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This paper introduces a risk-aware edge server selection framework that minimizes service-level objective (SLO) violations while maintaining decision stability. The framework uses predictive mean and uncertainty of network latency to estimate SLO violation risk, employing a Normal approximation and Cantelli bound for risk evaluation. Experiments on an edge testbed demonstrate a reduction in deadline-miss rate from 39% to 34% and switching frequency from 46% to 5.5% compared to a mean-only baseline, all while maintaining sub-SLO average latency.
Reduce deadline misses and server switching by explicitly accounting for tail risk and stability in edge server selection.
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.
