一句话：通过负载感知服务放置与动态功耗重塑，缓解数据中心功耗碎片化，提升服务器密度和吞吐。
云计算与 Web 服务增长要求更多算力，但数据中心电力基础设施限制服务器扩容：峰值预留、流量波动与多级供电造成功耗预算碎片化和低利用率。作者基于 Facebook 数据中心功耗观察，提出利用服务功耗模式异质性，通过重分配服务重塑各供电节点的功耗曲线。方法将峰值时间异步的服务组合到同一节点，并设计负载感知服务放置框架，在供电树中均匀分散同步功耗模式实例以降低节点峰值；再以动态功耗重塑利用释放的余量。基于真实生产负载与功耗轨迹的实验显示，可在不改供电设施下多承载最多 13% 机器；结合动态重塑，延迟关键与批处理服务吞吐量最多提升 15% 和 11%，能量余量减少最多 44%。
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.
DOI 原文 ·
@inproceedings{paperbot4302,
title = {SmoothOperator},
author = {Chang-Hong Hsu and Qingyuan Deng and Jason Mars and Lingjia Tang},
booktitle = {ASPLOS},
year = {2018},
doi = {10.1145/3173162.3173190}
}