Collaborative Service Placement for Edge Computing in Dense Small Cell Networks

Abstract

Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service provision latency and saving backhaul network bandwidth. Although computation offloading for MEC systems has been extensively studied in the literature, service placement is an equally, if not more, important design topic of MEC, yet receives much less attention. Service placement refers to configuring the service platform and storing the related libraries/databases at the edge server, e.g., MEC-enabled Base Station (BS), which enables corresponding computation tasks to be executed. Due to the limited computing resource, the edge server can host only a small number of services and hence which services to host has to be judiciously decided to maximize the system performance. In this paper, we investigate collaborative service placement in MEC-enabled dense small cell networks. An efficient decentralized algorithm, called CSP (Collaborative Service Placement), is proposed where a network of small cell BSs optimize service placement decisions collaboratively to address a number of challenges in MEC systems, including service heterogeneity, spatial demand coupling, and decentralized coordination. CSP is developed based on parallel Gibbs sampling by exploiting the graph coloring on the small cell network. The algorithm significantly improves the time efficiency compared to conventional Gibbs sampling, yet guarantees provable convergence and optimality. CSP is further extended to work with selfish BSs, where BSs are allowed to choose “to cooperate” or “not to cooperate.” We employ coalitional game to investigate the strategic behaviors of selfish BSs and design a coalition formation scheme to form stable BS coalitions using merge-and-split rules. Simulations results show that CSP can effectively reduce edge system operational cost for both cooperative and selfish BSs.