EcoUp: Towards Economical Datacenter Upgrading

doi:10.1109/TPDS.2015.2477827

	EcoUp: Towards Economical Datacenter Upgrading
	Yan, Guihai; Ma, Jun; Han, Yinhe; Li, Xiaowei
	2016-07-01
发表期刊	IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
ISSN	1045-9219
卷号	27 期号:7 页码:1968-1981
摘要	The rapid growth of cloud services dictates increasingly powerful datacenters to maintain the high quality of service (QoS). It's a common practice in virtually all tiers of datacenters to continuously upgrade the datacenters, i.e. replacing outdated and failed servers with more advanced and efficient ones. However, how to upgrade a datacenter in the most cost-efficient strategy remains unclear, and however this problem goes increasingly challenging given the great diversity of applications. In practice, the datacenters' operators usually resort to expending the scale of servers. The preferred servers are either expensive but high-performance, or, by contrast, cheap but low-power. Whatever sever preferences, how to justify the cost-efficiency is still an open problem. We claim that a cost-efficient upgrading strategy should be fully aware of not only the capacity and cost of various servers, but also the resource demands of target applications. We model this strategy as a recommendation problem: recommending the "best" servers to a datacenter. We propose "EcoUp", a model-based framework that faithfully rates the cost efficiency of server candidates, relying on which an optimal server portfolio can be derived. The performance prediction on candidate servers is realized by employing a sophisticated latent factor model (LFM). The cost mainly involves the server purchasing cost and energy bill. Given the application distribution, EcoUp can give an optimal server portfolio under a certain capital budget. We use Google trace, a big profiling dataset opened by Google, to validate the performance prediction. Experimental results show that the error rate is below 8 percent on average. Meanwhile, we build a comprehensive upgrading procedure on a local cluster to evaluate the potential of EcoUp. The results show that our approach significantly outperforms two conventional upgrading strategies by 12.3 and 33.6 percent in terms of system throughput, respectively.
关键词	Datacenter upgrading cost efficiency performance prediction recommender systems collaborative filtering
DOI	10.1109/TPDS.2015.2477827
收录类别	SCI
语种	英语
资助项目	National Basic Research Program of China (973)[2011CB302503] ; NSFC[61100016] ; NSFC[61221062] ; NSFC[61376043] ; NSFC[61432017] ; NSFC[61572470] ; NSFC[61532017]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000378263800009
出版者	IEEE COMPUTER SOC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8346
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Yan, Guihai
作者单位	Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yan, Guihai,Ma, Jun,Han, Yinhe,et al. EcoUp: Towards Economical Datacenter Upgrading[J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,2016,27(7):1968-1981.
APA	Yan, Guihai,Ma, Jun,Han, Yinhe,&Li, Xiaowei.(2016).EcoUp: Towards Economical Datacenter Upgrading.IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,27(7),1968-1981.
MLA	Yan, Guihai,et al."EcoUp: Towards Economical Datacenter Upgrading".IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 27.7(2016):1968-1981.