SprintCon:数据中心服务器的可控高效计算冲刺

Wenli Zheng, Xiaorui Wang, Yue Ma, Chao Li, Hao Lin, Bin Yao, Jianfeng Zhang, M. Guo
{"title":"SprintCon:数据中心服务器的可控高效计算冲刺","authors":"Wenli Zheng, Xiaorui Wang, Yue Ma, Chao Li, Hao Lin, Bin Yao, Jianfeng Zhang, M. Guo","doi":"10.1109/IPDPS.2019.00090","DOIUrl":null,"url":null,"abstract":"Computational sprinting is an effective mechanism to temporarily boost the performance of data center servers. However, given the great effect on performance improvement, how to make the sprinting process controllable and how to maximize the sprinting efficiency have not been well discussed yet. Those can be significant problems for a data center when computational sprinting is needed for more than a few minutes, since it requires the support of energy storage, whose capacity is limited. The control and efficiency of sprinting not only involve how fast to run servers and how to allocate resources to co-running workloads, but also the impact on power overload, and how to handle the overload with circuit breakers and energy storage to ensure power safety. Different workloads can impact sprinting in different ways, and hence efficient sprinting requires workload-specific strategies. In this paper, we propose SprintCon to realize controllable and efficient computational sprinting for data center servers. SprintCon mainly consists of a power load allocator and two different power controllers. The allocator analyzes how to divide the power load to different power sources. The server power controller adapts the CPU cores that process batch workloads, to improve the efficiency in terms of computing, energy and cost. The UPS power controller dynamically adjusts the discharge rate of UPS energy storage to satisfy the time-varying power demand of interactive workloads, and ensure power safety. The experiment results show that compared to state-of-the-art solutions, SprintCon can achieve 6-56% better computing performance and up to 87% less demand of energy storage.","PeriodicalId":403406,"journal":{"name":"2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"SprintCon: Controllable and Efficient Computational Sprinting for Data Center Servers\",\"authors\":\"Wenli Zheng, Xiaorui Wang, Yue Ma, Chao Li, Hao Lin, Bin Yao, Jianfeng Zhang, M. Guo\",\"doi\":\"10.1109/IPDPS.2019.00090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Computational sprinting is an effective mechanism to temporarily boost the performance of data center servers. However, given the great effect on performance improvement, how to make the sprinting process controllable and how to maximize the sprinting efficiency have not been well discussed yet. Those can be significant problems for a data center when computational sprinting is needed for more than a few minutes, since it requires the support of energy storage, whose capacity is limited. The control and efficiency of sprinting not only involve how fast to run servers and how to allocate resources to co-running workloads, but also the impact on power overload, and how to handle the overload with circuit breakers and energy storage to ensure power safety. Different workloads can impact sprinting in different ways, and hence efficient sprinting requires workload-specific strategies. In this paper, we propose SprintCon to realize controllable and efficient computational sprinting for data center servers. SprintCon mainly consists of a power load allocator and two different power controllers. The allocator analyzes how to divide the power load to different power sources. The server power controller adapts the CPU cores that process batch workloads, to improve the efficiency in terms of computing, energy and cost. The UPS power controller dynamically adjusts the discharge rate of UPS energy storage to satisfy the time-varying power demand of interactive workloads, and ensure power safety. The experiment results show that compared to state-of-the-art solutions, SprintCon can achieve 6-56% better computing performance and up to 87% less demand of energy storage.\",\"PeriodicalId\":403406,\"journal\":{\"name\":\"2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPDPS.2019.00090\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPS.2019.00090","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

计算冲刺是一种暂时提高数据中心服务器性能的有效机制。然而,鉴于短跑对提高成绩的巨大作用,如何使短跑过程可控,如何使短跑效率最大化,目前还没有得到很好的讨论。当一个数据中心需要进行超过几分钟的计算冲刺时,这可能是一个严重的问题,因为它需要能量存储的支持,而能量存储的容量是有限的。短跑的控制和效率不仅涉及到服务器运行的速度有多快,如何将资源分配给协同运行的工作负载,还涉及到对电源过载的影响,以及如何通过断路器和储能来处理过载以确保电源安全。不同的工作负载会以不同的方式影响冲刺,因此高效的冲刺需要特定于工作负载的策略。为了实现数据中心服务器的可控、高效的计算冲刺,我们提出了SprintCon。SprintCon主要由一个电源负载分配器和两个不同的电源控制器组成。分配器分析如何将电力负荷分配给不同的电源。服务器电源控制器适配处理批量工作负载的CPU内核,从计算、能源和成本等方面提高效率。UPS电源控制器动态调节UPS储能的放电速率,满足交互式工作负载的时变功率需求,保证供电安全。实验结果表明,与目前最先进的解决方案相比,SprintCon的计算性能提高了6-56%,储能需求减少了87%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
SprintCon: Controllable and Efficient Computational Sprinting for Data Center Servers
Computational sprinting is an effective mechanism to temporarily boost the performance of data center servers. However, given the great effect on performance improvement, how to make the sprinting process controllable and how to maximize the sprinting efficiency have not been well discussed yet. Those can be significant problems for a data center when computational sprinting is needed for more than a few minutes, since it requires the support of energy storage, whose capacity is limited. The control and efficiency of sprinting not only involve how fast to run servers and how to allocate resources to co-running workloads, but also the impact on power overload, and how to handle the overload with circuit breakers and energy storage to ensure power safety. Different workloads can impact sprinting in different ways, and hence efficient sprinting requires workload-specific strategies. In this paper, we propose SprintCon to realize controllable and efficient computational sprinting for data center servers. SprintCon mainly consists of a power load allocator and two different power controllers. The allocator analyzes how to divide the power load to different power sources. The server power controller adapts the CPU cores that process batch workloads, to improve the efficiency in terms of computing, energy and cost. The UPS power controller dynamically adjusts the discharge rate of UPS energy storage to satisfy the time-varying power demand of interactive workloads, and ensure power safety. The experiment results show that compared to state-of-the-art solutions, SprintCon can achieve 6-56% better computing performance and up to 87% less demand of energy storage.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Distributed Weighted All Pairs Shortest Paths Through Pipelining SAFIRE: Scalable and Accurate Fault Injection for Parallel Multithreaded Applications Architecting Racetrack Memory Preshift through Pattern-Based Prediction Mechanisms Z-Dedup:A Case for Deduplicating Compressed Contents in Cloud Dual Pattern Compression Using Data-Preprocessing for Large-Scale GPU Architectures
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1