Joint Edge Server Deployment and Computation Offloading: A Multi-Timescale Stochastic Programming Framework

TL;DR

This paper proposes a multi-timescale stochastic programming framework for joint edge server deployment and computation offloading in MEC, addressing decision timing and information realization challenges to improve AI application QoS.

Contribution

It introduces a novel multi-timescale stochastic programming framework that separates strategic deployment from tactical offloading decisions, considering different information availability timelines.

Findings

Effective joint optimization of deployment and offloading.

Proposed algorithms handle multi-timescale decision-making.

Framework enhances MEC resource management under uncertainty.

Abstract

Mobile Edge Computing (MEC) is a promising approach for enhancing the quality-of-service (QoS) of AI-enabled applications in the B5G/6G era, by bringing computation capability closer to end-users at the network edge. In this work, we investigate the joint optimization of edge server (ES) deployment, service placement, and computation task offloading under the stochastic information scenario. Traditional approaches often treat these decisions as equal, disregarding the differences in information realization. However, in practice, the ES deployment decision must be made in advance and remain unchanged, prior to the complete realization of information, whereas the decisions regarding service placement and computation task offloading can be made and adjusted in real-time after information is fully realized. To address such temporal coupling between decisions and information realization, we introduce the stochastic programming (SP) framework, which involves a strategic-layer for deciding ES deployment based on (incomplete) stochastic information and a tactical-layer for deciding service placement and task offloading based on complete information realization. The problem is challenging due to the different timescales of two layers' decisions. To overcome this challenge, we propose a multi-timescale SP framework, which includes a large timescale (called period) for strategic-layer decision-making and a small timescale (called slot) for tactical-layer decision making. Moreover, we design a Lyapunov-based algorithm to solve the tactical-layer problem at each time slot, and a Markov approximation algorithm to solve the strategic-layer problem in every time period.