A Fast and Scalable Feedback-Driven Scheduler for Datacenter Applications
Microsecond-scale datacenter applications demand strict latency guarantees while operating under high load and variable service times. This environment often involves a mix of extremely short and long requests, where short requests — lasting just a few microseconds — are frequently delayed by longer...
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| Format: | master thesis |
| Status: | Published version |
| Publication Date: | 2025 |
| Country: | Brasil |
| Institution: | Universidade Federal de Mato Grosso do Sul (UFMS) |
| Repository: | Repositório Institucional da UFMS |
| Language: | Portuguese |
| OAI Identifier: | oai:repositorio.ufms.br:123456789/12732 |
| Online Access: | https://repositorio.ufms.br/handle/123456789/12732 |
| Access Level: | Open access |
| Keyword: | Datacenter Head-of-Line Blocking User-Level Scheduler |
| Summary: | Microsecond-scale datacenter applications demand strict latency guarantees while operating under high load and variable service times. This environment often involves a mix of extremely short and long requests, where short requests — lasting just a few microseconds — are frequently delayed by longer ones due to Head-of-Line (HOL) blocking, leading to higher latencies, especially at the tail. However, existing approaches to mitigate HOL blocking, such as centralized dispatching, fine-grained preemption, and resource reservation, face fundamental scalability limitations. This work introduces Synergy, a cooperative, application-aware scheduling system that uses direct feedback from applications to prioritize short requests, dynamically adapts scheduling parameters, and avoids unnecessary preemptions. Synergy adopts a decentralized architecture with distributed queues, job-aware preemption, and dynamic quantum sizing. By eliminating centralized classification and using real-time application measurements, Synergy effectively mitigates HOL blocking without compromising throughput. Synergy outperforms state-of-the-art systems, achieving up to 43% higher throughput while meeting microsecond-scale service-level objectives. |
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