Journal articles on the topic 'Edge Computation Offloading'
To see the other types of publications on this topic, follow the link: Edge Computation Offloading.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Edge Computation Offloading.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Patel, Minal Parimalbhai, and Sanjay Chaudhary. "Edge Computing." International Journal of Fog Computing 3, no. 1 (January 2020): 64–74. http://dx.doi.org/10.4018/ijfc.2020010104.
Abstract:
In this article, the researchers have provided a discussion on computation offloading and the importance of docker-based containers, known as light weight virtualization, to improve the performance of edge computing systems. At the end, they have also proposed techniques and a case study for computation offloading and light weight virtualization.
2
Man, Junfeng, Longqian Zhao, Bowen Xu, Cheng Peng, Junjie Jiang, and Yi Liu. "Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode." Journal of Database Management 34, no. 1 (February 24, 2023): 1–29. http://dx.doi.org/10.4018/jdm.318451.
Abstract:
Large-scale manufacturing enterprises have complex business processes in their production workshops, and the edge-edge collaborative business model cannot adapt to the traditional computation offloading methods, which leads to the problem of load imbalance. For this problem, a computation offloading algorithm based on edge-edge collaboration mode for large-scale factory access is proposed, called the edge and edge collaborative computation offloading (EECCO) algorithm. First, the method partitions the directed acyclic graphs (DAGs) on edge server and terminal industrial equipment, then updates the tasks using a synchronization policy based on set theory to improve the accuracy effectively, and finally achieves load balancing through processor allocation. The experimental results show that the method shortens the processing time by improving computational resource utilization and employs a heterogeneous distributed system to achieve high computing performance when processing large-scale task sets.
3
Xiao, Yong, Ling Wei, Junhao Feng, and Wang En. "Two-tier end-edge collaborative computation offloading for edge computing." Journal of Computational Methods in Sciences and Engineering 22, no. 2 (March 28, 2022): 677–88. http://dx.doi.org/10.3233/jcm-215923.
Abstract:
Edge computing has emerged for meeting the ever-increasing computation demands from delay-sensitive Internet of Things (IoT) applications. However, the computing capability of an edge device, including a computing-enabled end user and an edge server, is insufficient to support massive amounts of tasks generated from IoT applications. In this paper, we aim to propose a two-tier end-edge collaborative computation offloading policy to support as much as possible computation-intensive tasks while making the edge computing system strongly stable. We formulate the two-tier end-edge collaborative offloading problem with the objective of minimizing the task processing and offloading cost constrained to the stability of queue lengths of end users and edge servers. We perform analysis of the Lyapunov drift-plus-penalty properties of the problem. Then, a cost-aware computation offloading (CACO) algorithm is proposed to find out optimal two-tier offloading decisions so as to minimize the cost while making the edge computing system stable. Our simulation results show that the proposed CACO outperforms the benchmarked algorithms, especially under various number of end users and edge servers.
4
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.
Abstract:
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.
5
Lin, Li, Xiaofei Liao, Hai Jin, and Peng Li. "Computation Offloading Toward Edge Computing." Proceedings of the IEEE 107, no. 8 (August 2019): 1584–607. http://dx.doi.org/10.1109/jproc.2019.2922285.
6
Maftah, Sara, Mohamed El Ghmary, Hamid El Bouabidi, Mohamed Amnai, and Ali Ouacha. "Intelligent task processing using mobile edge computing: processing time optimization." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (March 1, 2024): 143. http://dx.doi.org/10.11591/ijai.v13.i1.pp143-152.
Abstract:
<p>The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delaysensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capabilities, battery resources, and storage availability, it enhances the durability and performance of mobile devices by offloading local intensive computation tasks to edge servers. However, the optimal solution is not always guaranteed by offloading computation, therefore, the offloading decision is a crucial step depending on many parameters that should be taken in consideration. In this paper, we use a simulator to compare a two tier edge orchestrator architecture with the results obtained by implementing a system model that aims to minimize a task’s processing time constrained by time delay and the limited device’s computational resource and usage based on a modified version.</p>
7
Li, Feixiang, Chao Fang, Mingzhe Liu, Ning Li, and Tian Sun. "Intelligent Computation Offloading Mechanism with Content Cache in Mobile Edge Computing." Electronics 12, no. 5 (March 6, 2023): 1254. http://dx.doi.org/10.3390/electronics12051254.
Abstract:
Edge computing is a promising technology to enable user equipment to share computing resources for task offloading. Due to the characteristics of the computing resource, how to design an efficient computation incentive mechanism with the appropriate task offloading and resource allocation strategies is an essential issue. In this manuscript, we proposed an intelligent computation offloading mechanism with content cache in mobile edge computing. First, we provide the network framework for computation offloading with content cache in mobile edge computing. Then, by deriving necessary and sufficient conditions, an optimal contract is designed to obtain the joint task offloading, resource allocation, and a computation strategy with an intelligent mechanism. Simulation results demonstrate the efficiency of our proposed approach.
8
Sheng, Jinfang, Jie Hu, Xiaoyu Teng, Bin Wang, and Xiaoxia Pan. "Computation Offloading Strategy in Mobile Edge Computing." Information 10, no. 6 (June 2, 2019): 191. http://dx.doi.org/10.3390/info10060191.
Abstract:
Mobile phone applications have been rapidly growing and emerging with the Internet of Things (IoT) applications in augmented reality, virtual reality, and ultra-clear video due to the development of mobile Internet services in the last three decades. These applications demand intensive computing to support data analysis, real-time video processing, and decision-making for optimizing the user experience. Mobile smart devices play a significant role in our daily life, and such an upward trend is continuous. Nevertheless, these devices suffer from limited resources such as CPU, memory, and energy. Computation offloading is a promising technique that can promote the lifetime and performance of smart devices by offloading local computation tasks to edge servers. In light of this situation, the strategy of computation offloading has been adopted to solve this problem. In this paper, we propose a computation offloading strategy under a scenario of multi-user and multi-mobile edge servers that considers the performance of intelligent devices and server resources. The strategy contains three main stages. In the offloading decision-making stage, the basis of offloading decision-making is put forward by considering the factors of computing task size, computing requirement, computing capacity of server, and network bandwidth. In the server selection stage, the candidate servers are evaluated comprehensively by multi-objective decision-making, and the appropriate servers are selected for the computation offloading. In the task scheduling stage, a task scheduling model based on the improved auction algorithm has been proposed by considering the time requirement of the computing tasks and the computing performance of the mobile edge computing server. Extensive simulations have demonstrated that the proposed computation offloading strategy could effectively reduce service delay and the energy consumption of intelligent devices, and improve user experience.
9
Huang, Yan-Yun, and Pi-Chung Wang. "Computation Offloading and User-Clustering Game in Multi-Channel Cellular Networks for Mobile Edge Computing." Sensors 23, no. 3 (January 19, 2023): 1155. http://dx.doi.org/10.3390/s23031155.
Abstract:
Mobile devices may use mobile edge computing to improve energy efficiency and responsiveness by offloading computation tasks to edge servers. However, the transmissions of mobile devices may result in interference that decreases the upload rate and prolongs transmission delay. Clustering has been shown as an effective approach to improve the transmission efficiency for dense devices, but there is no distributed algorithm for the optimization of clustering and computation offloading. In this work, we study the optimization problem of computation offloading to minimize the energy consumption of mobile devices in mobile edge computing by adaptively clustering devices to improve the transmission efficiency. To address the optimization problem in a distributed manner, the decision problem of clustering and computation offloading for mobile devices is formulated as a potential game. We introduce the construction of the potential game and show the existence of Nash equilibrium in the game with a finite enhancement ability. Then, we propose a distributed algorithm of clustering and computation offloading based on game theory. We conducted a simulation to evaluate the proposed algorithm. The numerical results from our simulation show that our algorithm can improve offloading efficiency for mobile devices in mobile edge computing by improving transmission efficiency. By offloading more tasks to edge servers, both the energy efficiency of mobile devices and the responsiveness of computation-intensive applications can be improved simultaneously.
10
Abbas, Aamir, Ali Raza, Farhan Aadil, and Muazzam Maqsood. "Meta-heuristic-based offloading task optimization in mobile edge computing." International Journal of Distributed Sensor Networks 17, no. 6 (June 2021): 155014772110230. http://dx.doi.org/10.1177/15501477211023021.
Abstract:
With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.
11
Khan, Prince Waqas, Khizar Abbas, Hadil Shaiba, Ammar Muthanna, Abdelrahman Abuarqoub, and Mashael Khayyat. "Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach." Electronics 9, no. 6 (June 17, 2020): 1010. http://dx.doi.org/10.3390/electronics9061010.
Abstract:
Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from 0.979 % and gradually decreases to 0.87 % . Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.
12
Khera, Nikhil, Krishna Sarthak Tiwari, Vaibhav Tripathi, G. Sai Vinit, and Nagaraja J. "Literature Survey of Computation Offloading for Mobile Applications in Mobile Edge Computation." Journal of Computer Science Engineering and Software Testing 8, no. 1 (February 28, 2022): 65–74. http://dx.doi.org/10.46610/jocses.2022.v08i01.005.
Abstract:
Despite their progress and improvements, mobile devices are still regarded as restricted computer devices. Users are growing more discerning, expecting to be able to run computationally intensive applications on their smartphones or tablets. As a result, Mobile Cloud Computing (MCC) integrates mobile computing and Cloud Computing in order to use offloading techniques to increase the capabilities of mobile devices. Although offloading programmes to the cloud might improve mobile device performance, it can also increase processing delay. Inevitably, the quality of user service (QoS) suffers as a result, particularly for specific applications (especially workflow applications). To address the problem of network latency, a new paradigm known as mobile edge computing (MEC) has been developed, which may be thought of as a subset of MCC. We also see deployment of Cloudlets, which are a form of edge server, to minimize latency and energy usage by offloading applications to the cloud, resulting in an efficient and cost-effective architecture as well as various decision-making techniques for offloading. And so, a literature survey regarding this will be presented which focuses on various methods of offloading and its decision-making techniques.
13
Lan, Yanwen, Xiaoxiang Wang, Chong Wang, Dongyu Wang, and Qi Li. "Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems." Electronics 8, no. 12 (November 30, 2019): 1430. http://dx.doi.org/10.3390/electronics8121430.
Abstract:
The hierarchical edge-cloud enabled paradigm has recently been proposed to provide abundant resources for 5G wireless networks. However, the computation and communication capabilities are heterogeneous which makes the potential advantages difficult to be fully explored. Besides, previous works on mobile edge computing (MEC) focused on server caching and offloading, ignoring the computational and caching gains brought by the proximity of user equipments (UEs). In this paper, we investigate the computation offloading in a three-tier cache-assisted hierarchical edge-cloud system. In this system, UEs cache tasks and can offload their workloads to edge servers or adjoining UEs by device-to-device (D2D) for collaborative processing. A cost minimization problem is proposed by the tradeoff between service delay and energy consumption. In this problem, the offloading decision, the computational resources and the offloading ratio are jointly optimized in each offloading mode. Then, we formulate this problem as a mixed-integer nonlinear optimization problem (MINLP) which is non-convex. To solve it, we propose a joint computation offloading and resource allocation optimization (JORA) scheme. Primarily, in this scheme, we decompose the original problem into three independent subproblems and analyze their convexity. After that, we transform them into solvable forms (e.g., convex optimization problem or linear optimization problem). Then, an iteration-based algorithm with the Lagrange multiplier method and a distributed joint optimization algorithm with the adoption of game theory are proposed to solve these problems. Finally, the simulation results show the performance of our proposed scheme compared with other existing benchmark schemes.
14
Gu, Xiaohui, Li Jin, Nan Zhao, and Guoan Zhang. "Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing." Mobile Information Systems 2019 (December 16, 2019): 1–9. http://dx.doi.org/10.1155/2019/3613250.
Abstract:
Mobile edge computing (MEC) is considered a promising technique that prolongs battery life and enhances the computation capacity of mobile devices (MDs) by offloading computation-intensive tasks to the resource-rich cloud located at the edges of mobile networks. In this study, the problem of energy-efficient computation offloading with guaranteed performance in multiuser MEC systems was investigated. Given that MDs typically seek lower energy consumption and improve the performance of computing tasks, we provide an energy-efficient computation offloading and transmit power allocation scheme that reduces energy consumption and completion time. We formulate the energy efficiency cost minimization problem, which satisfies the completion time deadline constraint of MDs in an MEC system. In addition, the corresponding Karush–Kuhn–Tucker conditions are applied to solve the optimization problem, and a new algorithm comprising the computation offloading policy and transmission power allocation is presented. Numerical results demonstrate that our proposed scheme, with the optimal computation offloading policy and adapted transmission power for MDs, outperforms local computing and full offloading methods in terms of energy consumption and completion delay. Consequently, our proposed system could help overcome the restrictions on computation resources and battery life of mobile devices to meet the requirements of new applications.
15
Shi, Yongpeng, Yujie Xia, and Ya Gao. "Cross-Server Computation Offloading for Multi-Task Mobile Edge Computing." Information 11, no. 2 (February 10, 2020): 96. http://dx.doi.org/10.3390/info11020096.
Abstract:
As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computation-intensive applications and the resource-constrained mobile devices. However, most available studies on computation offloading in MEC assume that the edge severs host various applications and can cope with all kinds of computation tasks, ignoring limited computing resources and storage capacities of the MEC architecture. To make full use of the available resources deployed on the edge servers, in this paper, we study the cross-server computation offloading problem to realize the collaboration among multiple edge servers for multi-task mobile edge computing, and propose a greedy approximation algorithm as our solution to minimize the overall consumed energy. Numerical results validate that our proposed method can not only give near-optimal solutions with much higher computational efficiency, but also scale well with the growing number of mobile devices and tasks.
16
Liu, Xiaokai, Fangmin Xu, Ye Xiao, Xiaoming Zhou, Zhao Li, Chenglin Zhao, and Min Zhang. "Multiple Local-Edge-Cloud Collaboration Strategies in Industrial Internet of Things: A Hybrid Genetic-Based Approach." Mathematical Problems in Engineering 2022 (September 24, 2022): 1–12. http://dx.doi.org/10.1155/2022/1486580.
Abstract:
To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading mode of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge computing architecture of an intelligent factory, this paper puts forward five different computation offloading methods. From a global perspective, the energy consumption and delay of tasks offloading in local, edge, cloud center, local-edge collaboration, and local-edge-cloud collaboration are considered. In this paper, the algorithm based on the genetic algorithm and particle swarm optimization is used to design and obtain the decision task flow offloading strategy with the lowest energy consumption and delay. Simulation results show that the proposed algorithm can reduce the computation offloading energy consumption and delay of mobile devices.
17
Li, Xianwei, and Baoliu Ye. "Latency-Aware Computation Offloading for 5G Networks in Edge Computing." Security and Communication Networks 2021 (September 22, 2021): 1–15. http://dx.doi.org/10.1155/2021/8800234.
Abstract:
With the development of Internet of Things, massive computation-intensive tasks are generated by mobile devices whose limited computing and storage capacity lead to poor quality of services. Edge computing, as an effective computing paradigm, was proposed for efficient and real-time data processing by providing computing resources at the edge of the network. The deployment of 5G promises to speed up data transmission but also further increases the tasks to be offloaded. However, how to transfer the data or tasks to the edge servers in 5G for processing with high response efficiency remains a challenge. In this paper, a latency-aware computation offloading method in 5G networks is proposed. Firstly, the latency and energy consumption models of edge computation offloading in 5G are defined. Then the fine-grained computation offloading method is employed to reduce the overall completion time of the tasks. The approach is further extended to solve the multiuser computation offloading problem. To verify the effectiveness of the proposed method, extensive simulation experiments are conducted. The results show that the proposed offloading method can effectively reduce the execution latency of the tasks.
18
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.
Abstract:
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.
19
Xing, Yongli, Tao Ye, Sami Ullah, Muhammad Waqas, Hisham Alasmary, and Zihui Liu. "A computational offloading optimization scheme based on deep reinforcement learning in perceptual network." PLOS ONE 18, no. 2 (February 24, 2023): e0280468. http://dx.doi.org/10.1371/journal.pone.0280468.
Abstract:
Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms.
20
Maray, Mohammed, and Junaid Shuja. "Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues." Mobile Information Systems 2022 (June 28, 2022): 1–17. http://dx.doi.org/10.1155/2022/1121822.
Abstract:
Cloud and mobile edge computing (MEC) provides a wide range of computing services for mobile applications. In particular, mobile edge computing enables a computing and storage infrastructure provisioned closely to the end-users at the edge of a cellular network. The small base stations are deployed to establish a mobile edge network that can be coined with cloud infrastructure. A large number of enterprises and individuals rely on services offered by mobile edge and clouds to meet their computational and storage demands. Based on user behavior and demand, the computational tasks are first offloaded from mobile users to the mobile edge network and then executed at one or several specific base stations in the mobile edge network. The MEC architecture has the capability to handle a large number of devices that in turn generate high volumes of traffic. In this work, we first provide a holistic overview of MCC/MEC technology that includes the background and evolution of remote computation technologies. Then, the main part of this paper surveys up-to-date research on the concepts of offloading mechanisms, offloading granularities, and computational offloading techniques. Furthermore, we discuss the offloading mechanism in the static and dynamic environment along with optimization techniques. We further discuss the challenges and potential future directions for MEC research.
21
Zhang, Peiying, Yu Su, Boxiao Li, Lei Liu, Cong Wang, Wei Zhang, and Lizhuang Tan. "Deep Reinforcement Learning Based Computation Offloading in UAV-Assisted Edge Computing." Drones 7, no. 3 (March 19, 2023): 213. http://dx.doi.org/10.3390/drones7030213.
Abstract:
Traditional multi-access edge computing (MEC) often has difficulty processing large amounts of data in the face of high computationally intensive tasks, so it needs to offload policies to offload computation tasks to adjacent edge servers. The computation offloading problem is a mixed integer programming non-convex problem, and it is difficult to have a good solution. Meanwihle, the cost of deploying servers is often high when providing edge computing services in remote areas or some complex terrains. In this paper, the unmanned aerial vehicle (UAV) is introduced into the multi-access edge computing network, and a computation offloading method based on deep reinforcement learning in UAV-assisted multi-access edge computing network (DRCOM) is proposed. We use the UAV as the space base station of MEC, and it transforms computation task offloading problems of MEC into two sub-problems: find the optimal solution of whether each user’s device is offloaded through deep reinforcement learning; allocate resources. We compared our algorithm with other three offloading methods, i.e., LC, CO, and LRA. The maximum computation rate of our algorithm DRCOM is 142.38% higher than LC, 50.37% higher than CO, and 12.44% higher than LRA. The experimental results demonstrate that DRCOM greatly improves the computation rate.
22
Wang, Yanyan, Lin Wang, Ruijuan Zheng, Xuhui Zhao, and Muhua Liu. "Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes." Sensors 21, no. 7 (March 28, 2021): 2347. http://dx.doi.org/10.3390/s21072347.
Abstract:
In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay.
23
Sun, Dingmi, Yimin Chen, and Hao Li. "Intelligent Vehicle Computation Offloading in Vehicular Ad Hoc Networks: A Multi-Agent LSTM Approach with Deep Reinforcement Learning." Mathematics 12, no. 3 (January 28, 2024): 424. http://dx.doi.org/10.3390/math12030424.
Abstract:
As distributed computing evolves, edge computing has become increasingly important. It decentralizes resources like computation, storage, and bandwidth, making them more accessible to users, particularly in dynamic Telematics environments. However, these environments are marked by high levels of dynamic uncertainty due to frequent changes in vehicle location, network status, and edge server workload. This complexity poses substantial challenges in rapidly and accurately handling computation offloading, resource allocation, and delivering low-latency services in such a variable environment. To address these challenges, this paper introduces a “Cloud–Edge–End” collaborative model for Telematics edge computing. Building upon this model, we develop a novel distributed service offloading method, LSTM Muti-Agent Deep Reinforcement Learning (L-MADRL), which integrates deep learning with deep reinforcement learning. This method includes a predictive model capable of forecasting the future demands on intelligent vehicles and edge servers. Furthermore, we conceptualize the computational offloading problem as a Markov decision process and employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) approach for autonomous, distributed offloading decision-making. Our empirical results demonstrate that the L-MADRL algorithm substantially reduces service latency and energy consumption by 5–20%, compared to existing algorithms, while also maintaining a balanced load across edge servers in diverse Telematics edge computing scenarios.
24
Wu, Jian, Min Jia, Liang Zhang, and Qing Guo. "DNNs Based Computation Offloading for LEO Satellite Edge Computing." Electronics 11, no. 24 (December 9, 2022): 4108. http://dx.doi.org/10.3390/electronics11244108.
Abstract:
Huge low earth orbit (LEO) satellite networks can achieve global coverage with low latency. In addition, mobile edge computing (MEC) servers can be mounted on LEO satellites to provide computing offloading services for users in remote areas. A multi-user multi-task system model is modeled and the problem of user’s offloading decisions and bandwidth allocation is formulated as a mixed integer programming problem to minimize the system utility function expressed as the weighted sum of the system energy consumption and delay. However, it cannot be effectively solved by general optimizations. Thus, a deep learning-based offloading algorithm for LEO satellite edge computing networks is proposed to generate offloading decisions through multiple parallel deep neural networks (DNNs) and store the newly generated optimal offloading decisions in memory to improve all DNNs to obtain near-optimal offloading decisions. Moreover, the optimal bandwidth allocation scheme of the system is theoretically derived for the user’s bandwidth allocation problem. The simulation results show that the proposed algorithm can achieve a good convergence effect within a small number of training steps, and obtain the optimal system utility function values compared with the comparative algorithms under different system parameters, and the time cost of the system and DNNs is very satisfactory.
25
Fayi, Sharifah Yaqoub, and Zhengguo Sheng. "A survey of security, privacy and trust issues in vehicular computation offloading and their solutions using blockchain." Open Research Europe 3 (July 7, 2023): 110. http://dx.doi.org/10.12688/openreseurope.16189.1.
Abstract:
Continuous improvement in transportation systems and smart vehicles' appearance make new highly intensive applications. Complex applications need high-performance capabilities, real-time responses, and generate massive amounts of data to process and exchange. This presents the idea of vehicular edge computing (VEC), which is proposed to handle complex applications and satisfy smart vehicle processing requirements. VEC enables computation offloading to an edge server to reduce communication latency, execution cost and energy consumption greatly. However, offloading to another node opens up new vulnerabilities regarding security and privacy. Moreover, trust issues in such an untrustworthy environment need an effective trust management solution and incentive mechanisms to improve overall security. This will increase the computation offloading success rate and the vehicles' willingness to share their resources. Particularly given the high transportability and heterogeneity of vehicular networks, the conventional security and trust management methods are inadequate. Blockchain, the rapidly emerging trend technology, is a unique solution that can help overcome security and privacy issues and meet trust management and incentive mechanism goals. Blockchain’s immutable distributed ledger, traceability, consensus validation system and smart contract features can improve vehicular network security. Although most research is focused on enhancing the performance of computation offloading algorithms, blockchain security solutions in computation offloading scenarios are not fully discussed. Thus, security and trust issues related to computation offloading in VEC environments need more consideration since supporting the new complex vehicular applications is essential. Therefore, this paper provides a review of recent surveys and studies, an overview of VEC, computation offloading and blockchain, in addition to discussing security, privacy and trust in vehicular networks and computation offloading while considering blockchain as a distributed security solution. We propose a new paradigm called blockchain edge of vehicle (BEoV) at the end, which enables several blockchain-based security services for vehicular computation offloading in particular.
26
Fayi, Sharifah Yaqoub, and Zhengguo Sheng. "A survey of security, privacy and trust issues in vehicular computation offloading and their solutions using blockchain." Open Research Europe 3 (October 25, 2023): 110. http://dx.doi.org/10.12688/openreseurope.16189.2.
Abstract:
Continuous improvement in transportation systems and smart vehicles' appearance make new highly intensive applications. Complex applications need high-performance capabilities, real-time responses, and generate massive amounts of data to process and exchange. This presents the idea of vehicular edge computing (VEC), which is proposed to handle complex applications and satisfy smart vehicle processing requirements. VEC enables computation offloading to an edge server to reduce communication latency, execution cost and energy consumption greatly. However, offloading to another node opens up new vulnerabilities regarding security and privacy. Moreover, trust issues in such an untrustworthy environment need an effective trust management solution and incentive mechanisms to improve overall security. This will increase the computation offloading success rate and the vehicles' willingness to share their resources. Particularly given the high transportability and heterogeneity of vehicular networks, the conventional security and trust management methods are inadequate. Blockchain, the rapidly emerging trend technology, is a unique solution that can help overcome security and privacy issues and meet trust management and incentive mechanism goals. Blockchain’s immutable distributed ledger, traceability, consensus validation system and smart contract features can improve vehicular network security. Although most research is focused on enhancing the performance of computation offloading algorithms, blockchain security solutions in computation offloading scenarios are not fully discussed. Thus, security and trust issues related to computation offloading in VEC environments need more consideration since supporting the new complex vehicular applications is essential. Therefore, this paper provides a review of recent surveys and studies, an overview of VEC, computation offloading and blockchain, in addition to discussing security, privacy and trust in vehicular networks and computation offloading while considering blockchain as a distributed security solution. We propose a new paradigm called blockchain edge of vehicle (BEoV) at the end, which enables several blockchain-based security services for vehicular computation offloading in particular.
27
Babar, Mohammad, and Muhammad Sohail Khan. "ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems." International Journal of Distributed Sensor Networks 17, no. 7 (July 2021): 155014772110353. http://dx.doi.org/10.1177/15501477211035332.
Abstract:
Edge computing brings down storage, computation, and communication services from the cloud server to the network edge, resulting in low latency and high availability. The Internet of things (IoT) devices are resource-constrained, unable to process compute-intensive tasks. The convergence of edge computing and IoT with computation offloading offers a feasible solution in terms of performance. Besides these, computation offload saves energy, reduces computation time, and extends the battery life of resource constrain IoT devices. However, edge computing faces the scalability problem, when IoT devices in large numbers approach edge for computation offloading requests. This research article presents a three-tier energy-efficient framework to address the scalability issue in edge computing. We introduced an energy-efficient recursive clustering technique at the IoT layer that prioritizes the tasks based on weight. Each selected task with the highest weight value offloads to the edge server for execution. A lightweight client–server architecture affirms to reduce the computation offloading overhead. The proposed energy-efficient framework for IoT algorithm makes efficient computation offload decisions while considering energy and latency constraints. The energy-efficient framework minimizes the energy consumption of IoT devices, decreases computation time and computation overhead, and scales the edge server. Numerical results show that the proposed framework satisfies the quality of service requirements of both delay-sensitive and delay-tolerant applications by minimizing energy and increasing the lifetime of devices.
28
Khisa, Shreya, and Sangman Moh. "Dynamic Computation Offloading Based on Q-Learning for UAV-Based Mobile Edge Computing." Korean Institute of Smart Media 12, no. 3 (April 30, 2023): 68–76. http://dx.doi.org/10.30693/smj.2023.12.3.68.
Abstract:
Emerging mobile edge computing (MEC) can be used in battery-constrained Internet of things (IoT). The execution latency of IoT applications can be improved by offloading computation-intensive tasks to an MEC server. Recently, the popularity of unmanned aerial vehicles (UAVs) has increased rapidly, and UAV-based MEC systems are receiving considerable attention. In this paper, we propose a dynamic computation offloading paradigm for UAV-based MEC systems, in which a UAV flies over an urban environment and provides edge services to IoT devices on the ground. Since most IoT devices are energy-constrained, we formulate our problem as a Markov decision process considering the energy level of the battery of each IoT device. We also use model-free Q-learning for time-critical tasks to maximize the system utility. According to our performance study, the proposed scheme can achieve desirable convergence properties and make intelligent offloading decisions.
29
Changyuan Xu, Changyuan Xu, Cheng Zhan Changyuan Xu, Jingrui Liao Cheng Zhan, and Bin Zeng Jingrui Liao. "UAV-Enabled Mobile Edge Computing with Binary Computation Offloading and Energy Constraints." 網際網路技術學刊 23, no. 5 (September 2022): 947–54. http://dx.doi.org/10.53106/160792642022092305003.
Abstract:
<p>Mobile edge computing (MEC) has been considered to provide computation services near the edge of mobile networks, while the unmanned aerial vehicle (UAV) is becoming an important integrated component to extend service coverage. In this paper, we consider a UAV-enabled MEC with binary computation offloading and energy constraints, where an energy-limited UAV is employed as an aerial edge server and each task of devices is either executing locally or offloading to the aerial edge server as a whole. To provide fairness among different ground devices, we aim to maximize the minimum computation throughput among all devices via the joint design of computing mode selection and UAV trajectory as well as resource allocation. The optimization problem is formulated as a mixed-integer non-linear problem consisting of binary variables, which is difficult to tackle. By employing deductive penalty function to penalize the effect of non-binary solution, we develop an efficient iterative algorithm to obtain a suboptimal solution via leveraging the penalty successive convex approximation (P-SCA) method and difference of two convex (D.C.) optimization framework, where the algorithm is guaranteed to converge. Extensive simulations are conducted and the results with different system parameters show that the proposed joint design algorithm can improve the computation throughput by about 40% compared to other benchmark schemes.</p> <p> </p>
30
Hasanin, Tawfiq, Aisha Alsobhi, Adil Khadidos, Ayman Qahmash, Alaa Khadidos, and Gabriel Ayodeji Ogunmola. "Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm." Applied Bionics and Biomechanics 2021 (November 10, 2021): 1–12. http://dx.doi.org/10.1155/2021/9014559.
Abstract:
Mobile edge computing (MEC) is a paradigm novel computing that promises the dramatic effect of reduction in latency and consumption of energy by computation offloading intensive; these tasks to the edge clouds in proximity close to the smart mobile users. In this research, reduce the offloading and latency between the edge computing and multiusers under the environment IoT application in 5G using bald eagle search optimization algorithm. The deep learning approach may consume high computational complexity and more time. In an edge computing system, devices can offload their computation-intensive tasks to the edge servers to save energy and shorten their latency. The bald eagle algorithm (BES) is the advanced optimization algorithm that resembles the strategy of eagle hunting. The strategies are select, search, and swooping stages. Previously, the BES algorithm is used to consume the energy and distance; to improve the better energy and reduce the offloading latency in this research and some delays occur when devices increase causes demand for cloud data, it can be improved by offering ROS (resource) estimation. To enhance the BES algorithm that introduces the ROS estimation stage to select the better ROSs, an edge system, which offloads the most appropriate IoT subtasks to edge servers then the expected time of execution, got minimized. Based on multiuser offloading, we proposed a bald eagle search optimization algorithm that can effectively reduce the end-end time to get fast and near-optimal IoT devices. The latency is reduced from the cloud to the local; this can be overcome by using edge computing, and deep learning expects faster and better results from the network. This can be proposed by BES algorithm technique that is better than other conventional methods that are compared on results to minimize the offloading latency. Then, the simulation is done to show the efficiency and stability by reducing the offloading latency.
31
Lu, Yanfei, Zengzi Chen, Qinghe Gao, Tao Jing, and Jin Qian. "A Mobility-Aware and Sociality-Associate Computation Offloading Strategy for IoT." Wireless Communications and Mobile Computing 2021 (June 25, 2021): 1–12. http://dx.doi.org/10.1155/2021/9919541.
Abstract:
Mobile edge computing, a promising paradigm, brings services closer to a user by leveraging the available resources in an edge network. The crux of MEC is to reasonably allocate resources to satisfy the computing requirements of each node in the network. In this paper, we investigate the service migration problem of the offloading scheme in a power-constrained network consisting of multiple mobile users and fixed edge servers. We propose an affinity propagation-based clustering-assisted offloading scheme by taking into account the users’ mobility prediction and sociality association between mobile users and edge servers. The clustering results provide the candidate edge servers, which greatly reduces the complexity of observing all edge servers and decreases the rate of service migration. Besides, the available resource of candidate edge servers and the channel conditions are considered to optimize the offloading scheme to guarantee the quality of service. Numerical simulation results demonstrate that our offloading strategy can enhance the data processing capability of power-constrained networks and reach computing load balance.
32
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.
Abstract:
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.
33
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.
Abstract:
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.
34
Wang, Mingzhi, Tao Wu, Xiaochen Fan, Penghao Sun, Yuben Qu, and Panlong Yang. "TPD: Temporal and Positional Computation Offloading with Dynamic and Dependent Tasks." Wireless Communications and Mobile Computing 2021 (November 10, 2021): 1–15. http://dx.doi.org/10.1155/2021/3877285.
Abstract:
With the rapid development of wireless communication technologies and the proliferation of the urban Internet of Things (IoT), the paradigm of mobile computing has been shifting from centralized clouds to edge networks. As an enabling paradigm for computation-intensive and latency-sensitive computation tasks, mobile edge computing (MEC) can provide in-proximity computing services for resource-constrained IoT devices. Nevertheless, it remains challenging to optimize computation offloading from IoT devices to heterogeneous edge servers, considering complex intertask dependency, limited bandwidth, and dynamic networks. In this paper, we address the above challenges in MEC with TPD, that is, temporal and positional computation offloading with dynamic-dependent tasks. In particular, we investigate channel interference and intertask dependency by considering the position and moment of computation offloading simultaneously. We define a novel criterion for assessing the criticality of each task, and we identify the critical path based on a directed acyclic graph of all tasks. Furthermore, we propose an online algorithm for finding the optimal computation offloading strategy with intertask dependency and adjusting the strategy in real-time when facing dynamic tasks. Extensive simulation results show that our algorithm reduces significantly the time to complete all tasks by 30–60% in different scenarios and takes less time to adjust the offloading strategy in dynamic MEC systems.
35
Huang, Mengxing, Qianhao Zhai, Yinjie Chen, Siling Feng, and Feng Shu. "Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing." Sensors 21, no. 8 (April 8, 2021): 2628. http://dx.doi.org/10.3390/s21082628.
Abstract:
Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.
36
Maftah, Sara, Mohamed El Ghmary, Hamid El Bouabidi, Mohamed Amnai, and Ali Ouacha. "Optimal Task Processing and Energy Consumption Using Intelligent Offloading in Mobile Edge Computing." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 20 (October 31, 2022): 130–42. http://dx.doi.org/10.3991/ijim.v16i20.34373.
Abstract:
The appearance of Edge Computing with the possibility to bring powerful computation servers near the mobile device is a major stepping stone towards better user experience and resource consumption optimization. Due to the Internet of Things invasion that led to the constant demand for communication and computation resources, many issues were imposed in order to deliver a seamless service within an optimized cost of time and energy, since most of the applications nowadays require real response time and rely on a limited battery resource. Therefore, Mobile Edge Computing is the new reliable paradigm in terms of communication and computation consumption by the mobile devices. Mobile Edge Computing rely on computation offloading to surpass cloud-based technologies issues and break the limitations of mobile devices such as computing, storage and battery resources. However, computation offloading is not always the optimal choice to adopt, which makes the offloading decision a crucial part in which many parameters should be taken in consideration such as delegating the heavy tasks to the appropriate machine within the network by migrating the high-resource node to an edge server and lend these capabilities to the low-resources one. In this paper, we use an Edge Computing simulator to see how network delay can impact the delivery of a certain result, we also experiment computation offloading using a two-tier with Edge Orchestration architecture, which turns out to be efficient in terms of processing time.
37
Cao, Shaohua, Shu Chen, Hui Chen, Hanqing Zhang, Zijun Zhan, and Weishan Zhang. "HCOME: Research on Hybrid Computation Offloading Strategy for MEC Based on DDPG." Electronics 12, no. 3 (January 21, 2023): 562. http://dx.doi.org/10.3390/electronics12030562.
Abstract:
With the growth of the Internet of Things, smart devices are subsequently generating a large number of computation-intensive and latency-sensitive tasks. Mobile edge computing can provide resources in close proximity, greatly reducing service latency and alleviating congestion in mobile core networks. Due to the instability of the mobile edge computing environment, it was difficult to guarantee the quality of service for users. To address this problem, a hybrid computation offloading framework based on Deep Deterministic Policy Gradient (DDPG) in IoT is proposed. The framework is a system consisting of edge servers and user devices. It is used to acquire the environment state through Software Defined Network technologies and generate the offloading strategy by Deep Deterministic Policy Gradient. The optimization objectives in this paper include the total system overhead of the mobile edge computing system, and considering both network load and computational load, an optimal offloading strategy can be obtained to enable users to obtain a better quality of service. Finally, the experimental results show that the algorithm outperforms the comparison algorithm and can reduce the system latency by 20%, while the network load and computational load are also more stable.
38
Zheng, Yi-fan, Ning Wei, and Yi Liu. "Collaborative Computation for Offloading and Caching Strategy Using Intelligent Edge Computing." Mobile Information Systems 2022 (July 30, 2022): 1–12. http://dx.doi.org/10.1155/2022/4840801.
Abstract:
Computation offloading and caching strategy is a well-established concept for allowing mobile applications that are high in resources. Furthermore, the unloaded duties can be replicated when several customers are within easy access because of the rising mobile cooperation applications. However, the problematic characteristics of offloading and caching strategy delay bandwidth transfer from mobile computing devices to cloud computing. A new technical approach to restrict the issues and unwanted functions in offloading and caching is called the intellectual power computing framework (IPCF). IPCF depends on two conventional offloading and caching strategies called systematic offloading technique and managerial migrant caching. The migration of data transfer from the destination to location basis lies in the systematic offloading technique to restrict network delays. Managerial migrant caching duplicates the data required by the mobile terminals (MTs) from the remote cloud storage to the mobile application to reduce the access time. The forbidden actions in current techniques are refused, and solutions are enhanced for better communication strategy. Thus, the simulation analysis performs better in IPCF to reach efficient outcomes for offloading and caching processes.
39
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.
Abstract:
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.
40
Chen, Ruoyu, Yanfang Fan, Shuang Yuan, and Yanbo Hao. "Vehicle Collaborative Partial Offloading Strategy in Vehicular Edge Computing." Mathematics 12, no. 10 (May 9, 2024): 1466. http://dx.doi.org/10.3390/math12101466.
Abstract:
Vehicular Edge Computing (VEC) is a crucial application of Mobile Edge Computing (MEC) in vehicular networks. In VEC networks, the computation tasks of vehicle terminals (VTs) can be offloaded to nearby MEC servers, overcoming the limitations of VTs’ processing power and reducing latency caused by distant cloud communication. However, a mismatch between VTs’ demanding tasks and MEC servers’ limited resources can overload MEC servers, impacting Quality of Service (QoS) for computationally intensive tasks. Additionally, vehicle mobility can disrupt communication with static MEC servers, further affecting VTs’ QoS. To address these challenges, this paper proposes a vehicle collaborative partial computation offloading model. This model allows VTs to offload tasks to two types of service nodes: collaborative vehicles and MEC servers. Factors like a vehicle’s mobility, remaining battery power, and available computational power are also considered when evaluating its suitability for collaborative offloading. Furthermore, we design a deep reinforcement learning-based strategy for collaborative partial computation offloading that minimizes overall task delay while meeting individual latency constraints. Experimental results demonstrate that compared to traditional approaches without vehicle collaboration, this scheme significantly reduces latency and achieves a significant reduction (around 2%) in the failure rate under tighter latency constraints.
41
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.
Abstract:
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.
42
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.
Abstract:
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.
43
M. Al-Tuhafi, Ola, and Emad H. Al-Hemiary. "EDGE-TO-CLOUD ADAPTIVE OFFLOADING FOR NEXT-GENERATION SERVICES." Iraqi Journal of Information and Communication Technology 6, no. 2 (August 31, 2023): 58–67. http://dx.doi.org/10.31987/ijict.6.2.230.
Abstract:
Due to the continuous growth of user traffic demands, there is a need to cope with the increased processing and storage requirements. The increased number of connected end devices causes a problem with carrying the generated loads efficiently. Edge and cloud computing and storage are real solutions to overcome the limitations of end devices on many prospective including computation capabilities, storage capabilities, and power consumption. Terminal devices offload their overflow tasks to the cloud for processing, analysis, and storage. This paper aims to improve computation offloading from edge nodes to the cloud in Internet of Things networks by making efficient decisions using an adaptive offloading algorithm. Offloading is controlled by a processing time offloading threshold value, which is determined automatically by edge nodes based on their traffic intensity and adaptively increased or decreased in loads. The proposed algorithm had been programmed and simulated; experimental evaluations show that the proposed adaptive offloading algorithm minimizes the edge mean response time by up to 58% and the cloud mean response time by up to 25% compared to the existing fixed, pre-defined offloading threshold value.
44
Qi, Ping. "Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment." Wireless Communications and Mobile Computing 2022 (January 7, 2022): 1–13. http://dx.doi.org/10.1155/2022/2890473.
Abstract:
Traditional intent recognition algorithms of intelligent prosthesis often use deep learning technology. However, deep learning’s high accuracy comes at the expense of high computational and energy consumption requirements. Mobile edge computing is a viable solution to meet the high computation and real-time execution requirements of deep learning algorithm on mobile device. In this paper, we consider the computation offloading problem of multiple heterogeneous edge servers in intelligent prosthesis scenario. Firstly, we present the problem definition and the detail design of MEC-based task offloading model for deep neural network. Then, considering the mobility of amputees, the mobility-aware energy consumption model and latency model are proposed. By deploying the deep learning-based motion intent recognition algorithm on intelligent prosthesis in a real-world MEC environment, the effectiveness of the task offloading and scheduling strategy is demonstrated. The experimental results show that the proposed algorithms can always find the optimal task offloading and scheduling decision.
45
Santa, José, Pedro J. Fernández, Ramon Sanchez-Iborra, Jordi Ortiz, and Antonio F. Skarmeta. "Offloading Positioning onto Network Edge." Wireless Communications and Mobile Computing 2018 (October 23, 2018): 1–13. http://dx.doi.org/10.1155/2018/7868796.
Abstract:
While satellite or cellular positioning implies dedicated hardware or network infrastructure functions, indoor navigation or novel IoT positioning techniques include flexible storage and computation requirements that can be fulfilled by both end-devices or cloud back-ends. Hybrid positioning systems support the integration of several algorithms and technologies; however, the common trend of delegating position calculation and storage of local geoinformation to mobile devices or centralized servers causes performance degradation in terms of delay, battery usage, and waste of network resources. The strategy followed in this work is offloading this computation effort onto the network edge, following a Mobile Edge Computing (MEC) approach. MEC nodes in the access network of the mobile device are in charge of receiving navigation data coming from both the smart infrastructure and mobile devices, in order to compute the final position following a hybrid approach. With the aim of supporting mobility and the access to multiple networks, an Information Centric Networking (ICN) solution is used to access generic position information resources. The presented system currently supports WiFi, Bluetooth LE, GPS, cellular and NFC technologies, involving both indoor and outdoor positioning, using fingerprinting and proximity for indoor navigation, and the integration of smart infrastructure data sources such as the door opening system within real smart campus deployment. Evaluations carried out reveal latency improvements of 50%, as compared with a regular configuration where position fixes are computed by mobile devices; at the same time the MEC solution offers extra flexibility features to manage positioning databases and algorithms and move extensive computation from constrained devices to the edge.
46
Li, Xianwei, Guolong Chen, Liang Zhao, and Bo Wei. "Multimedia Applications Processing and Computation Resource Allocation in MEC-Assisted SIoT Systems with DVS." Mathematics 10, no. 9 (May 7, 2022): 1593. http://dx.doi.org/10.3390/math10091593.
Abstract:
Due to the advancements of information technologies and the Internet of Things (IoT), the number of distributed sensors and IoT devices in the social IoT (SIoT) systems is proliferating. This has led to various multimedia applications, face recognition and augmented reality (AR). These applications are computation-intensive and delay-sensitive and have become popular in our daily life. However, IoT devices are well-known for their constrained computational resources, which hinders the execution of these applications. Mobile edge computing (MEC) has appeared and been deemed a prospective paradigm to solve this issue. Migrating the applications of IoT devices to be executed in the edge cloud can not only provide computational resources to process these applications but also lower the transmission latency between the IoT devices and the edge cloud. In this paper, computation resource allocation and multimedia applications offloading in MEC-assisted SIoT systems are investigated. We aim to optimize the resource allocation and application offloading by jointly minimizing the execution latency of multimedia applications and the consumed energy of IoT devices. The studied problem is a formulation of the total computation overhead minimization problem by optimizing the computational resources in the edge servers. Besides, as the technology of dynamic voltage scaling (DVS) can offer more flexibility for the MEC system design, we incorporate it into the application offloading. Since the studied problem is a mixed-integer nonlinear programming (MINP) problem, an efficient method is proposed to address it. By comparing with the baseline schemes, the theoretic analysis and simulation results demonstrate that the proposed multimedia applications offloading method can improve the performances of MEC-assisted SIoT systems for the most part.
47
Dong, Chongwu, and Wushao Wen. "Joint Optimization for Task Offloading in Edge Computing: An Evolutionary Game Approach." Sensors 19, no. 3 (February 12, 2019): 740. http://dx.doi.org/10.3390/s19030740.
Abstract:
The mobile edge computing (MEC) paradigm provides a promising solution to solve the resource-insufficiency problem in mobile terminals by offloading computation-intensive and delay-sensitive tasks to nearby edge nodes. However, limited computation resources in edge nodes may not be sufficient to serve excessive offloading tasks exceeding the computation capacities of edge nodes. Therefore, multiple edge clouds with a complementary central cloud coordinated to serve users is the efficient architecture to satisfy users’ Quality-of-Service (QoS) requirements while trying to minimize some network service providers’ cost. We study a dynamic, decentralized resource-allocation strategy based on evolutionary game theory to deal with task offloading to multiple heterogeneous edge nodes and central clouds among multi-users. In our strategy, the resource competition among multi-users is modeled by the process of replicator dynamics. During the process, our strategy can achieve one evolutionary equilibrium, meeting users’ QoS requirements under resource constraints of edge nodes. The stability and fairness of this strategy is also proved by mathematical analysis. Illustrative studies show the effectiveness of our proposed strategy, outperforming other alternative methods.
48
Huynh, Luan N. T., Quoc-Viet Pham, Xuan-Qui Pham, Tri D. T. Nguyen, Md Delowar Hossain, and Eui-Nam Huh. "Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach." Applied Sciences 10, no. 1 (December 26, 2019): 203. http://dx.doi.org/10.3390/app10010203.
Abstract:
In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.
49
Deng, Xiaoheng, Zihui Sun, Deng Li, Jie Luo, and Shaohua Wan. "User-Centric Computation Offloading for Edge Computing." IEEE Internet of Things Journal 8, no. 16 (August 15, 2021): 12559–68. http://dx.doi.org/10.1109/jiot.2021.3057694.
50
Luo, Jie, Xiaoheng Deng, Honggang Zhang, and Huamei Qi. "QoE-driven computation offloading for Edge Computing." Journal of Systems Architecture 97 (August 2019): 34–39. http://dx.doi.org/10.1016/j.sysarc.2019.01.019.