Numerical Evaluation of Network Latency and Throughput in Distributed Systems (The Science Post, 2025)
This research investigates the critical impact of network latency and throughput on the efficiency of load-sharingalgorithms in distributed computing systems. While traditional load-balancing strategies often rely on instantaneousstate information, they frequently fail to account for the stochastic nature of the Load-Sharing Window (LSW) theduration during which a node remains in a specific state. This paper develops a mathematical framework usingM/M/1 queueing models to numerically evaluate the probability of successful job transfers under varying trafficintensities (ρ) and communication delays.Our analysis identifies two primary failure modes: Transfer in Vain (TIV), where the receiver becomes busy beforethe job arrives, and Transfer Unnecessary (TU), where the sender clears its own backlog during the transfer process.To mitigate these inefficiencies, we propose and evaluate a β -quantile decision rule that utilizes the probabilitydistribution of the LSW to filter out high-risk migrations. Numerical results demonstrate that in high-load scenarios(ρ= 0.9), the quantile-aware approach improves effective system throughput by up to 25% compared to latency-blindalgorithms. The study concludes that incorporating the statistical confidence of the LSW into scheduling decisions isessential for maintaining system stability and maximizing resource utility in high-latency distributed environments.