🤖 AI Summary
This study addresses the complex trade-offs among cost, performance, and latency in mobile edge computing scenarios, where existing research lacks a systematic modeling framework for edge–cloud architectures. The authors present the first closed-form queueing network model to quantitatively analyze how workload mobility and system dynamics jointly impact end-to-end latency and operational cost. Through both simulation and real-world validation, the model’s accuracy and practical relevance are demonstrated. The work highlights the critical roles of mobility patterns and system utilization, offering theoretical foundations and actionable insights for resource provisioning and optimization in 5G and future mobile edge computing systems.
📝 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 trade- offs, 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.