Socially Optimal Correlated Equilibrium in Class-Anonymous Offloading Game with Computing Access Points

We consider a multi-user mobile offloading network with multiple computing access points (CAPs). Each user has one task to be processed, and may choose to reduce the cost of processing the task by offloading it to a CAP or to a remote cloud server. Each user belongs to one of a fixed number of classes, which determines the distribution of their task parameters. We aim to produce an offloading decision that minimizes the expected social cost of the system, while giving selfish users an incentive to follow that decision. Towards that goal, we show that our system can be formulated as a class-anonymous game, and we derive the reduced form of this game to prove that a socially optimal correlated equilibrium (CE) can be computed in polynomial time and space with respect to the number of users. Like the Nash Equilibrium, the CE maintains the necessary conditions for stability in a system with rational and selfish users, while being much easier to compute for non-potential finite games. Simulation results demonstrate the superior results of our solution when compared with random mapping and an alternate means of computing a CE.