A Two-stage Service-oriented Task Offloading Framework with Edge-cloud Collaboration: A Game Theory Approach

Abstract

With the fast development of Mobile Internet, data traffic generated by end devices is anticipated to witness substantial growth in the future years. However, processing tasks locally will cause latency due to the limited resources of the end devices. Edge-cloud collaboration, an effective solution for latency-sensitive applications, is attracting greater attention from both industry and academia. It combines the advantages of the cloud center with abundant computing resources and edge nodes with low-latency capabilities. In this paper, we propose a two-stage task offloading framework with edge-cloud collaboration to assist end devices processing latency-sensitive tasks either on the edge servers or in the cloud center. As for homogeneous task offloading, in the first stage, the competitive end devices offload tasks to the edge gateways. We formulate the selfish task offloading problem among end devices as a potential game. In the second stage, the edge nodes request resources from the cloud center to process end devices tasks due to their limited resources. Then, we consider the heterogeneous task offloading problem and use intelligent optimization algorithm to obtain the optimal offloading strategy. Simulation results show that the service prices of edge nodes influence the decisions and task offloading costs of end devices. We also verify the intelligent optimization algorithm can achieve optimal performance with low complexity and fast convergence.

Keywords

Edge-cloud collaboration / task offloading / potential game / simulation annealing algorithm

References

[1] Alwarafy A, Al-Thelaya K, Abdallah M, Schneider J, Hamdi M. A survey on security and privacy issues in edge-computing-assisted internet of things. IEEE Internet of Things Journal, 2021, 8(6): 4004-4022.

[2] Caiazza C, Giordano S, Luconi V, Vecchio A. Edge computing vs centralized cloud: Impact of communication latency on the energy consumption of LTE terminal nodes. Computer Communications, 2022, 194: 213-225.

[3] Chen J, Ran X. Deep learning with edge computing: A review. Proceedings of the IEEE, 2019, 107(8): 1655-1674.

[4] Chen Z, Ma Q, Gao L, Chen X (2021). Edgeconomics: Price competition and selfish computation offloading in multi-server edge computing networks. Proceedings of the 19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks, USA.

[5] Dai Y, Xu D, Maharjan S, Qiao G, Zhang Y. Artificial intelligence empowered edge computing and caching for internet of vehicles. IEEE Wireless Communications, 2019, 26(3): 12-18.

[6] Dai F, Liu G, Mo Q, Xu W, Huang B. Task offloading for vehicular edge computing with edge-cloud cooperation. World Wide Web-Internet and Web Information Systems, 2022, 25(5): 1999-2017.

[7] Ding S, Lin D. Multi-agent reinforcement learning for cooperative task offloading in distributed edge cloud computing. IEICE Transactions on Information and Systems, 2022, E105D(5): 936-945.

[8] Fang J, Ye Z, Song S (2022). Research on task offloading strategy based on priority chemical reaction algorithm in edge-cloud scenario. Proceedings of the 11th International Conference on Communications, Circuits and Systems, Singapore.

[9] Gao J, Chang R, Yang Z, Huang Q, Zhao Y, Wu Y. A task offloading algorithm for cloud-edge collaborative system based on Lyapunov optimization. Cluster Computing - the Journal of Networks Software Tools and Applications, 2023, 26(1): 337-348.

[10] Gu X, Zhang G, Cao Y. Cooperative mobile edge computing-cloud computing in Internet of vehicle: Architecture and energy-efficient workload allocation. Transactions on Emerging Telecommunications Technologies, 2021, 32(8): e4095.

[11] Guorav K, Kaur A (2023). Computation offloading scheme classification using cloud-edge computing for Internet of Vehicles (IoV). Proceedings of the 5th International Conference on Innovative Computing and Communications, India.

[12] Hamzah H, Le D, Kim M, Choo H (2021). Location-aware task offloading for MEC-based high mobility service. Proceedings of the 35th International Conference on Information Networking, Thailand.

[13] Hayyolalam V, Otoum S, Özkasap Ö. Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence. Cluster Computing - The Journal of Networks Software Tools and Applications, 2022, 25(3): 1695-1713.

[14] Laili Y, Guo F, Ren L, Li X, Li Y, Zhang L. Parallel scheduling of large-scale tasks for industrial cloud-edge collaboration. IEEE Internet of Things Journal, 2023, 10(4): 3231-3242.

[15] Li Z, Zhou X, Li T, Liu Y (2021). An optimal-transport-based reinforcement learning approach for computation offloading. Proceedings of the IEEE Wireless Communications and Networking Conference, China.

[16] Li Y (2021). Optimization of task offloading problem based on simulated annealing algorithm in MEC. Proceedings of the 9th International Conference on Intelligent Computing and Wireless Optical Communications, China.

[17] Li S, Sun W. Utility maximisation for resource allocation of migrating enterprise applications into the cloud. Enterprise Information Systems, 2021, 15(2): 197-229.

[18] Li S, Liu H, Li W, Sun W. Optimal cross-layer resource allocation in fog computing: A market-based framework. Journal of Network and Computer Applications, 2023, 209: 103528.

[19] Li S, Liu H, Li W, Sun W. An optimization framework for migrating and deploying multiclass enterprise applications into the cloud. IEEE Transactions on Services Computing, 2023, 16(2): 941-956.

[20] Liu Y, Yu H, Xie S, Zhang Y. Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks. IEEE Transactions on Vehicular Technology, 2019, 68(11): 11158-11168.

[21] Liu X, Jiang J, Li L (2021). Computation offloading and task scheduling with fault-tolerance for minimizing redundancy in edge computing. Proceedings of the 32nd IEEE International Symposium on Software Reliability Engineering, China.

[22] Liu T, Fang L, Zhu Y, Tong W, Yang Y. A near-optimal approach for online task offloading and resource allocation in edge-cloud orchestrated computing. IEEE Transactions on Mobile Computing, 2022, 21(8): 2687-2700.

[23] Mukherjee M, Kumar V, Zhang Q, Mavromoustakis C, Matam R. Optimal pricing for offloaded hard- and soft-deadline tasks in edge computing. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 9829-9839.

[24] Nour B, Mastoraki S, Mtibaa A (2021). Whispering: Joint service offloading and computation reuse in cloud-edge networks. Proceedings of the IEEE International Conference on Communications, Canada.

[25] Qiao G, Leng S, Maharjan S, Zhang Y, Ansari N. Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks. IEEE Internet of Things Journal, 2020, 7(1): 247-257.

[26] Sandholm W H. Potential games with continuous player sets. Journal of Economic Theory, 2001, 97(1): 81-108.

[27] Shah-Mansouri H, Wong V. Hierarchical fog-cloud computing for IoT systems: A computation offloading game. IEEE Internet of Things Journal, 2018, 5(4): 3246-3257.

[28] Shen H, Jiang Y, Deng F, Shan Y. Task unloading strategy of multi uav for transmission line inspection based on deep reinforcement learning. Electronics, 2022, 11(14): 2188.

[29] Su M, Wang G, Chen J (2022). Efficient task offloading with swarm intelligence evolution for edge-cloud collaboration in vehicular edge computing. Software-Practice & Experience.

[30] Suzuki A, Kobayashi M (2022). Multi-agent deep reinforcement learning for cooperative offloading in cloud-edge computing. Proceedings of the IEEE International Conference on Communications, Korea.

[31] Tang H, Li D, Wan J, Imran M, Shoaib M. A reconfigurable method for intelligent manufacturing based on industrial cloud and edge intelligence. IEEE Internet of Things Journal, 2020, 7(5): 4248-4259.

[32] Wu H, Wolter K, Jiao P, Deng Y, Zhao Y, Xu M. EEDTO: An energy-efficient dynamic task offloading algorithm for blockchain-enabled iot-edge-cloud orchestrated computing. IEEE Internet of Things Journal, 2021, 8(4): 2163-2176.

[33] Xu X, Xue Y, Qi L, Yuan Y, Zhang X, Umer T, Wan S. An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles. Future Generation Computer Systems-The International Journal of Escience, 2019, 96: 89-100.

[34] Xu X, Fang Z, Zhang J, He Q, Yu D, Qi L, Dou W. Edge content caching with deep spatiotemporal residual network for iov in smart city. ACM Transactions on Sensor Networks, 2021, 17(3): 29.

[35] Xu F, Xie Y, Sun Y, Qin Z, Li G, Zhang Z. Two-stage computing offloading algorithm in cloud-edge collaborative scenarios based on game theory. Computers & Electrical Engineering, 2022, 97: 107624.

[36] Xu X, Li H, Xu W, Liu Z, Yao L, Dai F. Artificial intelligence for edge service optimization in internet of vehicles: A survey. Tsinghua Science and Technology, 2022, 27(2): 270-287.

[37] Yang J, Dai Y, Ma K, Liu H, Liu Z. A pricing strategy based on potential game and bargaining theory in smart grid. IET Generation, Transmission & Distribution, 2021, 15(2): 253-263.

[38] You Q, Tang B. Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things. Journal of Cloud Computing-Advances Systems and Applications, 2021, 10(1): 41.

[39] Yue Z, Zhu Z, Wang C, Du W (2020). Research on big data processing model of edge-cloud collaboration in cyber-physical systems. Proceedings of the 5th IEEE International Conference on Big Data Analytics, USA.

[40] Zhang H, Chen S, Zou P, Xiong G, Zhao H, Zhang Y (2019). Research and application of industrial equipment management service system based on cloud-edge collaboration. Proceedings of the Chinese Automation Congress, China.

[41] Zhang J, Letaief K. Mobile edge intelligence and computing for the internet of vehicles. Proceedings of the IEEE, 2020, 108(2): 246-261.

[42] Zhang Z. A computing allocation strategy for Internet of things resources based on edge computing. International Journal of Distributed Sensor Networks, 2021, 17(12): 15501477211064800.

[43] Zhu S, Ota K, Dong M. Energy-efficient artificial intelligence of things with intelligent edge. IEEE Internet of Things Journal, 2022, 9(10): 7525-7532.