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1
Hasheminezhad, Mahdi, and Ardeshir Bahreininejad. "A Multi-Agent Taxi Dispatching System." International Journal of Agent Technologies and Systems 2, no. 2 (April 2010): 1–10. http://dx.doi.org/10.4018/jats.2010040101.
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The taxi assignment problem may be categorized as a vehicle routing problem. ?When placed in the field of resource allocation, it is a dynamic problem in which ?the situation changes as the work progresses. This paper presents a new agent-based approach to tackle the taxi assignment problem. New parameters are ?introduced to increase the satisfaction of the drivers. The authors propose a new algorithm to improve the parameters. Simulations were also conducted to examine the efficiency of the proposed method. The results indicate the effectiveness of the proposed taxi assignment/dispatching approach.
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Leng, Tao, Xiaoyao Li, Dongwei Hu, Gaofeng Cui, and Weidong Wang. "Collaborative Computing and Resource Allocation for LEO Satellite-Assisted Internet of Things." Wireless Communications and Mobile Computing 2021 (September 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/4212548.
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Satellite-assisted internet of things (S-IoT), especially the S-IoT based on low earth orbit (LEO) satellite, plays an important role in future wireless systems. However, the limited on-board communication and computing resource and high mobility of LEO satellites make it hard to provide satisfied service for IoT users. To maximize the task completion rate under latency constraints, collaborative computing and resource allocation among LEO networks are jointly investigated in this paper, and the joint task offloading, scheduling, and resource allocation is formulated as a dynamic mixed-integer problem. To tack the complex problem, we decouple it into two subproblems with low complexity. First, the max-min fairness is adopted to minimize the maximum latency via optimal resource allocation with fixed task assignment. Then, the joint task offloading and scheduling is formulated as a Markov decision process with optimal communication and computing resource allocation, and deep reinforcement learning is utilized to obtain long-term benefits. Simulation results show that the proposed scheme has superior performance compared with other referred schemes.
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Robu, V., E. H. Gerding, S. Stein, D. C. Parkes, A. Rogers, and N. R. Jennings. "An Online Mechanism for Multi-Unit Demand and its Application to Plug-in Hybrid Electric Vehicle Charging." Journal of Artificial Intelligence Research 48 (October 23, 2013): 175–230. http://dx.doi.org/10.1613/jair.4064.
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We develop an online mechanism for the allocation of an expiring resource to a dynamic agent population. Each agent has a non-increasing marginal valuation function for the resource, and an upper limit on the number of units that can be allocated in any period. We propose two versions on a truthful allocation mechanism. Each modifies the decisions of a greedy online assignment algorithm by sometimes cancelling an allocation of resources. One version makes this modification immediately upon an allocation decision while a second waits until the point at which an agent departs the market. Adopting a prior-free framework, we show that the second approach has better worst-case allocative efficiency and is more scalable. On the other hand, the first approach (with immediate cancellation) may be easier in practice because it does not need to reclaim units previously allocated. We consider an application to recharging plug-in hybrid electric vehicles (PHEVs). Using data from a real-world trial of PHEVs in the UK, we demonstrate higher system performance than a fixed price system, performance comparable with a standard, but non-truthful scheduling heuristic, and the ability to support 50% more vehicles at the same fuel cost than a simple randomized policy.
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Fu, Yanming, Yuming Shen, and Liang Tang. "A Dynamic Task Allocation Framework in Mobile Crowd Sensing with D3QN." Sensors 23, no. 13 (July 1, 2023): 6088. http://dx.doi.org/10.3390/s23136088.
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With the coverage of sensor-rich smart devices (smartphones, iPads, etc.), combined with the need to collect large amounts of data, mobile crowd sensing (MCS) has gradually attracted the attention of academics in recent years. MCS is a new and promising model for mass perception and computational data collection. The main function is to recruit a large group of participants with mobile devices to perform sensing tasks in a given area. Task assignment is an important research topic in MCS systems, which aims to efficiently assign sensing tasks to recruited workers. Previous studies have focused on greedy or heuristic approaches, whereas the MCS task allocation problem is usually an NP-hard optimisation problem due to various resource and quality constraints, and traditional greedy or heuristic approaches usually suffer from performance loss to some extent. In addition, the platform-centric task allocation model usually considers the interests of the platform and ignores the feelings of other participants, to the detriment of the platform’s development. Therefore, in this paper, deep reinforcement learning methods are used to find more efficient task assignment solutions, and a weighted approach is adopted to optimise multiple objectives. Specifically, we use a double deep Q network (D3QN) based on the dueling architecture to solve the task allocation problem. Since the maximum travel distance of the workers, the reward value, and the random arrival and time sensitivity of the sensing tasks are considered, this is a dynamic task allocation problem under multiple constraints. For dynamic problems, traditional heuristics (eg, pso, genetics) are often difficult to solve from a modeling and practical perspective. Reinforcement learning can obtain sub-optimal or optimal solutions in a limited time by means of sequential decision-making. Finally, we compare the proposed D3QN-based solution with the standard baseline solution, and experiments show that it outperforms the baseline solution in terms of platform profit, task completion rate, etc., the utility and attractiveness of the platform are enhanced.
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Abbass, Waseem, Riaz Hussain, Jaroslav Frnda, Nasim Abbas, Muhammad Awais Javed, and Shahzad A. Malik. "Resource Allocation in Spectrum Access System Using Multi-Objective Optimization Methods." Sensors 22, no. 4 (February 9, 2022): 1318. http://dx.doi.org/10.3390/s22041318.
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The paradigm of dynamic shared access aims to provide flexible spectrum usage. Recently, Federal Communications Commission (FCC) has proposed a new dynamic spectrum management framework for the sharing of a 3.5 GHz (3550–3700 MHz) federal band, called a citizen broadband radio service (CBRS) band, which is governed by spectrum access system (SAS). It is the responsibility of SAS to manage the set of CBRS-SAS users. The set of users are classified in three tiers: incumbent access (IA) users, primary access license (PAL) users and the general authorized access (GAA) users. In this article, dynamic channel assignment algorithm for PAL and GAA users is designed with the goal of maximizing the transmission rate and minimizing the total cost of GAA users accessing PAL reserved channels. We proposed a new mathematical model based on multi-objective optimization for the selection of PAL operators and idle PAL reserved channels allocation to GAA users considering the diversity of PAL reserved channels’ attributes and the diversification of GAA users’ business needs. The proposed model is estimated and validated on various performance metrics through extensive simulations and compared with existing algorithms such as Hungarian algorithm, auction algorithm and Gale–Shapley algorithm. The proposed model results indicate that overall transmission rate, net cost and data-rate per unit cost remain the same in comparison to the classical Hungarian method and auction algorithm. However, the improved model solves the resource allocation problem approximately up to four times faster with better load management, which validates the efficiency of our model.
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Zayas-Cabán, Gabriel, and Hyun-Soo Ahn. "DYNAMIC CONTROL OF A SINGLE-SERVER SYSTEM WHEN JOBS CHANGE STATUS." Probability in the Engineering and Informational Sciences 32, no. 3 (June 7, 2017): 353–95. http://dx.doi.org/10.1017/s0269964817000213.
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From health care to maintenance shops, many systems must contend with allocating resources to customers or jobs whose initial service requirements or costs change when they wait too long. We present a new queueing model for this scenario and use a Markov decision process formulation to analyze assignment policies that minimize holding costs. We show that the classic cμ rule is generally not optimal when service or cost requirements can change. Even for a two-class customer model where a class 1 task becomes a class 2 task upon waiting, we show that additional orderings of the service rates are needed to ensure the optimality of simple priority rules. We then show that seemingly-intuitive switching curve structures are also not optimal in general. We study these scenarios and provide conditions under which they do hold. Lastly, we show that results from the two-class model do not extend to when there are n≥3 customer classes. More broadly, we find that simple priority rules are not optimal. We provide sufficient conditions under which a simple priority rule holds. In short, allowing service and/or cost requirements to change fundamentally changes the structure of the optimal policy for resource allocation in queueing systems.
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Behera, Sasmita Rani, Niranjan Panigrahi, Sourav Kumar Bhoi, Kshira Sagar Sahoo, N. Z. Jhanjhi, and Rania M. Ghoniem. "Time Series-Based Edge Resource Prediction and Parallel Optimal Task Allocation in Mobile Edge Computing Environment." Processes 11, no. 4 (March 27, 2023): 1017. http://dx.doi.org/10.3390/pr11041017.
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The offloading of computationally intensive tasks to edge servers is indispensable in the mobile edge computing (MEC) environment. Once the tasks are offloaded, the subsequent challenges lie in buffering them and assigning them to edge virtual machine (VM) resources to meet the multicriteria requirement. Furthermore, the edge resources’ availability is dynamic in nature and needs a joint prediction and optimal allocation for the efficient usage of resources and fulfillment of the tasks’ requirements. To this end, this work has three contributions. First, a delay sensitivity-based priority scheduling (DSPS) policy is presented to schedule the tasks as per their deadline. Secondly, based on exploratory data analysis and inferred seasonal patterns in the usage of edge CPU resources from the GWA-T-12 Bitbrains VM utilization dataset, the availability of VM resources is predicted by using a Holt–Winters-based univariate algorithm (HWVMR) and a vector autoregression-based multivariate algorithm (VARVMR). Finally, for optimal and fast task assignment, a parallel differential evolution-based task allocation (pDETA) strategy is proposed. The proposed algorithms are evaluated extensively with standard performance metrics, and the results show nearly 22%, 35%, and 69% improvements in cost and 41%, 52%, and 78% improvements in energy when compared with MTSS, DE, and min–min strategies, respectively.
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Si, JiaShuai, and MingRui Hao. "Online Weapon-target Assignment based on Distributed Auction Mechanism." Journal of Physics: Conference Series 2456, no. 1 (March 1, 2023): 012044. http://dx.doi.org/10.1088/1742-6596/2456/1/012044.
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Abstract To solve the problem of online weapon-target assignment (OWTA) in the integration of large-scale search and attack in unknown environment, an OWTA algorithm based on distributed auction mechanism is presented. Aiming at the problem that the traditional combinatorial optimization algorithm needs to set up the global battlefield situation in advance, considering the consumability of resources in the attack process, the integrated search and attack task flow is established. Considering the communication restricted environment, the unmanned aerial vehicles (uavs) are grouped, with centralized architecture within the group and distributed structure between the groups, and the corresponding distributed auction mechanism is constructed to achieve OWTA within the communication range limited. In order to solve the problem that it is difficult to ensure the time consistency of the coordinated attack target, a dubins cooperative path planning based on cooperative particle swarm optimization (CPSO) algorithm is proposed. Particle swarm optimization algorithm is used to adjust the radius of the dubins path of each bomb, so that the uav in the same group can hit the target simultaneously without collision and have the shortest flight range. The simulation results show that the designed distributed auction algorithm takes into account the consumption of attack resources, and quickly redistributes the firepower to the new targets in the dynamic uncertain environment, which ensures the maximization of the execution efficiency of the multi-machine cluster fire allocation task.
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Alkanhel, Reem, Ahsan Rafiq, Evgeny Mokrov, Abdukodir Khakimov, Mohammed Saleh Ali Muthanna, and Ammar Muthanna. "Enhanced Slime Mould Optimization with Deep-Learning-Based Resource Allocation in UAV-Enabled Wireless Networks." Sensors 23, no. 16 (August 10, 2023): 7083. http://dx.doi.org/10.3390/s23167083.
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Unmanned aerial vehicle (UAV) networks offer a wide range of applications in an overload situation, broadcasting and advertising, public safety, disaster management, etc. Providing robust communication services to mobile users (MUs) is a challenging task because of the dynamic characteristics of MUs. Resource allocation, including subchannels, transmit power, and serving users, is a critical transmission problem; further, it is also crucial to improve the coverage and energy efficacy of UAV-assisted transmission networks. This paper presents an Enhanced Slime Mould Optimization with Deep-Learning-based Resource Allocation Approach (ESMOML-RAA) in UAV-enabled wireless networks. The presented ESMOML-RAA technique aims to efficiently accomplish computationally and energy-effective decisions. In addition, the ESMOML-RAA technique considers a UAV as a learning agent with the formation of a resource assignment decision as an action and designs a reward function with the intention of the minimization of the weighted resource consumption. For resource allocation, the presented ESMOML-RAA technique employs a highly parallelized long short-term memory (HP-LSTM) model with an ESMO algorithm as a hyperparameter optimizer. Using the ESMO algorithm helps properly tune the hyperparameters related to the HP-LSTM model. The performance validation of the ESMOML-RAA technique is tested using a series of simulations. This comparison study reports the enhanced performance of the ESMOML-RAA technique over other ML models.
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D. Suresh, S. Satheesbabu, P. Gokulakrishnan,. "ONLINE PURCHASING PLATFORM USING CROWD SOURCING WITH IMPROVISATION OF CLASSIFICATION ACCURACY." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 17, 2021): 1123–34. http://dx.doi.org/10.17762/itii.v9i1.246.
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Crowd-sourcing is a prototype where persons cum organisations acquire facts such as ideas, micro-tasks, financial, vote casting associated to items and offerings from individuals of large, open and rapidly-evolving nature. It entails utilization of web acquired and distribute work between members to get a collective result. The software of classification tasks in crowd-sourcing is a counter step due to the inclined reputation of crowd-sourcing market. Dynamic Label Acquisition and Answer Aggregation (DLTA) crowd-sourcing framework accomplishes the classification assignment in a promising manner. But most of the current works are now not in a position to supply an budget allocation for labels due to the fact they do not make the most the Label inference and acquisition phase. In addition, label mismatch and multi-label tasks are the different issues encountered in the current works. To overcome, it is proposed to undertake Random Forest Algorithm (RFA) for classification in crowd-sourcing. The goal of this work is to enhance the crowd-sourcing classification task efficiency with Dynamic Resource Algorithm. RFA is activated by means of developing a multitude of decision tree at training time and consequences with the training and it applies a bagging approach to produce the last end result with more accuracy.
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Wang, Wenfei, Maolong Lv, Le Ru, Bo Lu, Shiguang Hu, and Xinlong Chang. "Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases." Aerospace 9, no. 9 (September 1, 2022): 491. http://dx.doi.org/10.3390/aerospace9090491.
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Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model that considers the constraints of task execution, time, and target task execution demand. Based on an improved consensus-based bundle algorithm (CBBA), we propose a two-tier task bidding mechanism. According to dynamic changes in new tasks, we study a dynamic assignment strategy and propose a mechanism based on task continuity adjustment and time windows. Finally, a simulation experiment is used to verify the feasibility and effectiveness of the proposed allocation method in multi-UAV target assignment scenarios. The results show that the dynamic task assignment strategy can efficiently assign random new tasks as they arise.
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He, Jianhua, Siqi Tao, Yang Deng, Libin Chen, and Zhiying Mou. "Research on Multi-Sensor Resource Dynamic Allocation Auction Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 2 (April 2019): 330–36. http://dx.doi.org/10.1051/jnwpu/20193720330.
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This paper designs a multi-sensor resource dynamic allocation method based on auction algorithm. Tasks are prioritized according to the needs of the engineering field. Task priority is used as the basis for multi-sensor resource allocation order, taking into account the target's threat value and information needs. The sensor and task pairing function is established and used to measure the sensor resource dynamic allocation, we also use Analytic Hierarchy Process to determine the weight of each performance parameter in the pairing function (such as detection probability, intercept probability, positioning accuracy, tracking accuracy, recognition probability, etc.). The auction algorithm is improved by adding resource dynamic allocation constraints, which not only ensures the continuous execution of the target task, but also improves the dynamic allocation efficiency of multi-sensor resources. The simulation results show that the allocation method in this paper is scientific and reasonable.
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BURDETT, ROBERT L., and ERHAN KOZAN. "THE ASSIGNMENT OF INDIVIDUAL RENEWABLE RESOURCES IN SCHEDULING." Asia-Pacific Journal of Operational Research 21, no. 03 (September 2004): 355–77. http://dx.doi.org/10.1142/s021759590400028x.
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Resource constrained scheduling problems are concerned with the allocation of limited resources to tasks over time. The solution to these problems is often a sequence, resource allocation, and schedule. When human workers are incorporated as a renewable resource, the allocation is defined as the number of workers assigned to perform each task. In practice, however, this solution does not adequately address how individual workers are to be assigned to tasks. This paper, therefore, provides mathematical models and heuristic techniques for solving this multi-period precedence constrained assignment problem. Results of a significant numerical investigation are also presented.
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Fazal, Nayyer, Muhammad Tahir Khan, Shahzad Anwar, Javaid Iqbal, and Shahbaz Khan. "Task allocation in multi-robot system using resource sharing with dynamic threshold approach." PLOS ONE 17, no. 5 (May 4, 2022): e0267982. http://dx.doi.org/10.1371/journal.pone.0267982.
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Task allocation is a fundamental requirement for multi-robot systems working in dynamic environments. An efficient task allocation algorithm allows the robots to adjust their behavior in response to environmental changes such as fault occurrences, or other robots’ actions to increase overall system performance. To address these challenges, this paper presents a Task Allocation technique based on a threshold level which is an accumulative value aggregated by a centralized unit using the Task-Robot ratio and the number of the available resource in the system. The threshold level serves as a reference for task acceptance and the task acceptance occurs despite resource shortage. The deficient resources for the accepted task are acquired through an auction process using objective minimization. Despite resource shortage, task acceptance occurs. The threshold approach and the objective minimization in the auction process reduce the overall completion time and increase the system’s resource utilization up to 96%, which is demonstrated theoretically and validated through simulations and real experimentation.
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Zhang, Jiarui, Gang Wang, and Yafei Song. "Task Assignment of the Improved Contract Net Protocol under a Multi-Agent System." Algorithms 12, no. 4 (April 1, 2019): 70. http://dx.doi.org/10.3390/a12040070.
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Background: The existing contract net protocol has low overall efficiency during the bidding and release period, and a large amount of redundant information is generated during the negotiation process. Methods: On the basis of an ant colony algorithm, the dynamic response threshold model and the flow of pheromone model were established, then the complete task allocation process was designed. Three experimental settings were simulated under different conditions. Results: When the number of agents was 20 and the maximum load value was L max = 3 , the traffic and run-time of task allocation under the improved contract net protocol decreased. When the number of tasks and L max was fixed, the improved contract net protocol had advantages over the dynamic contract net and classical contract net protocols in terms of both traffic and run-time. Setting up the number of agents, tasks and L max to improve the task allocation under the contract net not only minimizes the number of errors, but also the task completion rate reaches 100%. Conclusions: The improved contract net protocol can reduce the traffic and run-time compared with classical contract net and dynamic contract net protocols. Furthermore, the algorithm can achieve better assignment results and can re-forward all erroneous tasks.
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Xu, Shufang, Linlin Li, Ziyun Zhou, Yingchi Mao, and Jianxin Huang. "A Task Allocation Strategy of the UAV Swarm Based on Multi-Discrete Wolf Pack Algorithm." Applied Sciences 12, no. 3 (January 26, 2022): 1331. http://dx.doi.org/10.3390/app12031331.
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With the continuous development of artificial intelligence, swarm control and other technologies, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment.
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Yang, Qingwei, Libing Jiang, Shuyu Zheng, Yingjian Zhao, and Zhuang Wang. "Joint Power and Bandwidth Allocation with RCS Fluctuation Characteristic for Space Target Tracking." Remote Sensing 15, no. 16 (August 10, 2023): 3971. http://dx.doi.org/10.3390/rs15163971.
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Reasonable allocation of space-based radar resources is a crucial aspect of improving the accuracy of space multi-target tracking and enhancing spatial awareness. The conventional resource allocation algorithm fails to exploit the high dynamic radar cross-section (RCS) characteristics, resulting in poor tracking robustness, tracking divergence, or even loss of tracking. However, the RCS of space targets fluctuates considerably in actual tracking scenarios, which cannot be disregarded for space target tracking tasks. To address this issue, we propose an adaptive allocation method that considers the dynamic RCS fluctuation characteristic for space-based radar tracking assignments. The proposed method exploits the predictable orbital information of space target to calculate the real-time observation angle of radar, and then obtains the multi-target dynamic RCS through the target RCS dataset. By combining the obtained RCS sequence, radar power, and bandwidth, an optimal model for radar tracking accuracy is established based on the multi-target posterior Cramér–Rao lower bound (PCRLB) to evaluate the tracking performance. By resolving the aforementioned multivariance optimization problem, we eventually acquire the results of power and bandwidth pre-allocation for tracking multiple space targets. Simulation results validate that, compared with the traditional methods, the proposed joint dynamic RCS power and bandwidth allocation (JRPBA) method can achieve superior tracking accuracy and minimize instances of missed tracking.
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Wu, Xiaojun, Zhiyuan Gao, Sheng Yuan, Qiao Hu, and Zerui Dang. "A Dynamic Task Allocation Algorithm for Heterogeneous UUV Swarms." Sensors 22, no. 6 (March 9, 2022): 2122. http://dx.doi.org/10.3390/s22062122.
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Aiming at the task allocation problem of heterogeneous unmanned underwater vehicle (UUV) swarms, this paper proposes a dynamic extended consensus-based bundle algorithm (DECBBA) based on consistency algorithm. Our algorithm considers the multi-UUV task allocation problem that each UUV can individually complete multiple tasks, constructs a “UUV-task” matching matrix and designs new marginal utility, reward and cost functions for the influence of time, path and UUV voyage. Furthermore, in view of the unfavorable factors that restrict the underwater acoustic communication range between UUVs in the real environment, our algorithm complete dynamic task allocation of UUV swarms with optimization in load balance indicator by the update of the UUV individual and the task completion status in the discrete time stage. The performance indicators (including global utility and task completion rate) of the dynamic task allocation algorithm in the scenario with communication constraints can be well close to the static algorithm in the ideal scenario without communication constraints. The simulation experiment results show that the algorithm proposed in this paper can quickly and efficiently obtain the dynamic and conflict-free task allocation assignment of UUV swarms with great performance.
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Chilton, Michael A. "Resource Allocation and Planning in Single and Multi-Project Environments." International Journal of Social and Organizational Dynamics in IT 5, no. 1 (January 2016): 16–33. http://dx.doi.org/10.4018/ijsodit.2016010102.
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IT projects often fail to be completed on time, on budget, within scope, with the required functionality and error free. The reasons for project failure are numerous and have been well studied and identified in the literature; however, most studies assume that project managers are completely devoted to the execution of the project and disregard any time wasting efforts that keep them from these duties. One such duty is the assignment and tracking of personnel to tasks within a project. As workers become over-allocated, the PM must redirect his or her efforts to balancing the overall schedule. This task is often performed manually causing PMs to spend much of their time attending to the details of the schedule instead of dealing with the issues critical to the project. The author describes here software that optimizes the schedule when resources are limited and describe its use in both single and multiple project environments.
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Wang, Bo, and Mingchu Li. "Resource Allocation Scheduling Algorithm Based on Incomplete Information Dynamic Game for Edge Computing." International Journal of Web Services Research 18, no. 2 (April 2021): 1–24. http://dx.doi.org/10.4018/ijwsr.2021040101.
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With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency, energy consumption, smart device mobility, end-user power, and other issues. This paper proposes a dynamic multi-winner game model based on incomplete information to solve multi-end users' task offloading and edge resource allocation. First, based on the history of end-users storage in edge data centers, a hidden Markov model can predict other end-users' bid prices at time t. Based on these predicted auction prices, the model determines their bids. A dynamic multi-winner game model is used to solve the offload strategy that minimizes latency, energy consumption, cost, and to maximizes end-user satisfaction at the edge data center. Finally, the authors designed a resource allocation algorithm based on different priorities and task types to implement resource allocation in edge data centers. To ensure the prediction model's accuracy, the authors also use the expectation-maximization algorithm to learn the model parameters. Comparative experimental results show that the proposed model can better results in time delay, energy consumption, and cost.
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Zhu, Pengxing, and Xi Fang. "Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning." Symmetry 13, no. 12 (December 14, 2021): 2417. http://dx.doi.org/10.3390/sym13122417.
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Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to develop a reasonable allocation plan for the tasks. Therefore, this paper constructs a heterogeneous UAV multitask assignment model based on several realistic constraints and proposes an improved half-random Q-learning (HR Q-learning) algorithm. The algorithm is based on the Q-learning algorithm under reinforcement learning, and by changing the way the Q-learning algorithm selects the next action in the process of random exploration, the probability of obtaining an invalid action in the random case is reduced, and the exploration efficiency is improved, thus increasing the possibility of obtaining a better assignment scheme, this also ensures symmetry and synergy in the distribution process of the drones. Simulation experiments show that compared with Q-learning algorithm and other heuristic algorithms, HR Q-learning algorithm can improve the performance of task execution, including the ability to improve the rationality of task assignment, increasing the value of gains by 12.12%, this is equivalent to an average of one drone per mission saved, and higher success rate of task execution. This improvement provides a meaningful attempt for UAV task assignment.
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Yarahmadi, Hossein, Mohammad Ebrahim Shiri, Moharram Challenger, Hamidreza Navidi, and Arash Sharifi. "Multi-Agent Credit Assignment and Bankruptcy Game for Improving Resource Allocation in Smart Cities." Sensors 23, no. 4 (February 6, 2023): 1804. http://dx.doi.org/10.3390/s23041804.
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In recent years, the development of smart cities has accelerated. There are several issues to handle in smart cities, one of the most important of which is efficient resource allocation. For the modeling of smart cities, multi-agent systems (MASs) can be used. In this paper, an efficient approach is proposed for resource allocation in smart cities based on the multi-agent credit assignment problem (MCA) and bankruptcy game. To this end, the resource allocation problem is mapped to MCA and the bankruptcy game. To solve this problem, first, a task start threshold (TST) constraint is introduced. The MCA turns into a bankruptcy problem upon introducing such a constraint. Therefore, based on the concept of bankruptcy, three methods of TS-Only, TS + MAS, and TS + ExAg are presented to solve the MCA. In addition, this work introduces a multi-score problem (MSP) in which a different reward is offered for solving each part of the problem, and we used it in our experiments to examine the proposed methods. The proposed approach is evaluated based on the learning rate, confidence, expertness, efficiency, certainty, and correctness parameters. The results reveal the better performance of the proposed approach compared to the existing methods in five parameters.
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Wang, Pengfei, Zijie Zheng, Boya Di, and Lingyang Song. "HetMEC: Latency-Optimal Task Assignment and Resource Allocation for Heterogeneous Multi-Layer Mobile Edge Computing." IEEE Transactions on Wireless Communications 18, no. 10 (October 2019): 4942–56. http://dx.doi.org/10.1109/twc.2019.2931315.
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Schillinger, Philipp, Mathias Bürger, and Dimos V. Dimarogonas. "Simultaneous task allocation and planning for temporal logic goals in heterogeneous multi-robot systems." International Journal of Robotics Research 37, no. 7 (May 23, 2018): 818–38. http://dx.doi.org/10.1177/0278364918774135.
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This paper describes a framework for automatically generating optimal action-level behavior for a team of robots based on temporal logic mission specifications under resource constraints. The proposed approach optimally allocates separable tasks to available robots, without requiring a priori an explicit representation of the tasks or the computation of all task execution costs. Instead, we propose an approach for identifying sub-tasks in an automaton representation of the mission specification and for simultaneously allocating the tasks and planning their execution. The proposed framework avoids the need to compute a combinatorial number of possible assignment costs, where each computation itself requires solving a complex planning problem. This can improve computational efficiency compared with classical assignment solutions, in particular for on-demand missions where task costs are unknown in advance. We demonstrate the applicability of the approach with multiple robots in an existing office environment and evaluate its performance in several case study scenarios.
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Han, Yueming, Fei Han, and Darong Xian. "Research on multi-Unmanned aerial vehicle joint delivery mission assignment based on multiple Alliance." Frontiers in Computing and Intelligent Systems 3, no. 1 (March 17, 2023): 82–84. http://dx.doi.org/10.54097/fcis.v3i1.6029.
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In order to improve the battlefield delivery ability, for the battlefield joint delivery process task is heavy, time, traditional iron, public, water and other vulnerable to terrain, transportation conditions lead to low delivery efficiency, large-scale delivery mission capacity, the paper considers to use drones joint delivery task, according to the problem, summarize inspired rules, design fast construction model algorithm, using dynamic comprehensive sorting method, improve the task allocation and delivery efficiency, provide a reference for enriching the military diversified delivery mode, and improve the air delivery ability.
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Yakubu, Ismail Zaharaddeen, Lele Muhammed, Zainab Aliyu Musa, Zakari Idris Matinja, and Ilya Musa Adamu. "A Multi Agent Based Dynamic Resource Allocation in Fog-Cloud Computing Environment." Trends in Sciences 18, no. 22 (November 5, 2021): 413. http://dx.doi.org/10.48048/tis.2021.413.
Full textAbstract:
Cloud high latency limitation has necessitated the introduction of Fog computing paradigm that extends computing infrastructures in the cloud data centers to the edge network. Extended cloud resources provide processing, storage and network services to time sensitive request associated to the Internet of Things (IoT) services in network edge. The rapid increase in adoption of IoT devices, variations in user requirements, limited processing and storage capacity of fog resources and problem of fog resources over saturation has made provisioning and allotment of computing resources in fog environment a formidable task. Satisfying application and request deadline is the most substantial challenge compared to other dynamic variations in parameters of client requirements. To curtail these issues, the integrated fog-cloud computing environment and efficient resource selection method is highly required. This paper proposed an agent based dynamic resource allocation that employs the use of host agent to analyze the QoSrequirements of application and request and select a suitable execution layer. The host agent forwards the application request to a layer agent which is responsible for the allocation of best resource that satisfies the requirement of the application request. Host agent and layers agents maintains resource information tables for matching of task and computing resources. CloudSim toolkit functionalities were extended to simulate a realistic fog environment where the proposed method is evaluated. The experimental results proved that the proposed method performs better in terms of processing time, latency and percentage QoS delivery.
HIGHLIGHTS
The distance between the cloud infrastructure and the edge IoT devices makes the cloud not too competent for some IoT applications, especially the sensitive ones
To minimize the latency in the cloud and ensure prompt response to user requests, Fog computing, which extends the cloud services to edge network was introduced
The proliferation in adoption of IoT devices and fog resource limitations has made resource scheduling in fog computing a tedious one
GRAPHICAL ABSTRACT
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Liu, Ziwei, Changzhen Qiu, and Zhiyong Zhang. "Sequence-to-Sequence Multi-Agent Reinforcement Learning for Multi-UAV Task Planning in 3D Dynamic Environment." Applied Sciences 12, no. 23 (November 28, 2022): 12181. http://dx.doi.org/10.3390/app122312181.
Full textAbstract:
Task planning involving multiple unmanned aerial vehicles (UAVs) is one of the main research topics in the field of cooperative unmanned aerial vehicle control systems. This is a complex optimization problem where task allocation and path planning are dealt with separately. However, the recalculation of optimal results is too slow for real-time operations in dynamic environments due to a large amount of computation required, and traditional algorithms are difficult to handle scenarios of varying scales. Meanwhile, the traditional approach confines task planning to a 2D environment, which deviates from the real world. In this paper, we design a 3D dynamic environment and propose a method for task planning based on sequence-to-sequence multi-agent deep deterministic policy gradient (SMADDPG) algorithm. First, we construct the task-planning problem as a multi-agent system based on the Markov decision process. Then, the DDPG is combined sequence-to-sequence to learn the system to solve task assignment and path planning simultaneously according to the corresponding reward function. We compare our approach with the traditional reinforcement learning algorithm in this system. The simulation results show that our approach satisfies the task-planning requirements and can accomplish tasks more efficiently in competitive as well as cooperative scenarios with dynamic or constant scales.
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Kopeikin, Andrew N., Sameera S. Ponda, Luke B. Johnson, and Jonathan P. How. "Dynamic Mission Planning for Communication Control in Multiple Unmanned Aircraft Teams." Unmanned Systems 01, no. 01 (June 20, 2013): 41–58. http://dx.doi.org/10.1142/s2301385013500039.
Full textAbstract:
A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control network communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. This work builds upon a distributed algorithm previously developed by the authors, CBBA with Relays, which uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes, the team is able to optimize the use of agents to address the needs of dynamic complex missions. In this work, the algorithm is extended to explicitly consider realistic network communication dynamics, including path loss, stochastic fading, and information routing. Simulation and flight test results validate the proposed approach, demonstrating that the algorithm ensures both data-rate and interconnectivity bit-error-rate requirements during task execution.
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Zhenjiang Zhang, Zhenjiang Zhang, Quancheng Zhao Zhenjiang Zhang, Xuan Zhao Quancheng Zhao, Wei Lin Xuan Zhao, and Shuai Wu Wei Lin. "Research on High Confidence Resource Allocation Technology in Edge Computing." 電腦學刊 32, no. 5 (October 2021): 192–202. http://dx.doi.org/10.53106/199115992021103205016.
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This paper studies resource allocation and security performance. Aiming at the problem of privacy disclosure caused by eavesdropping during task unloading in single cell and multi user scenarios, the physical layer security technology is used to formulate corresponding confidentiality measures. Facing the dynamic change of the wireless channel state of the system and the computing power required by users, taking reducing the average processing delay of tasks as the optimization goal, the multi-user task partial migration problem is modeled as a joint optimization problem of computing resources and power resources under the constraints of security set-tings and energy constraints, and the system is established as a Markov decision process model, and proposes a resource allocation algorithm based on physical layer security and depth deterministic policy gradient. Simulation results show that the algorithm can approach the optimal performance, has adaptability and lower computational complexity, can make better unloading strategy and resource allocation scheme in trusted environment, effectively reduce the average task processing delay and improve the robustness of the system.
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Guo, Wenzhong, Ying Chen, and Guolong Chen. "Dynamic Task Scheduling Strategy with Game Theory in Wireless Sensor Networks." New Mathematics and Natural Computation 10, no. 03 (August 21, 2014): 211–24. http://dx.doi.org/10.1142/s1793005714500124.
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Task allocation and scheduling is an important typical problem in the area of high performance computing. Unfortunately, the existing traditional solutions to this problem in high performance computing cannot be directly implemented in wireless sensor networks (WSNs) due to the limitations of WSNs such as resource availability and shared communication medium. In this paper, a dynamic task scheduling strategy with the application of the game theory in WSNs is presented. First, an effective parallel alliance generating algorithm is proposed to process the multi-tasks environment. A task allocation algorithm based on the game theory is used to enhance the performance of the network. A novel resource conflict eliminating algorithm is also developed to eliminate the conflicting issues. Finally, the simulation results confirm and reassure the effectiveness of our proposed scheme as we compare with that of the other schema's available in the public domain.
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Danino, Tom, Yehuda Ben-Shimol, and Shlomo Greenberg. "Container Allocation in Cloud Environment Using Multi-Agent Deep Reinforcement Learning." Electronics 12, no. 12 (June 9, 2023): 2614. http://dx.doi.org/10.3390/electronics12122614.
Full textAbstract:
Nowadays, many computation tasks are carried out using cloud computing services and virtualization technology. The intensive resource requirements of virtual machines have led to the adoption of a lighter solution based on containers. Containers isolate packaged applications and their dependencies, and they can also operate as part of distributed applications. Containers can be distributed over a cluster of computers with available resources, such as the CPU, memory, and communication bandwidth. Any container distribution mechanism should consider resource availability and their impact on overall performance. This work suggests a new approach to assigning containers to servers in the cloud, while meeting computing and communication resource requirements and minimizing the overall task completion time. We introduce a multi-agent environment using a deep reinforcement learning-based decision mechanism. The high action space complexity is tackled by decentralizing the allocation decisions among multiple agents. Considering the interactions among the agents, we introduce a new cooperative mechanism for a state and reward design, resulting in efficient container assignments. The performances of both long short term memory (LSTM) and memory augmented-based agents are examined, for solving the challenging container assignment problem. Experimental results demonstrated an improvement of up to 28% in the execution runtime compared to existing bin-packing heuristics and the common Kubernetes industrial tool.
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Zheng, Zhuoling, HaoYu Gong, RuoBing Duan, XinYu Qiu, and Jiajun Zou. "Design of multi-robot collaborative navigation and control system based on ROS and laser SLAM." Journal of Physics: Conference Series 2284, no. 1 (June 1, 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2284/1/012008.
Full textAbstract:
Abstract Through in-depth research in the current robot field, a design scheme of cooperative navigation and control system for multiple robots based on ROS and laser SLAM is proposed. Compared with a single robot, multi-robot cooperation can improve the operation range and efficiency, and is suitable for more diversified scenarios. The system design includes multi-machine mapping, multi-machine global and local path planning, multi-machine navigation, real-time obstacle avoidance, multi-machine dynamic task assignment. Through the centralized control structure, the robot collects the lidar data in real time to build the map in real time and realizes the integration of multi-machine map building. Then, the path planning algorithm is called to plan the path. Combined with the dynamic task allocation algorithm, the efficiency of multi-robot cooperative operation is maximized and the execution experience is provided for the implementation of other similar projects.
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Fu, Shuang, and Dailin Jiang. "Multi-Dimensional Resource Allocation for throughput Maximization in CRIoT with SWIPT." Energies 16, no. 12 (June 16, 2023): 4767. http://dx.doi.org/10.3390/en16124767.
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To solve the power supply problem of battery-limited Internet of Things devices (IoDs) and the spectrum scarcity problem, simultaneous wireless information and power transfer (SWIPT) and cognitive radio(CR) technology were integrated into the Internet of Things (IoT) network to build a cognitive radio IoT (CRIoT) with SWIPT. In this network, secondary users (SUs) could adaptively switch between spectrum sensing, SWIPT, and information transmission to improve the total throughput. To solve the complicated multi-dimensional resource allocation problem in CRIoT with SWIPT, we propose a multi-dimensional resource allocation algorithm for maximizing the total throughput. Three-dimensional resources were jointly optimized, which are time resource (the duration of each process), power resource (the transmit power and the power splitting ratio of each node), and spectrum resource, under some constraints, such as maximum transmit power constraint and maximum permissible interference constraint. To solve this intractable mixed-integer nonlinear program (MINLP) problem, firstly, the sensing task assignment for cooperative spectrum sensing (CSS) was obtained by using a greedy sensing algorithm. Secondly, the original problem was transformed into a convex problem via some transformations with fixed-power splitting ratio and time switching. The Lagrange dual method and subgradient method were adopted to obtain the optimal power and channel allocation. Then, a one-dimensional search algorithm was used to obtain the optimal power splitting ratio and the time switching ratio. Finally, a heuristic algorithm was adopted to obtain the optimal sensing duration. The simulation results show that the proposed algorithm can achieve higher total system throughput than other benchmark algorithms, such as a greedy algorithm, an average algorithm, and the Kuhn–Munkres (KM) algorithm.
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Kalimuthu, Raj Kumar, and Brindha Thomas. "An effective multi-objective task scheduling and resource optimization in cloud environment using hybridized metaheuristic algorithm." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 4051–63. http://dx.doi.org/10.3233/jifs-212370.
Full textAbstract:
In today’s world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources are considered to be dynamic and the demands necessitated for resource allocation for a certain task are different. These diverse factors may cause load and power imbalance which also affect the resource utilization and task scheduling in the cloud-based IoT environment. Recently, a bio-inspired algorithm can work effectually to solve task scheduling problems in the cloud-based IoT system. Therefore, this work focuses on efficient task scheduling and resource allocation through a novel Hybrid Bio-Inspired algorithm with the hybridized of Improvised Particle Swarm Optimization and Ant Colony Optimization. The vital objective of hybridizing these two approaches is to determine the nearest multiple sources to attain discrete and continuous solutions. Here, the task has been allocated to the virtual machine through a particle swarm and continuous resource management can be carried out by an ant colony. The performance of the proposed approach has been evaluated using the CloudSim simulator. The simulation results manifest that the proposed Hybridized algorithm efficiently scheduling the task in the cloud-based IoT environment with a lesser average response time of 2.18 sec and average waiting time of 3.6 sec as compared with existing state-of-the-art algorithms.
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Srivastava, Ankita, and Narander Kumar. "Multi-Objective Binary Whale Optimization-Based Virtual Machine Allocation in Cloud Environments." International Journal of Swarm Intelligence Research 14, no. 1 (February 3, 2023): 1–23. http://dx.doi.org/10.4018/ijsir.317111.
Full textAbstract:
With the rising demands for the services provided by cloud computing, virtual machine allocation (VMA) has become a tedious task due to the dynamic nature of the cloud. Millions of virtual machines (VMs) are allocated and de-allocated at every instant, so an efficient VMA has been a significant concern to enhance resource utilization and depreciate its wastage. Encouraged by the prodigious performance of the nature-inspired algorithm, the binary whale optimization approach has been eventuated to get to grips with the VMA issue with the focus on minimizing the resource waste and volume of servers working actively. The deliberate approach's accomplishment is assessed against the literature's well-known algorithms for VMA issues. The comparison results showed that the least resource wastage fitness of 15.68, minimum active servers of 216, and effective CPU and memory utilization of 88.31% and 88.79%, respectively, have been achieved.
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Shen, Yu, and Hecheng Li. "A new differential evolution using a bilevel optimization model for solving generalized multi-point dynamic aggregation problems." Mathematical Biosciences and Engineering 20, no. 8 (2023): 13754–76. http://dx.doi.org/10.3934/mbe.2023612.
Full textAbstract:
<abstract><p>The multi-point dynamic aggregation problem (MPDAP) comes mainly from real-world applications, which is characterized by dynamic task assignation and routing optimization with limited resources. Due to the dynamic allocation of tasks, more than one optimization objective, limited resources, and other factors involved, the computational complexity of both route programming and resource allocation optimization is a growing problem. In this manuscript, a task scheduling problem of fire-fighting robots is investigated and solved, and serves as a representative multi-point dynamic aggregation problem. First, in terms of two optimized objectives, the cost and completion time, a new bilevel programming model is presented, in which the task cost is taken as the leader's objective. In addition, in order to effectively solve the bilevel model, a differential evolution is developed based on a new matrix coding scheme. Moreover, some percentage of high-quality solutions are applied in mutation and selection operations, which helps to generate potentially better solutions and keep them into the next generation of population. Finally, the experimental results show that the proposed algorithm is feasible and effective in dealing with the multi-point dynamic aggregation problem.</p></abstract>
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Muthulakshmi, B., and K. Somasundaram. "Multi-Agent -Master Resource Finding Engine for Fast and Efficient Dynamic Resource Finding in Cloud Computing." International Journal of Engineering & Technology 7, no. 3.34 (September 1, 2018): 206. http://dx.doi.org/10.14419/ijet.v7i3.34.18965.
Full textAbstract:
Resource finding is an enormous and tedious task in the cloud environment. There are various protocols suggested in existing approach to deal the resource finding. But from job scheduling to resource identification none of the approaches is available for best. It leads to a tailoring of functionalities in different phases like job scheduling, resource finding and resource identification. This paper analyses the automation from the job scheduling phase to resource allocation phase which includes, semantic model, optimization, Master Resource Finding Engine (MRFE). A decision-making mechanism called adjudicator is a control unit which collects input from all the above-said phase models and provides the best and efficient resource to the requirement. In this paper, a new model is provisioned to attain all the phases explained above and compared with resource simulations. The results were analyzed in terms of quality of resource, performance, resources acquired, patterns considered for semantic finding, interactive jobs, a workload of resources in normal and peak time
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Chen, Yu. "MEC Network Resource Allocation Strategy Based on Improved PSO in 5G Communication Network." International Journal on Semantic Web and Information Systems 19, no. 1 (August 18, 2023): 1–17. http://dx.doi.org/10.4018/ijswis.328526.
Full textAbstract:
Relying on features such as high-speed, low latency, support for cutting-edge technology, internet of things, and multimodality, 5G networks will greatly contribute to the transformation of Web 3.0. In order to realize low-latency and high-speed information exchange in 5G communication networks, a method based on the allocation of network computing resource in view of edge computing model is proposed. The method first considers three computing modes: local device computing, local mobile edge computing (MEC) server computing, and adjacent MEC server computing. Then, a multi-scenario edge computing model is further constructed for optimizing energy consumption and delay. At the same time, the encoding-decoding mode is used to optimize PSO algorithm and combined with the improvement of fitness function, which can effectively support the communication network to achieve reasonable allocation of resources, ensuring efficiency of information exchange in the network. In the end, the results show that when the number of users is 500, the method can complete the task assignment within 44s.
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Sun, Peng, Tian Yun Shi, and Wei Jiao Zhang. "Optimization on Resource-Constrained Multi-Project Scheduling of Electric Multiple Unit Overhaul." Advanced Materials Research 748 (August 2013): 457–62. http://dx.doi.org/10.4028/www.scientific.net/amr.748.457.
Full textAbstract:
As the numbers and running distance of Chinese high-speed trains increase, many electric multiple units (EMU) gradually enter into overhaul stage, EMU maintenance bases face challenges of transition from practical exploration, regular production to lean production. In accordance of business requirement, built a model of dynamic resource allocation, task splitting, and soft precedence constraints. By the design of nonlinear decline inertia weight factor, a refined particle swarm optimization (PSO), as well as the corresponding parallel transformation scheme from particle to schedule, is presented. Finally, computational analysis is performed to validate the model and algorithm on optimization capabilities, resource utilization and performance.
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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.
Full textAbstract:
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.
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Pham, Xuan-Qui, Tien-Dung Nguyen, VanDung Nguyen, and Eui-Nam Huh. "Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing." Symmetry 11, no. 1 (January 7, 2019): 58. http://dx.doi.org/10.3390/sym11010058.
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The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.
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Zhuang, Zhen Sheng. "Research on Simulation Analysis of Elimination Conflicts in Large Financial System." Applied Mechanics and Materials 687-691 (November 2014): 4890–93. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4890.
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Data of financial system traffic randomness large, the data is not fixed to a single channel by multiple users is difficult to form a unified task scheduling rules. In this paper, we put forward a multi-user flexible financial system task scheduling algorithm based on decision distance. The algorithm adopted decision distance can get the data of dynamic relation matrix, and use dynamic feedback scheduling method to control the matrix, in order to ensure the rationality of the task allocation and reduced the probability of task conflict in the system. The dynamic feedback scheduling decisions distance can analyze data from the use of case decisions using the resource nodes, get the dynamic relationship between data matrix, the matrix is analyzed to extract maximum data connection group with no maximum data connections correlation data, which will be filtered with a smaller maximum data associated with the connection data.
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Potluri, Sirisha, and Katta Subba Rao. "Optimization model for QoS based task scheduling in cloud computing environment." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 1081. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp1081-1088.
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Shortest job first task scheduling algorithm allocates task based on the length of the task, i.e the task that will have small execution time will be scheduled first and the longer tasks will be executed later based on system availability. Min- Min algorithm will schedule short tasks parallel and long tasks will follow them. Short tasks will be executed until the system is free to schedule and execute longer tasks. Task Particle optimization model can be used for allocating the tasks in the network of cloud computing network by applying Quality of Service (QoS) to satisfy user’s needs. The tasks are categorized into different groups. Every one group contains the tasks with attributes (types of users and tasks, size and latency of the task). Once the task is allocated to a particular group, scheduler starts assigning these tasks to accessible services. The proposed optimization model includes Resource and load balancing Optimization, Non-linear objective function, Resource allocation model, Queuing Cost Model, Cloud cost estimation model and Task Particle optimization model for task scheduling in cloud computing environement. The main objectives identified are as follows. To propose an efficient task scheduling algorithm which maps the tasks to resources by using a dynamic load based distributed queue for dependent tasks so as to reduce cost, execution and tardiness time and to improve resource utilization and fault tolerance. To develop a multi-objective optimization based VM consolidation technique by considering the precedence of tasks, load balancing and fault tolerance and to aim for efficient resource allocation and performance of data center operations. To achieve a better migration performance model to efficiently model the requirements of memory, networking and task scheduling. To propose a QoS based resource allocation model using fitness function to optimize execution cost, execution time, energy consumption and task rejection ratio and to increase the throughput. QoS parameters such as reliability, availability, degree of imbalance, performance and SLA violation and response time for cloud services can be used to deliver better cloud services.
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Hu, Yanjuan, Ziyu Zhang, Jinwu Wang, Zhanli Wang, and Hongliang Liu. "Task Decomposition Based on Cloud Manufacturing Platform." Symmetry 13, no. 8 (July 21, 2021): 1311. http://dx.doi.org/10.3390/sym13081311.
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As a new service-oriented manufacturing paradigm, cloud manufacturing (CMfg) realizes the optimal allocation of resources in the product manufacturing process through the network. Task decomposition is a key problem of the CMfg system for resource scheduling. A high-quality task decomposition method can shorten product development time, reduce costs for resource service providers, and provide technical support for the application of CMfg. However, a cloud manufacturing system has to manage the allocation the correct amount of manufacturing resources, complex production processes, and highly dynamic production environments. At the same time, the tasks issued by service demanders are usually asymmetric and tightly coupled. We solve the complex task decomposition problem by using the traditional methods, that are hard to complete in CMfg. To overcome the shortcomings of CMfg, this paper proposed a task decomposition method based on the cloud platform. For achieving modular production, this approach creatively divides the product production process into four stages: design, manufacturing, transportation, and maintenance. Then a hybrid method, which combines with depth-first search algorithm, fast modular optimization algorithm, and artificial bee colony algorithm, is introduced. The method can obtain a multi-stage task optimization decomposition plan in CMfg. Simulation results demonstrate the proposed method can achieve complex task optimization decomposition in a CMfg environment.
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Liang, Kaibo, Li Zhou, Jianglong Yang, Huwei Liu, Yakun Li, Fengmei Jing, Man Shan, and Jin Yang. "Research on a Dynamic Task Update Assignment Strategy Based on a “Parts to Picker” Picking System." Mathematics 11, no. 7 (March 31, 2023): 1684. http://dx.doi.org/10.3390/math11071684.
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Order picking is a crucial operation in the storage industry, with a significant impact on storage efficiency and cost. Responding quickly to customer demands and shortening picking time is crucial given the random nature of order arrival times and quantities. This paper presents a study on the order-picking process in a distribution center, employing a “parts-to-picker” system, based on dynamic order batching and task optimization. Firstly, dynamic arriving orders with uncertain information are transformed into static picking orders with known information. A new method of the hybrid time window is proposed by combining fixed and variable time windows, and an order consolidation batch strategy is established with the aim of minimizing the number of target shelves for picking. A heuristic algorithm is designed to select a shelf selection model, taking into account the constraint condition that the goods on the shelf can meet the demand of the selection list. Subsequently, task division of multi-AGV is carried out on the shelf to be picked, and the matching between the target shelf and the AGVs, as well as the order of the AGVs to complete the task of picking, is determined. A scheduling strategy model is constructed to consider the task completion time as the incorporation of moving time, queuing time, and picking time, with the shortest task completion time as the objective function and AGV task selection as the decision variable. The improved ant colony algorithm is employed to solve the problem. The average response time of the order batching algorithm based on a hybrid time window is 4.87 s, showing an improvement of 22.20% and 40.2% compared to fixed and variable time windows, respectively. The convergence efficiency of the improved ant colony algorithm in AGV task allocation is improved four-fold, with a better convergence effect. By pre-selecting the nearest picking station for the AGVs, the multi-AGV picking system can increase the queuing time. Therefore, optimizing the static picking station selection and dynamically selecting the picking station queue based on the queuing situation are proposed. The Flexsim simulation results show that the queue-waiting and picking completion times are reduced to 34% of the original, thus improving the flexibility of the queuing process and enhancing picking efficiency.
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Sing, Ranumayee, Sourav Kumar Bhoi, Niranjan Panigrahi, Kshira Sagar Sahoo, Nz Jhanjhi, and Mohammed AlZain. "A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications." Electronics 11, no. 19 (October 6, 2022): 3207. http://dx.doi.org/10.3390/electronics11193207.
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Fog computing has been prioritized over cloud computing in terms of latency-sensitive Internet of Things (IoT) based services. We consider a limited resource-based fog system where real-time tasks with heterogeneous resource configurations are required to allocate within the execution deadline. Two modules are designed to handle the real-time continuous streaming tasks. The first module is task classification and buffering (TCB), which classifies the task heterogeneity using dynamic fuzzy c-means clustering and buffers into parallel virtual queues according to enhanced least laxity time. The second module is task offloading and optimal resource allocation (TOORA), which decides to offload the task either to cloud or fog and also optimally assigns the resources of fog nodes using the whale optimization algorithm, which provides high throughput. The simulation results of our proposed algorithm, called whale optimized resource allocation (WORA), is compared with results of other models, such as shortest job first (SJF), multi-objective monotone increasing sorting-based (MOMIS) algorithm, and Fuzzy Logic based Real-time Task Scheduling (FLRTS) algorithm. When 100 to 700 tasks are executed in 15 fog nodes, the results show that the WORA algorithm saves 10.3% of the average cost of MOMIS and 21.9% of the average cost of FLRTS. When comparing the energy consumption, WORA consumes 18.5% less than MOMIS and 30.8% less than FLRTS. The WORA also performed 6.4% better than MOMIS and 12.9% better than FLRTS in terms of makespan and 2.6% better than MOMIS and 4.3% better than FLRTS in terms of successful completion of tasks.
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R G, Umesh, Sushil Kumar G N, Santhosh K, Suraksha M S, and Dr Praveen Kumar K V. "Radio Resource Allocation for 5G Network Using Deep Reinforcement Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 677–83. http://dx.doi.org/10.22214/ijraset.2023.49468.
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Abstract: Resource allocation is a critical task in 5Gnetworks that determines how network resources are assigned to different devices and services. Traditional methods rely on predefined rules or heuristics, which may not always be optimal. Deep reinforcement learning (DRL)is a promising approach for radio resource allocation in 5Gnetworks as it can learn to optimize resource allocation based on feedback from the network. In DRL, an agent learns to make decisions based on rewards and penalties received from the environment. In radio resource allocation, the agent would learn to allocate resources, such as frequency bands and power levels, to different devices and services to maximize some performance metric, such asthroughput or energy efficiency. The main challenge in applying DRL to radio resource allocation is designing an appropriate reward function that incentivizes the agent to improve the performance metric while avoiding undesirable behavior. Additionally, the radio resource allocation problem is complex, requiring the agent to consider many variables and constraints, such as channel conditions, interference, and QoS requirements. To address this, researchers have proposed various techniques such as hierarchical RL, multi-agent RL, and curriculum learning. Despite the challenges, DRL has shown promising results inradio resource allocation for 5G networks. It has outperformed traditional methods in some scenarios, especially when network conditions are dynamic and unpredictable. However, further research is necessary to explore the scalability and robustness of DRL-based approaches in practical 5G networks. In this method we suggest an algorithm for voice and data carriers in sub-6 GHz and millimeter wave (mmWave) frequencies respectively. The mmWave ranges between 30GHz to 300GHz
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Yu, Liang, Qixin Guo, Rui Wang, Minyan Shi, Fucheng Yan, and Ran Wang. "Dynamic Offloading Loading Optimization in Distributed Fault Diagnosis System with Deep Reinforcement Learning Approach." Applied Sciences 13, no. 7 (March 23, 2023): 4096. http://dx.doi.org/10.3390/app13074096.
Full textAbstract:
Artificial intelligence and distributed algorithms have been widely used in mechanical fault diagnosis with the explosive growth of diagnostic data. A novel intelligent fault diagnosis system framework that allows intelligent terminals to offload computational tasks to Mobile edge computing (MEC) servers is provided in this paper, which can effectively address the problems of task processing delays and enhanced computational complexity. As the resources at the MEC and intelligent terminals are limited, performing reasonable resource allocation optimization can improve the performance, especially for a multi-terminals offloading system. In this study, to minimize the task computation delay, we jointly optimize the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection under a dynamic environment with stochastic task arrivals. The challenging dynamic joint optimization problem is formulated as a reinforcement learning (RL) problem, which is designed as the computational offloading policies to minimize the long-term average delay cost. Two deep RL strategies, deep Q-learning network (DQN) and deep deterministic policy gradient (DDPG), are adopted to learn the computational offloading policies adaptively and efficiently. The proposed DQN strategy takes the MEC selection as a unique action while using the convex optimization approach to obtain the local content splitting ratio and the transmission/computation power allocation. Simultaneously, the actions of the DDPG strategy are selected as all dynamic variables, including the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection. Numerical results demonstrate that both proposed strategies perform better than the traditional non-learning schemes. The DDPG strategy outperforms the DQN strategy in all simulation cases exhibiting minimal task computation delay due to its ability to learn all variables online.
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Zhang, Chenglei, Cunshan Zhang, Jiaojiao Zhuang, Hu Han, Bo Yuan, Jiajia Liu, Kang Yang, Shenle Zhuang, and Ronglan Li. "Evaluation of Cloud 3D Printing Order Task Execution Based on the AHP-TOPSIS Optimal Set Algorithm and the Baldwin Effect." Micromachines 12, no. 7 (July 6, 2021): 801. http://dx.doi.org/10.3390/mi12070801.
Full textAbstract:
Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments, and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents’ dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.
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Kumar, Vijay, and Ramita Sahni. "Dynamic testing resource allocation modeling for multi-release software using optimal control theory and genetic algorithm." International Journal of Quality & Reliability Management 37, no. 6/7 (February 7, 2020): 1049–69. http://dx.doi.org/10.1108/ijqrm-09-2019-0296.
Full textAbstract:
PurposeThe use of software is overpowering our modern society. Advancement in technology is directly proportional to an increase in user demand which further leads to an increase in the burden on software firms to develop high-quality and reliable software. To meet the demands, software firms need to upgrade existing versions. The upgrade process of software may lead to additional faults in successive versions of the software. The faults that remain undetected in the previous version are passed on to the new release. As this process is complicated and time-consuming, it is important for firms to allocate resources optimally during the testing phase of software development life cycle (SDLC). Resource allocation task becomes more challenging when the testing is carried out in a dynamic nature.Design/methodology/approachThe model presented in this paper explains the methodology to estimate the testing efforts in a dynamic environment with the assumption that debugging cost corresponding to each release follows learning curve phenomenon. We have used optimal control theoretic approach to find the optimal policies and genetic algorithm to estimate the testing effort. Further, numerical illustration has been given to validate the applicability of the proposed model using a real-life software failure data set.FindingsThe paper yields several substantive insights for software managers. The study shows that estimated testing efforts as well as the faults detected for both the releases are closer to the real data set.Originality /valueWe have proposed a dynamic resource allocation model for multirelease of software with the objective to minimize the total testing cost using the flexible software reliability growth model (SRGM).