Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

Abstract

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.

Multi-access Edge Computing: A Survey

“…c 2018 Information Processing Society of Japan Chen et al [73] study the problem of wireless channel assignment. As the processing offload to a MEC server requires to transfer various input data over a wireless access, and the wireless channels are regarded as a shared resource among users that want offloading.…”

Energy Efficiency in Latency-Constrained Application Offloading From Mobile Clients to Multiple Virtual Machines

“…Since more energy is needed in the robust case, the value of R UL,max will be lower than the one of the perfect CSI case. The value of R UL,max in the robust case can be calculated based on the expressions (16)- (17) in [20] by…”

“…One of the main disadvantages is that these clouds may be situated far away from the end user, which entails high delays that may vary depending on the saturation of the network and the quality of the backhaul (BH) links. A possible solution to cope with this comes from the edge-cloud concept or mobile edge computing (MEC) [16], [17]. Recent surveys on architecture and computation offloading and radio-andcomputational resource management for MEC can be found in [18] and [19], respectively.…”

Multi-access edge computing: open issues, challenges and future perspectives

“…Fully virtualized MEC infrastructure is proposed in [23]. A distributed computation offloading algorithm is presented in [24]. SEcS (Scalable Edge computing Services) framework is presented to build and deploy Edge computing Services to address the challenge of scalability, high availability, fault tolerance and robustness [25].…”