Pigeon: an Effective Distributed, Hierarchical Datacenter Job Scheduler
Pigeon: an Effective Distributed, Hierarchical Datacenter Job Scheduler
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DOI：
10.1145/3357223.3362728
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发表时间：
2019-11
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影响因子：
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通讯作者：
Zhijun Wang;Huiyang Li;Zhongwei Li;Xiaocui Sun;J. Rao;Hao Che;Hong Jiang
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文献类型：
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作者：
Zhijun Wang;Huiyang Li;Zhongwei Li;Xiaocui Sun;J. Rao;Hao Che;Hong Jiang
在当今的数据中心中，工作异质性使调度程序很难同时满足延迟需求并保持高度资源利用。包括集中式，分布式和混合计划器在内的最先进的数据中心调度程序无法确保在大规模和高度加载系统中短期工作的潜伏期低。关键问题是集中调度程序中的可伸缩性，在分布式和混合调度程序中均无效且效率低下的探测和资源共享。在本文中，我们提出了基于两层设计的分布式层次工作调度程序Pigeon。鸽子将工人分为小组，每个工人由一个单独的主人管理。在鸽子到达后，分布式调度程序直接在最低的工作处理开销的大师中直接分配任务，因此可以保留最高的可扩展性。同时，每个主人都在集中管理和分发所有收到的任务，忽略了工作环境，从而使工人池在小组级别完全共享，以最大程度地提高多重增益。为了最大程度地减少在线障碍的机会，避免长期工作的饥饿，每个主人都使用两个加权的公平队列来适应短期和长期工作的任务，并保留一小部分工人短期工作。通过理论分析，痕量驱动的模拟和原型实现的评估表明，鸽子的表现明显优于代表性分布式调度程序的麻雀，而混合调度程序Eagle。
In today's datacenters, job heterogeneity makes it difficult for schedulers to simultaneously meet latency requirements and maintain high resource utilization. The state-of-the-art datacenter schedulers, including centralized, distributed, and hybrid schedulers, fail to ensure low latency for short jobs in large-scale and highly loaded systems. The key issues are the scalability in centralized schedulers, ineffective and inefficient probing and resource sharing in both distributed and hybrid schedulers. In this paper, we propose Pigeon, a distributed, hierarchical job scheduler based on a two-layer design. Pigeon divides workers into groups, each managed by a separate master. In Pigeon, upon a job arrival, a distributed scheduler directly distribute tasks evenly among masters with minimum job processing overhead, hence, preserving highest possible scalability. Meanwhile, each master manages and distributes all the received tasks centrally, oblivious of the job context, allowing for full sharing of the worker pool at the group level to maximize multiplexing gain. To minimize the chance of head-of-line blocking for short jobs and avoid starvation for long jobs, two weighted fair queues are employed in each master to accommodate tasks from short and long jobs, separately, and a small portion of the workers are reserved for short jobs. Evaluation via theoretical analysis, trace-driven simulations, and a prototype implementation shows that Pigeon significantly outperforms Sparrow, a representative distributed scheduler, and Eagle, a hybrid scheduler.