SmoothOperator:减少大规模数据中心的电力碎片和提高电力利用率

Chang-Hong Hsu, Qingyuan Deng, Jason Mars, Lingjia Tang
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引用次数: 36

摘要

随着云计算和web服务的日益普及,互联网公司需要增加计算能力来满足需求。然而,电力已经成为阻碍行业增长的主要限制因素:通常情况下,如果不超过现有电力基础设施的容量,就无法向数据中心添加更多的服务器。在这项工作中,我们首先调查了Facebook数据中心的电力使用情况。我们观察到,峰值电力供应、高波动流量和多层次电力输送基础设施的结合导致了严重的电力预算碎片化问题和低效的低电力利用率。为了解决这个问题,我们的见解是,不同服务之间的电力消耗模式的异质性提供了通过重新分配服务来重新塑造每个电源节点的电力配置的机会。通过对同一功率节点下具有异步峰值时间的服务进行分组,我们可以降低每个节点的峰值功率,从而创建更多的功率头空间,以允许托管更多的服务器,从而实现更高的吞吐量。基于此,我们开发了一个工作负载感知的服务放置框架,系统地将具有同步电源模式的服务实例均匀地分布在电源树下,从而大大降低了电源节点的峰值功耗。然后,我们利用动态功率轮廓重塑,以最大限度地利用我们的放置框架解锁的净空空间。我们基于实际生产工作负载和电源跟踪的实验表明,在不改变底层电源基础设施的情况下,我们能够在生产中托管多达13%的机器。利用动态重塑释放的功率余量,我们同时为延迟关键型服务和批处理服务实现了高达15%和11%的吞吐量提高,并减少了高达44%的能量松弛。
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SmoothOperator: Reducing Power Fragmentation and Improving Power Utilization in Large-scale Datacenters
With the ever growing popularity of cloud computing and web services, Internet companies are in need of increased computing capacity to serve the demand. However, power has become a major limiting factor prohibiting the growth in industry: it is often the case that no more servers can be added to datacenters without surpassing the capacity of the existing power infrastructure. In this work, we first investigate the power utilization in Facebook datacenters. We observe that the combination of provisioning for peak power usage, highly fluctuating traffic, and multi-level power delivery infrastructure leads to significant power budget fragmentation problem and inefficiently low power utilization. To address this issue, our insight is that heterogeneity of power consumption patterns among different services provides opportunities to re-shape the power profile of each power node by re-distributing services. By grouping services with asynchronous peak times under the same power node, we can reduce the peak power of each node and thus creating more power head-rooms to allow more servers hosted, achieving higher throughput. Based on this insight, we develop a workload-aware service placement framework to systematically spread the service instances with synchronous power patterns evenly under the power supply tree, greatly reducing the peak power draw at power nodes. We then leverage dynamic power profile reshaping to maximally utilize the headroom unlocked by our placement framework. Our experiments based on real production workload and power traces show that we are able to host up to 13% more machines in production, without changing the underlying power infrastructure. Utilizing the unleashed power headroom with dynamic reshaping, we achieve up to an estimated total of 15% and 11% throughput improvement for latency-critical service and batch service respectively at the same time, with up to 44% of energy slack reduction.
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