Delay-Aware Multi-Stage Edge Server Upgrade with Budget Constraint

2025-12-18

AI Summary

This paper addresses the joint optimization of server upgrade scheduling and task offloading in edge computing systems under multi-stage budget constraints, aiming to maximize the number of latency-constrained tasks successfully served. It formulates, for the first time, a dynamic multi-stage server deployment/upgrading planning problem that incorporates realistic evolving constraints—including hardware depreciation, increasing task volumes, and progressively tightening latency requirements. We propose a mixed-integer linear programming (MILP) model alongside an efficient heuristic algorithm, M-ESU/H, balancing solution optimality and scalability. On small-scale instances, M-ESU/H achieves 98.75% of the optimal objective value while accelerating computation by several orders of magnitude. On large-scale scenarios, it improves task satisfaction rates by up to 21.57% over baseline methods, demonstrating both practical efficacy and strong scalability.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In this paper, the Multi-stage Edge Server Upgrade (M-ESU) is proposed as a new network planning problem, involving the upgrading of an existing multi-access edge computing (MEC) system through multiple stages (e.g., over several years). More precisely, the problem considers two key decisions: (i) whether to deploy additional edge servers or upgrade those already installed, and (ii) how tasks should be offloaded so that the average number of tasks that meet their delay requirement is maximized. The framework specifically involves: (i) deployment of new servers combined with capacity upgrades for existing servers, and (ii) the optimal task offloading to maximize the average number of tasks with a delay requirement. It also considers the following constraints: (i) budget per stage, (ii) server deployment and upgrade cost (in $) and cost depreciation rate, (iii) computation resource of servers, (iv) number of tasks and their growth rate (in %), and (v) the increase in task sizes and stricter delay requirements over time. We present two solutions: a Mixed Integer Linear Programming (MILP) model and an efficient heuristic algorithm (M-ESU/H). MILP yields the optimal solution for small networks, whereas M-ESU/H is used in large-scale networks. For small networks, the simulation results show that the solution computed by M-ESU/H is within 1.25% of the optimal solution while running several orders of magnitude faster. For large networks, M-ESU/H is compared against three alternative heuristic solutions that consider only server deployment, or giving priority to server deployment or upgrade. Our experiments show that M-ESU/H yields up to 21.57% improvement in task satisfaction under identical budget and demand growth conditions, confirming its scalability and practical value for long-term MEC systems.

Problem

• Optimizing multi-stage edge server deployment and upgrade decisions
• Maximizing tasks meeting delay requirements under budget constraints
• Addressing dynamic task growth and stricter delay demands over time

Innovation

• Multi-stage edge server upgrade with budget constraints
• Mixed Integer Linear Programming for optimal small network solutions
• Heuristic algorithm for scalable large network task offloading