Pocolo:功率受限环境下的功率优化主机托管

Iyswarya Narayanan, Adithya Kumar, A. Sivasubramaniam
{"title":"Pocolo:功率受限环境下的功率优化主机托管","authors":"Iyswarya Narayanan, Adithya Kumar, A. Sivasubramaniam","doi":"10.1109/IISWC50251.2020.00010","DOIUrl":null,"url":null,"abstract":"There is a considerable amount of prior effort on co-locating applications on datacenter servers for boosting resource utilization. However, we note that it is equally important to take power into consideration from the co-location viewpoint. Applications can still interfere on power in stringent power constrained infrastructures, despite no direct resource contention between the coexisting applications. This becomes particularly important with dynamic load variations, where even if the power capacity is tuned for the peak load of an application, co-locating another application with it during its off-period can lead to overshooting of the power capacity. Therefore, to extract maximum returns on datacenter infrastructure investments one needs to jointly handle power and server resources. We explore this problem in the context of a private-cloud cluster which is provisioned for a primary latency-critical application, but also admits secondary best-effort applications to improve utilization during off-peak periods. Our solution, Pocolo, draws on principles from economics to reason about resource demands in power constrained environments and provides answers to the when/where/what questions pertaining to co-location. We implement Pocolo on a Linux cluster to demonstrate its performance and cost benefits over a number of latency-sensitive and best-effort datacenter workloads.","PeriodicalId":365983,"journal":{"name":"2020 IEEE International Symposium on Workload Characterization (IISWC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Pocolo: Power Optimized Colocation in Power Constrained Environments\",\"authors\":\"Iyswarya Narayanan, Adithya Kumar, A. Sivasubramaniam\",\"doi\":\"10.1109/IISWC50251.2020.00010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There is a considerable amount of prior effort on co-locating applications on datacenter servers for boosting resource utilization. However, we note that it is equally important to take power into consideration from the co-location viewpoint. Applications can still interfere on power in stringent power constrained infrastructures, despite no direct resource contention between the coexisting applications. This becomes particularly important with dynamic load variations, where even if the power capacity is tuned for the peak load of an application, co-locating another application with it during its off-period can lead to overshooting of the power capacity. Therefore, to extract maximum returns on datacenter infrastructure investments one needs to jointly handle power and server resources. We explore this problem in the context of a private-cloud cluster which is provisioned for a primary latency-critical application, but also admits secondary best-effort applications to improve utilization during off-peak periods. Our solution, Pocolo, draws on principles from economics to reason about resource demands in power constrained environments and provides answers to the when/where/what questions pertaining to co-location. We implement Pocolo on a Linux cluster to demonstrate its performance and cost benefits over a number of latency-sensitive and best-effort datacenter workloads.\",\"PeriodicalId\":365983,\"journal\":{\"name\":\"2020 IEEE International Symposium on Workload Characterization (IISWC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Symposium on Workload Characterization (IISWC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IISWC50251.2020.00010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Symposium on Workload Characterization (IISWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IISWC50251.2020.00010","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

为了提高资源利用率,之前在数据中心服务器上共同定位应用程序方面做了大量的工作。然而,我们注意到,从共址的角度考虑功率也同样重要。尽管共存的应用程序之间没有直接的资源争用,但在严格的电力约束基础设施中,应用程序仍然可能干扰电力。对于动态负载变化,这一点变得尤为重要,即使针对应用程序的峰值负载调整了功率容量,但在其停机期间将另一个应用程序与它放在一起也可能导致功率容量过调。因此,为了获得数据中心基础设施投资的最大回报,需要联合处理电源和服务器资源。我们在私有云集群的上下文中探讨了这个问题,私有云集群是为主要的延迟关键型应用程序提供的,但也允许次要的尽最大努力的应用程序来提高非高峰期间的利用率。我们的解决方案Pocolo借鉴了经济学原理来解释电力受限环境中的资源需求,并提供了与托管相关的时间/地点/内容问题的答案。我们在Linux集群上实现Pocolo,以演示它在许多延迟敏感和尽力而为的数据中心工作负载上的性能和成本优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pocolo: Power Optimized Colocation in Power Constrained Environments
There is a considerable amount of prior effort on co-locating applications on datacenter servers for boosting resource utilization. However, we note that it is equally important to take power into consideration from the co-location viewpoint. Applications can still interfere on power in stringent power constrained infrastructures, despite no direct resource contention between the coexisting applications. This becomes particularly important with dynamic load variations, where even if the power capacity is tuned for the peak load of an application, co-locating another application with it during its off-period can lead to overshooting of the power capacity. Therefore, to extract maximum returns on datacenter infrastructure investments one needs to jointly handle power and server resources. We explore this problem in the context of a private-cloud cluster which is provisioned for a primary latency-critical application, but also admits secondary best-effort applications to improve utilization during off-peak periods. Our solution, Pocolo, draws on principles from economics to reason about resource demands in power constrained environments and provides answers to the when/where/what questions pertaining to co-location. We implement Pocolo on a Linux cluster to demonstrate its performance and cost benefits over a number of latency-sensitive and best-effort datacenter workloads.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信