(untitled) (The Science Post, 2025)
This research investigates the critical impact of network latency and throughput on the efficiency of load-sharing algorithms in distributed computing systems. While traditional load-balancing strategies often rely on instantaneous state information, they frequently fail to account for the stochastic nature of the Load-Sharing Window (LSW) the duration during which a node remains in a specific state. This paper develops a mathematical framework using M/M/1 queueing models to numerically evaluate the probability of successful job transfers under varying traffic intensities (ρ) and communication delays. Our analysis identifies two primary failure modes: Transfer in Vain (TIV), where the receiver becomes busy before the 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 probability distribution 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-blind algorithms. The study concludes that incorporating the statistical confidence of the LSW into scheduling decisions is essential for maintaining system stability and maximizing resource utility in high-latency distributed environments.