Dynamic Computation Offloading in Multi-Server MEC Systems: An Online Learning Approach

As the network becomes dense, multi-server mobile edge computing (MEC) systems with multiple candidate MEC servers, bring new opportunities to enrich user experience through computation offloading. Specifically, MEC server selection, as a new dimension, arises to strike a well balance between energy consumption and task execution delay of mobile device (MD). Since channel conditions between the MD and MEC server (or its connected access point) as well as available computing capability at MEC servers are time-varying, this paper aims to devise dynamic computation offloading mechanism to account for delay-energy tradeoffs in multi-server MEC systems. To this end, we jointly optimize transmit power and MEC server selection for the MD to minimize time average expected task execution delay, under the constraint of average energy consumption. With partial current network status available, we then combine Lyapunov optimization framework and multi-armed bandit framework for an online learning based computation offloading algorithm, whose feasibility and regret bound are given through theoretical analyses. Finally, simulation results are presented to demonstrate its efficiency and superiority.