Risk-Aware and Stable Edge Server Selection Under Network Latency SLOs

TL;DR

This paper introduces a lightweight, risk-aware decision framework for dynamic edge server selection that improves latency adherence and reduces switching oscillations in network latency-critical applications.

Contribution

It presents a novel approach combining risk evaluation and hysteresis-based stability to enhance edge server selection under latency SLOs.

Findings

Reduces deadline-miss rate from 39% to 34%.

Decreases switching frequency from 46% to 5.5%.

Maintains sub-SLO average latency (~0.45s).

Abstract

We present a lightweight and interpretable decision framework for dynamic edge server selection in latency-critical applications that explicitly accounts for tail risk and switching stability. Each candidate server is characterised by predictive mean and uncertainty summaries of network latency, which are used to estimate the risk of service-level objective (SLO) violations and to guide selection. Risk is evaluated using a tight Normal approximation complemented by a conservative Cantelli bound, while percentile-based scoring coupled with hysteresis stabilizes decisions and suppresses oscillatory switching under short-lived network fluctuations. Experimental results on a multi-server edge testbed with a strict SLO of $\tau = 0.5$\,s show that the proposed approach reduces the deadline-miss rate from 39\% to 34\% compared to a mean-only baseline, while reducing switching frequency from 46\% to 5.5\% ($\approx$88\% reduction) and maintaining sub-SLO average latency ($\approx$0.45\,s). These results demonstrate that explicit risk evaluation combined with stability-preserving control enables practical and robust adaptive server selection in dynamic edge environments.