Workload Re-Allocation for Edge Computing With Server Collaboration: A Cooperative Queueing Game Approach

In this paper, a long-term workload management problem for multi-server edge computing with server collaboration is studied. In the considered model, mobile users’ computation-intensive tasks are generated dynamically over the time and offloaded to associated edge servers according to pre-determined subscription agreements. Upon receiving the subscribed workload, each edge server can then decide to whether participate in server collaboration for enabling workload re-allocation (i.e., workload exchange) with other heterogeneously configured edge servers. Unlike most of the existing work, this paper takes into account both competitions and collaborations among strategic edge servers in sharing their computing capacities. To achieve the equilibrium for each edge server in minimizing its expected cost (including energy consumption, delay, transmission, configuration and pricing costs), a joint optimization is formulated for determining i) its amount of workload to undertake, ii) compensation price charged from peers, and iii) computing speed to adopt. To efficiently solve this problem, we propose a novel cooperative queueing game approach, which integrates a convex optimization, a core cost sharing scheme and a mapping rule. Theoretical analyses and extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

1 Introduction

Edge computing [1], [2] has been widely accepted as a promising technology for supporting resource constrained mobile users (or end devices) to run computation-intensive while delay-sensitive Internet-of-Things (IoT) applications, such as natural language processing, virtual reality, facial recognition, interactive entertainment, healthcare monitoring and crowdsensing. Different from the traditional cloud computing systems (e.g., AWS, AliCloud and Azure)[3], in which public cloud servers are usually located far away from mobile users and have to be reached through wide area networks, edge computing, in contrast, enables computing capabilities at ubiquitous wireless access points (e.g., small-cell base stations) so that more flexible, agile and convenient supplementary computing services can be provided to mobile users whenever they encounter computational burdens [4].