ABSTRACT Cloudlet is a new paradigm in mobile cloud computing to provide resources to nearby mobile users via one-hop wireless connections. In this paper, we leverage the social tie structure among mobile users to achieve mutually beneficial computation offloading decision making, and hence, enhance the system-wide performance. Drawing on a social group utility maximization (SGUM) framework, we cast users' decision making of whether to offload or not as a Socially aware computation offloading game (COG). We study the SGUM-based COG for both strong and weak information cases. For the strong information case, where each user has the knowledge of other users' actions and the perfect observation of its achieved social group utility, we show that there exists a socially aware Nash equilibrium (SNE). We then design a distributed algorithm to achieve the SNE and quantify its performance gap with respect to the social optimal solution. For the weak information case, where each user does not have the knowledge of other users' actions and observes a noise-corrupted social group utility, we develop a distributed reinforcement learning algorithm, which is shown to converge almost surely to an -SNE. The numerical results show that the computation offloading performance can be significantly enhanced by leveraging the social ties among the users.
Tang et al. REF designed a socially-aware mobile network and modeled the offloading problem as a socially-aware computation offloading game.
When Social Network Meets Mobile Cloud: A Social Group Utility Approach for Optimizing Computation Offloading in Cloudlet
IEEE Access
IEEE Access
Computer Science