Online optimal service caching for multi-access edge computing: A constrained Multi-Armed Bandit optimization approach

In order to fully exploit the power of Multi-Access Edge Computing, services need to be cached at the network edge in an adaptive and responsive way to accommodate the high system dynamics and uncertainty. In this paper, we study the online service caching problem in MEC, with the goal to minimize users’ perceived latency while at the same time, ensure the rate of tasks processed by the edge server is no less than a preset threshold. We model the problem with a Constrained stochastic Multi-Armed Bandit formulation, and propose a simple yet effective online caching algorithm called Constrained Confidence Bound (CCB). CCB achieves O(TlnT) bounds on both regret and violation of the constraint, and is able to achieve a good balance between them. We further consider the scenario when there is cost (i.e., delay) due to service switches, and propose two service switch-aware caching algorithms – Explore-First (EF) and Successive Elimination-based (SE) caching, together with a novel sampling scheme. We prove that EF achieves O(T23(lnT)13) bound on regret and violation, whereas SE achieves O(TlnT) and converges significantly faster. Lastly, we conduct extensive simulations to evaluate our algorithms and results demonstrate their superior performance over baselines.