Calibrated Bandit Learning for Decentralized Task Offloading in Ultra-Dense Networks

Abstract

The integration of mobile edge computing (MEC) into an ultra-dense network (UDN) can provide ubiquitous task offloading services to computation-demanding users leveraging densely deployed micro base stations. The conventional multi-user task offloading strategies are performed centrally, where a central node makes global task offloading decisions on server selection and resource allocation. In practice, the deployment becomes prohibitively complex with the increasing number of users as it involves high communication overhead and complex global optimization operations. In this paper, we develop a novel decentralized task offloading strategy in UDN, enabling users to independently make local task offloading decisions. We formulate the associated optimization problem to minimize the long-term average task delay among all users. On this basis, we develop a novel calibrated contextual bandit learning (CCBL) algorithm, where users can learn the computational delay functions of micro base stations and predict the task offloading decisions of other users in a decentralized manner. The convergence of the proposed CCBL algorithm is verified via the approachability theory. Moreover, we transfer the target of calibrated learning from all micro base stations to a single user and propose a user-oriented CCBL algorithm to further decrease the computational complexity and increase the convergence rate. Simulation results illustrate that our proposed algorithm outperforms the existing decentralized algorithms and approaches the centralized one.