Goldilocks: Adaptive Resource Provisioning in Containerized Data Centers
Goldilocks: Adaptive Resource Provisioning in Containerized Data Centers
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DOI：
10.1109/icdcs.2019.00072
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Liang Zhou;L. Bhuyan;K. K. Ramakrishnan-K.
中科院分区：
文献类型：
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作者：
Liang Zhou;L. Bhuyan;K. K. Ramakrishnan-K.
由于工作负载波动和严格的任务完成时间要求，数据中心的电源管理具有挑战性。最近的资源供应系统，如Borg和RC通知的，将任务打包在服务器上以节省电力。然而，当前基于打包的功率优化框架几乎没有为峰值留出净空空间，并且任务完成时间受到影响。在本文中，我们设计了一个新颖的资源供应系统金发姑娘，它通过将任务分配到组中的服务器来优化功率和任务完成时间。容器中承载的任务通过运行图分区算法分组在一起。频繁通信的容器被放置在一起，从而缩短了任务完成时间。我们还利用有关现代服务器功耗的新发现，确保它们的利用率处于与功率成比例的范围内。试验床实施测量和大规模跟踪驱动模拟都证明，金发姑娘在数据中心节能方面的表现优于以往的所有工作。根据工作负载的不同，金发可节省11.7%-26.2%的电力，而最好的替代方案Borg节省8.9%-22.8%。金发姑娘的Twitter内容缓存工作负载的每个请求的能量只有RC通知的33%。最后，就任务完成时间而言，最好的替代方案E-PVM在不同工作负载下的任务完成时间是金发女孩的1.17-3.29倍。
Power management in data centers is challenging because of fluctuating workloads and strict task completion time requirements. Recent resource provisioning systems, such as Borg and RC-Informed, pack tasks on servers to save power. However, current power optimization frameworks based on packing leave very little headroom for spikes, and the task completion times are compromised. In this paper, we design Goldilocks, a novel resource provisioning system for optimizing both power and task completion time by allocating tasks to servers in groups. Tasks hosted in containers are grouped together by running a graph partitioning algorithm. Containers communicating frequently are placed together, which improves the task completion times. We also leverage new findings on power consumption of modern-day servers to ensure that their utilizations are in a range where they are power-proportional. Both testbed implementation measurements and large-scale trace-driven simulations prove that Goldilocks outperforms all the previous works on data center power saving. Goldilocks saves power by 11.7%-26.2% depending on the workload, whereas the best of the implemented alternatives, Borg, saves 8.9%-22.8%. The energy per request for the Twitter content caching workload in Goldilocks is only 33% of RC-Informed. Finally, the best alternative in terms of task completion time, E-PVM, has 1.17-3.29 times higher task completion times than Goldilocks across different workloads.