model that can estimate the execution time for spending the processing of the detected-intensive applications. The runtime computation phase uses the time predicting model for computing the predicted execution time to decide whether to run the application remotely and perform the offloading process or to run the application locally. Eventually, the RESTful web service is applied to carry out the offloading task in the case of a positive offloading decision. The proposed framework experimentally outperforms a competitive state-of-the-art technique by 73% concerning the time factor. The proposed time-predicting model records minimal deviation of the originally obtained values as it is applied 0.4997, 8.9636, 0.0020, and 0.6797 on the mean squared error metric for matrix-determinant, image-sharpening, matrix-multiplication, and n-queens problems, respectively.