{"ID":2851162,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20506","arxiv_id":"2510.20506","title":"Morpheus: Lightweight RTT Prediction for Performance-Aware Load Balancing","abstract":"Distributed applications increasingly demand low end-to-end latency, especially in edge and cloud environments where co-located workloads contend for limited resources. Traditional load-balancing strategies are typically reactive and rely on outdated or coarse-grained metrics, often leading to suboptimal routing decisions and increased tail latencies. This paper investigates the use of round-trip time (RTT) predictors to enhance request routing by anticipating application latency. We develop lightweight and accurate RTT predictors that are trained on time-series monitoring data collected from a Kubernetes-managed GPU cluster. By leveraging a reduced set of highly correlated monitoring metrics, our approach maintains low overhead while remaining adaptable to diverse co-location scenarios and heterogeneous hardware. The predictors achieve up to 95% accuracy while keeping the prediction delay within 10% of the application RTT. In addition, we identify the minimum prediction accuracy threshold and key system-level factors required to ensure effective predictor deployment in resource-constrained clusters. Simulation-based evaluation demonstrates that performance-aware load balancing can significantly reduce application RTT and minimize resource waste. These results highlight the feasibility of integrating predictive load balancing into future production systems.","short_abstract":"Distributed applications increasingly demand low end-to-end latency, especially in edge and cloud environments where co-located workloads contend for limited resources. Traditional load-balancing strategies are typically reactive and rely on outdated or coarse-grained metrics, often leading to suboptimal routing decisi...","url_abs":"https://arxiv.org/abs/2510.20506","url_pdf":"https://arxiv.org/pdf/2510.20506v1","authors":"[\"Panagiotis Giannakopoulos\",\"Bart van Knippenberg\",\"Kishor Chandra Joshi\",\"Nicola Calabretta\",\"George Exarchakos\"]","published":"2025-10-23T12:49:28Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[]","has_code":false}