Strategic Server Deployment under Uncertainty in Mobile Edge Computing

2025-12-13

AI Summary

This paper addresses the dynamic server placement and user-cell association problem in mobile edge computing, where both user loads and server capacities are unknown and time-varying. To jointly optimize computational offloading rate (i.e., maximize edge-processed load) and communication overhead (i.e., minimize transmission cost), we formulate the first stochastic bilevel optimization model. To overcome its NP-hardness, we innovatively approximate the objective as a submodular function. Leveraging stochastic optimization, submodularity theory, and greedy approximation algorithms, we design an efficient and robust solution framework. Extensive evaluations on real-world datasets demonstrate that our approach achieves up to 55% higher computational efficiency compared to baseline methods, while significantly improving deployment sustainability and robustness under uncertainty.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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%

Problem

• Optimizes server placement and cell assignment in mobile edge computing
• Addresses uncertainties in user workload and server capacity variations
• Formulates and solves a stochastic bilevel optimization problem efficiently

Innovation

• Stochastic bilevel optimization for server deployment
• Submodular function approximation for objective simplification
• Greedy algorithms for effective submodular maximization