Distributed Computation Offloading Based on Stochastic Game in Multi-server Mobile Edge Computing Networks

With the popularity of the Internet of things (IoT), 5G technology needs to meet various requirements of a large number of IoT applications. Mobile edge computing (MEC) is a promising approach in 5G scenario, which can solve the problems of resource shortage and high latency. Computation offloading is a key technology to reduce latency and energy consumption in MEC. In this paper, we consider a scenario with a dense distribution of edge nodes, namely multi-edge server distribution, and focus on the offloading problem in the overlapping coverage area of service scope. We build a two-step game model using the stochastic game theory. We pay attention to the relevance of state transition, that is, the cost of the next state is taken into account when making decisions in current state. In addition, we prove the existence of Nash Equilibrium (NE) by the concept of exact potential game, then propose the best response based on stochastic game (BRSG) algorithm to solve the problem. Numerical results illustrate that our algorithm can reach a NE through finite number of iterations, and an equilibrium strategy can be obtained. Besides, considering the state correlation makes the equilibrium cost significantly lower.

[1]  Jianping Pan,et al.  Learning Based Mobility Management Under Uncertainties for Mobile Edge Computing , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[2]  Jun Cai,et al.  A Multi-User Mobile Computation Offloading and Transmission Scheduling Mechanism for Delay-Sensitive Applications , 2020, IEEE Transactions on Mobile Computing.

[3]  Hao Jin,et al.  Computation Offloading Optimization Based on Probabilistic SFC for Mobile Online Gaming in Heterogeneous Network , 2019, IEEE Access.

[4]  Yueming Cai,et al.  Dynamic Computation Offloading for Mobile Cloud Computing: A Stochastic Game-Theoretic Approach , 2019, IEEE Transactions on Mobile Computing.

[5]  Ke Zhang,et al.  Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks , 2016, IEEE Access.

[6]  Peng Li,et al.  A Survey on Computation Offloading for Mobile Edge Computing Information , 2018, 2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS).

[7]  Xinyu Yang,et al.  A Survey on the Edge Computing for the Internet of Things , 2018, IEEE Access.

[8]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[9]  Yunlong Cai,et al.  D2D Communications Meet Mobile Edge Computing for Enhanced Computation Capacity in Cellular Networks , 2019, IEEE Transactions on Wireless Communications.

[10]  Haiyun Luo,et al.  Energy-optimal mobile application execution: Taming resource-poor mobile devices with cloud clones , 2012, 2012 Proceedings IEEE INFOCOM.

[11]  Zdenek Becvar,et al.  Mobile Edge Computing: A Survey on Architecture and Computation Offloading , 2017, IEEE Communications Surveys & Tutorials.

[12]  Yueming Cai,et al.  Optimal Power Allocation and User Scheduling in Multicell Networks: Base Station Cooperation Using a Game-Theoretic Approach , 2014, IEEE Transactions on Wireless Communications.

[13]  Tie Qiu,et al.  TOSG: A Topology Optimization Scheme With Global Small World for Industrial Heterogeneous Internet of Things , 2019, IEEE Transactions on Industrial Informatics.

[14]  Jiming Chen,et al.  Narrowband Internet of Things: Implementations and Applications , 2017, IEEE Internet of Things Journal.

[15]  Weiwei Xia,et al.  Joint Offloading and Resource Allocation Optimization for Mobile Edge Computing , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[16]  Ning Li,et al.  Distributed Power Control for Interference-Aware Multi-User Mobile Edge Computing: A Game Theory Approach , 2018, IEEE Access.

[17]  György Dán,et al.  A game theoretic analysis of selfish mobile computation offloading , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[18]  Jianchao Zheng,et al.  QoE Driven Decentralized Spectrum Sharing in 5G Networks: Potential Game Approach , 2017, IEEE Transactions on Vehicular Technology.

[19]  Alagan Anpalagan,et al.  Mobile Cloud Storage Over 5G: A Mechanism Design Approach , 2019, IEEE Systems Journal.

[20]  Lei Liu,et al.  TMED: A Spider-Web-Like Transmission Mechanism for Emergency Data in Vehicular Ad Hoc Networks , 2018, IEEE Transactions on Vehicular Technology.

[21]  Wenzhong Li,et al.  Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing , 2015, IEEE/ACM Transactions on Networking.

[22]  Huaming Wu,et al.  Multi-Objective Decision-Making for Mobile Cloud Offloading: A Survey , 2018, IEEE Access.

[23]  Gerhard P. Hancke,et al.  A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges , 2018, IEEE Access.

[24]  Hyundong Shin,et al.  Learning for Computation Offloading in Mobile Edge Computing , 2018, IEEE Transactions on Communications.