Morpheus: Lightweight RTT Prediction for Performance-Aware Load Balancing
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908860921413632 |
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| author | Giannakopoulos, Panagiotis van Knippenberg, Bart Joshi, Kishor Chandra Calabretta, Nicola Exarchakos, George |
| author_facet | Giannakopoulos, Panagiotis van Knippenberg, Bart Joshi, Kishor Chandra Calabretta, Nicola Exarchakos, George |
| contents | 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 Traditional load-balancing strategies are typically reactive and rely on outdated or coarse-grained metrics, often leading to suboptimal routing decisions and increased tail latencies.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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20506 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Morpheus: Lightweight RTT Prediction for Performance-Aware Load Balancing Giannakopoulos, Panagiotis van Knippenberg, Bart Joshi, Kishor Chandra Calabretta, Nicola Exarchakos, George Distributed, Parallel, and Cluster Computing 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. |
| title | Morpheus: Lightweight RTT Prediction for Performance-Aware Load Balancing |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2510.20506 |