Adaptive User-managed Service Placement for Mobile Edge Computing via Contextual Multi-armed Bandit Learning

Abstract

Mobile Edge Computing (MEC), envisioned as a cloud extension, pushes cloud resource from the network core to the network edge, thereby meeting the stringent service requirements of many emerging computation-intensive mobile applications. Many existing works have focused on studying the system-wide MEC service placement issues, personalized service performance optimization yet receives much less attention. As motivated, in this paper we propose a novel adaptive user-managed service placement mechanism, which jointly optimizes a users perceived-latency and service migration cost, weighted by user-specific preferences. We first formulate the user-managed dynamic service placement process with limited system information as a contextual multi-armed bandit learning problem. In particular, we investigate both cases without and with neighboring edge feedbacks, where the later considers edge information sharing for more informed decision making. For both cases, we design lightweight Thompson-sampling based online learning algorithms, which can efficiently assist the user to make adaptive service placement decisions. We further conduct a novel information-directed theoretical analysis on the regret bound of the proposed online learning algorithms and reveal the structural impact of edge information sharing. Extensive evaluations demonstrate the superior performance gain of the proposed adaptive user-managed service placement mechanism over existing learning schemes.