Modeling Tradeoffs between mobility, cost, and performance in Edge Computing

TL;DR

This paper models the trade-offs between mobility, cost, and performance in edge computing using queuing theory, providing insights into resource management for mobile workloads in 5G networks.

Contribution

It introduces a novel queuing model to analyze cost-performance trade-offs in mobile edge computing environments, considering workload mobility and variations.

Findings

Mobility significantly impacts edge computing costs and latency.

The model accurately predicts system behavior under various workload dynamics.

Practical guidelines for resource management in edge systems are derived.

Abstract

Edge computing provides a cloud-like architecture where small-scale resources are distributed near the network edge, enabling applications on resource-constrained devices to offload latency-critical computations to these resources. While some recent work showed that the resource constraints of the edge could result in higher end-to-end latency under medium to high utilization due to higher queuing delays, to the best of our knowledge, there has not been any work on modeling the trade-offs of deploying on edge versus cloud infrastructures in the presence of mobility. Understanding the costs and trade-offs of this architecture is important for network designers, as the architecture is now adopted to be part of 5G and beyond networks in the form of the Multi-access Edge Computing (MEC). In this paper we focus on quantifying and estimating the cost of edge computing. Using closed-form queuing models, we explore the cost-performance trade-offs in the presence of different systems dynamics. We model how workload mobility and workload variations influence these tradeoffs, and validate our results with realistic experiments and simulations. Finally, we discuss the practical implications for designing edge systems and developing algorithms for efficient resource and workload management.