AGENT BASED APPROACH FOR TASK OFFLOADING IN EDGE COMPUTING

(Received: 7-Jan.-2023, Revised: 24-Mar.-2023 and 9-Apr.-2023 , Accepted: 10-Apr.-2023)

Authors Hossein Morshedlou, Reza Vafa Shoar,

Keywords #Edge computing #Task offloading #Nash equilibrium #Agent #User satisfaction

Abstract Due to limited resource capacity in the edge network and a high volume of tasks offloaded to edge servers, edge resources may be unable to provide the required capacity for serving all tasks. As a result, some tasks should be moved to the cloud, which may cause additional delays. This may lead to dissatisfaction among users of the transferred tasks. In this paper, a new agent-based approach to decision-making is presented about which tasks should be transferred to the cloud and which ones should be served locally. This approach tries to pair tasks with resources, such that a paired resource is the most preferred resource by the user or task among all available resources. We demonstrate that reaching a Nash Equilibrium point can satisfy the aforementioned condition. A game-theoretic analysis is included to demonstrate that the presented approach increases the average utility of the users and their level of satisfaction.

References

[1] C. Feng et al., "Computation Offloading in Mobile Edge Computing Networks: A Survey," J. of Network and Computer Applications, vol. 202, no. 103366, p. 103366, Jun. 2022.

[2] Y.-Y. Huang and P.-C. Wang, "Computation Offloading and User-clustering Game in Multi-channel Cellular Networks for Mobile Edge Computing," Sensors (Basel), vol. 23, no. 3, p. 1155, Jan. 2023.

[3] X. Chen, "Decentralized Computation Offloading Game for Mobile Cloud Computing," IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 4, pp. 974–983, Apr. 2015.

[4] Y. Mao et al., "Dynamic Computation Offloading for Mobile-edge Computing with Energy Harvesting Devices," IEEE J. on Selected Areas in Communications, vol. 34, no. 12, pp. 3590–3605, Dec. 2016.

[5] X. Chen, L. Jiao, W. Li and X. Fu, "Efficient Multi-user Computation Offloading for Mobile-edge Cloud Computing," IEEE/ACM Transactions on Networking, vol. 24, no. 5, pp. 2795–2808, Oct. 2016.

[6] Y. Liu et al., "Incentive Mechanism for Computation Offloading Using Edge Computing: A Stackelberg Game Approach," Computer Networks, vol. 129, pp. 399–409, Dec. 2017.

[7] W. Wang, Y. Zhao, M. Tornatore, A. Gupta, J. Zhang and B. Mukherjee, "Virtual Machine Placement and Workload Assignment for Mobile Edge Computing," Proc. of the 2017 IEEE 6th Int. Conf. on Cloud Networking (CloudNet), DOI; 10.1109/CloudNet.2017.8071527, Prague, Czech Republic, 2017.

[8] C. Jian, L. Bao and M. Zhang, "A High-efficiency Learning Model for Virtual Machine Placement in Mobile Edge Computing," Cluster Computing, vol. 25, no. 5, pp. 3051–3066, Oct. 2022.

[9] J. Plachy, Z. Becvar and E. C. Strinati, "Dynamic Resource Allocation Exploiting Mobility Prediction in Mobile Edge Computing," Proc. of the 2016 IEEE 27th Annual Int. Symp. on Personal, Indoor and Mobile Radio Communications (PIMRC), DOI: 10.1109/PIMRC.2016.7794955, Valencia, Spain, 2016.

[10] J. Zhou et al., "Distributed Task Offloading Optimization with Queueing Dynamics in Multiagent Mobile-edge Computing Networks," IEEE Internet of Things J., vol. 8, no. 15, pp. 12311–12328, 2021.

[11] X. Xu et al., "Game Theory for Distributed IoV Task Offloading with Fuzzy Neural Network in Edge Computing," IEEE Transactions on Fuzzy Systems, vol. 30, no. 11, pp. 4593–4604, Nov. 2022.

[12] W. Ding et al., "A Multi-agent Meta-based Task Offloading Strategy for Mobile Edge Computing," IEEE Trans. on Cognitive and Developmental Systems, DOI: 10.1109/TCDS.2023.3246107, 2023.

[13] J. Yang, Q. Yuan, S. Chen, H. He, X. Jiang and X. Tan, "Cooperative Task Offloading for Mobile Edge Computing Based on Multi-agent Deep Reinforcement Learning," IEEE Transactions on Network and Service Management, DOI: 10.1109/TNSM.2023.3240415, 2023.

[14] J. Hou et al., "GP-NFSP: Decentralized Task Offloading for Mobile Edge Computing with Independent Reinforcement Learning," Future Generation Computer Systems, vol. 141, pp. 205–217, Apr. 2023.

[15] J. Lei and U. V. Shanbhag, "Stochastic Nash Equilibrium Problems: Models, Analysis and Algorithms," IEEE Control Systems, vol. 42, no. 4, pp. 103–124, Aug. 2022.