Resource Provisioning in Edge Computing for Latency Sensitive Applications

TL;DR

This paper introduces a resource provisioning scheme for edge computing tailored for latency-sensitive IoT applications, utilizing a resource representation model and Lyapunov optimization to improve latency and resource efficiency.

Contribution

It proposes a novel resource representation scheme and a dynamic allocation method using Lyapunov optimization for edge devices in low-latency IoT applications.

Findings

Outperforms benchmark approaches in reducing latency.

Achieves optimal resource utilization.

Validated through extensive simulations.

Abstract

Low-Latency IoT applications such as autonomous vehicles, augmented/virtual reality devices and security applications require high computation resources to make decisions on the fly. However, these kinds of applications cannot tolerate offloading their tasks to be processed on a cloud infrastructure due to the experienced latency. Therefore, edge computing is introduced to enable low latency by moving the tasks processing closer to the users at the edge of the network. The edge of the network is characterized by the heterogeneity of edge devices forming it; thus, it is crucial to devise novel solutions that take into account the different physical resources of each edge device. In this paper, we propose a resource representation scheme, allowing each edge device to expose its resource information to the supervisor of the edge node through the mobile edge computing application programming interfaces proposed by European Telecommunications Standards Institute. The information about the edge device resource is exposed to the supervisor of the EN each time a resource allocation is required. To this end, we leverage a Lyapunov optimization framework to dynamically allocate resources at the edge devices. To test our proposed model, we performed intensive theoretical and experimental simulations on a testbed to validate the proposed scheme and its impact on different system's parameters. The simulations have shown that our proposed approach outperforms other benchmark approaches and provides low latency and optimal resource consumption.