Dynamic Edge Server Selection in Time-Varying Environments: A Reliability-Aware Predictive Approach

TL;DR

This paper introduces MO-HAN, a lightweight, reliability-aware server selection method for edge computing that predicts latency and adaptively manages handovers to reduce latency and handover frequency in dynamic environments.

Contribution

It presents a novel predictive approach combining latency estimation and reliability metrics with hysteresis to improve edge server selection.

Findings

MO-HAN reduces mean and tail latencies compared to static baselines.

Handover frequency is nearly halved with MO-HAN.

The method is practical for resource-constrained embedded devices.

Abstract

Latency-sensitive embedded applications increasingly rely on edge computing, yet dynamic network congestion in multi-server architectures challenges proper edge server selection. This paper proposes a lightweight server-selection method for edge applications that fuses latency prediction with adaptive reliability and hysteresis-based handover. Using passive measurements (arrival rate, utilization, payload size) and an exponentially modulated rational delay model, the proposed Moderate Handover (MO-HAN) method computes a score that balances predicted latency and reliability to ensure handovers occur only when the expected gain is meaningful and maintain reduced end-to-end latency. Results show that MO-HAN consistently outperforms static and fair-distribution baselines by lowering mean and tail latencies, while reducing handovers by nearly 50% compared to pure opportunistic selection. These gains arise without intrusive instrumentation or heavy learning infrastructure, making MO-HAN practical for resource-constrained embedded devices.