REMR: A Reliability Evaluation Method for Dynamic Edge Computing Network under Time Constraints

TL;DR

This paper introduces REMR, a new reliability evaluation method for dynamic edge computing networks that accounts for time constraints and resource fluctuations, improving deployment plan assessment accuracy.

Contribution

The paper proposes REMR, a reliability evaluation approach based on lower boundary rules under time constraints, tailored for dynamic edge computing scenarios with fluctuating resources.

Findings

REMR effectively evaluates service deployment reliability in DECN.

Simulation results validate the accuracy of REMR using NS3 and Google cluster data.

The method improves reliability assessment compared to existing approaches.

Abstract

While the concept of Artificial Intelligent Internet of Things\ (AIoT) is booming, computation and/or communication-intensive tasks accompanied by several sub-tasks are slowly moving from centralized deployment to edge-side deployment. The idea of edge computing also makes intelligent services sink locally. But in actual scenarios like dynamic edge computing networks (DECN), due to fluctuations in available computing resources of intermediate servers and changes in bandwidth during data transmission, service reliability becomes difficult to guarantee. Coupled with changes in the amount of data in a service, the above three problems all make the existing reliability evaluation methods no longer accurate. To study the effect of distributed service deployment strategies under such a background, this paper proposes a reliability evaluation method (REMR) based on lower boundary rule under time constraint to study the degree of the rationality of a service deployment plan combined with DECN. In this scenario, time delay is the main concern which would be affected by three quantitative factors: data packet storing and sending time, data transmission time and the calculation time of executing sub-tasks on the node devices, specially while the last two are in dynamic scenarios. In actual calculation, based on the idea of the minimal paths, the solution set would to be found that can meet requirements in the current deployment. Then the reliability of the service supported by the solution sets would be found out based on the principle of inclusion-exclusion combined with the distribution of available data transmission bandwidth and the distribution of node available computing resources. Besides a illustrative example was provided, to verify the calculated reliability of the designed service deployment plan, the NS3 is utilized along with Google cluster data set for simulation.