A Systematic Design Space Exploration of Datacenter Schedulers
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Abstract
Datacenter infrastructure has become vital for stakeholders across industry, academia and government. To operate efficiently, datacenter operators rely on a variety of complex scheduling techniques, to distribute user workloads across resources. In this work, we leverage a reference architecture for datacenter scheduling to design and implement an instrument for systematic design space exploration of datacenter schedulers. We construct a formal representation of the design space for datacenter schedulers, using scheduling policies collected from real-world schedulers. We then use a genetic algorithm in combination with trace-based simulation to explore the space, optimizing for workload metrics. Through several experiments, we assess the viability of the instrument. We find that our instrument is able to identify patterns in the workloads and adapt the scheduling policies appropriately. Overall, our work leads to numerous findings, which can become valuable for future comprehension and development of schedulers.