Spatio-temporal Edge Service Placement: A Bandit Learning Approach

TL;DR

This paper introduces SEEN, a novel bandit learning algorithm for optimizing edge service placement under budget constraints, utilizing user context to improve decision-making in dynamic edge computing environments.

Contribution

The paper formulates the edge service placement as a combinatorial contextual bandit problem and proposes SEEN, a new algorithm with proven sublinear regret bounds for efficient resource allocation.

Findings

SEEN outperforms benchmark solutions in simulations.

The algorithm effectively learns optimal placement strategies over time.

It handles overlapping coverage scenarios with a knapsack extension.

Abstract

Shared edge computing platforms deployed at the radio access network are expected to significantly improve quality of service delivered by Application Service Providers (ASPs) in a flexible and economic way. However, placing edge service in every possible edge site by an ASP is practically infeasible due to the ASP's prohibitive budget requirement. In this paper, we investigate the edge service placement problem of an ASP under a limited budget, where the ASP dynamically rents computing/storage resources in edge sites to host its applications in close proximity to end users. Since the benefit of placing edge service in a specific site is usually unknown to the ASP a priori, optimal placement decisions must be made while learning this benefit. We pose this problem as a novel combinatorial contextual bandit learning problem. It is "combinatorial" because only a limited number of edge sites can be rented to provide the edge service given the ASP's budget. It is "contextual" because we utilize user context information to enable finer-grained learning and decision making. To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore, SEEN is extended to scenarios with overlapping service coverage by incorporating a disjunctively constrained knapsack problem. In both cases, we prove that our algorithm achieves a sublinear regret bound when it is compared to an oracle algorithm that knows the exact benefit information. Simulations are carried out on a real-world dataset, whose results show that SEEN significantly outperforms benchmark solutions.