Demand response in NOMA-based mobile edge computing: a two-phase game-theoretical approach

Mobile edge computing (MEC), as a key technology that facilitates 5G networks, provides a new and prospective mobile computing paradigm that allows the deployment of edge servers at base stations geographically close to mobile users to reduce their end-to-end network latency. Similar to cloud servers, edge servers running 24/7 in an MEC system consume a large amount of energy, contribute a significant proportion of global carbon emissions, and thus require demand response management. Demand response has been widely employed to reduce energy consumption at data centers. However, existing demand response approaches for data centers are rendered obsolete by the new and unique characteristics of MEC systems: 1) proximity constraint-mobile users can be served by neighbor edge servers only; 2) latency constraint-mobile users' workloads should be processed by their neighbor edge servers to ensure low latency; and 3) capacity constraint-edge servers have limited computing and communication resources to serve mobile users. Demand response for MEC is further complicated by the non-orthogonal multiple access (NOMA) scheme-the emerging radio access scheme for 5G. Communication resources like channels and transmit power in the NOMA-based MEC system must be systematically considered with computing resources like CPU, memory and storage to fulfill mobile users' resource demands. This paper makes the first attempt to tackle this Edge Demand Response (EDR) problem. We first formulate this problem and prove its NP NP-hardness. Then, we propose a two-phase game-theoretical approach, named EDRGame, to solve the EDR problem. Its performance is theoretically analyzed and experimentally evaluated against three baseline approaches and two state-of-the-art approaches on a widely-used real-world dataset. The results show that it solves the EDR problem effectively and efficiently.