Spatiotemporal Dependable Task Execution Services in MEC-enabled Wireless Systems

TL;DR

This paper introduces a spatiotemporal framework combining stochastic geometry and Markov chains to analyze the dependability of MEC-enabled wireless systems, focusing on resource contention, task offloading, and failure mitigation.

Contribution

It presents a novel integrated framework for evaluating communication and computation dependability in MEC systems, considering resource contention and failure-prone virtual machines.

Findings

Optimal number of virtual machines maximizes task capacity.

Dependability metrics are influenced by system parameters.

Framework enables comprehensive performance evaluation.

Abstract

Multi-access Edge Computing (MEC) enables computation and energy-constrained devices to offload and execute their tasks on powerful servers. Due to the scarce nature of the spectral and computation resources, it is important to jointly consider i) contention-based communications for task offloading and ii) parallel computing and occupation of failure-prone MEC processing resources (virtual machines). The feasibility of task offloading and successful task execution with virtually no failures during the operation time needs to be investigated collectively from a combined point of view. To this end, this letter proposes a novel spatiotemporal framework that utilizes stochastic geometry and continuous time Markov chains to jointly characterize the communication and computation performance of dependable MEC-enabled wireless systems. Based on the designed framework, we evaluate the influence of various system parameters on different dependability metrics such as (i) computation resources availability, (ii) task execution retainability, and (iii) task execution capacity. Our findings showcase that there exists an optimal number of virtual machines for parallel computing at the MEC server to maximize the task execution capacity.