Strategic Server Deployment under Uncertainty in Mobile Edge Computing

TL;DR

This paper addresses the complex problem of deploying servers in mobile edge computing under uncertain, time-varying workloads by formulating a stochastic bilevel optimization and applying submodular maximization algorithms, validated with real data.

Contribution

It introduces a novel stochastic bilevel optimization framework for server deployment under uncertainty and demonstrates an effective approximation approach using submodular functions.

Findings

Proposed algorithm outperforms alternatives with up to 55% improvement.

Formulated the deployment problem as a strongly NP-hard stochastic bilevel optimization.

Validated effectiveness with real-world data.

Abstract

Server deployment is a fundamental task in mobile edge computing: where to place the edge servers and what user cells to assign to them. To make this decision is context-specific, but common goals are 1) computing efficiency: maximize the amount of workload processed by the edge, and 2) communication efficiency: minimize the communication cost between the cells and their assigned servers. We focus on practical scenarios where the user workload in each cell is unknown and time-varying, and so are the effective capacities of the servers. Our research problem is to choose a subset of candidate servers and assign them to the user cells such that the above goals are sustainably achieved under the above uncertainties. We formulate this problem as a stochastic bilevel optimization, which is strongly NP-hard and unseen in the literature. By approximating the objective function with submodular functions, we can utilize state-of-the-art greedy algorithms for submodular maximization to effectively solve our problem. We evaluate the proposed algorithm using real-world data, showing its superiority to alternative methods; the improvement can be as high as 55%