Fast resource scaling in elastic clusters with an agile method for demand estimation

doi:10.1016/j.suscom.2018.03.001

	Fast resource scaling in elastic clusters with an agile method for demand estimation
	Hu, Cheng 1; Deng, Yuhui 1,2
	2018-09-01
发表期刊	SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS
ISSN	2210-5379
卷号	19 页码:165-173
摘要	For energy saving, elastic clusters are introduced to cut back the energy wasted on powering unused servers. In an elastic cluster, the number of working servers, or called resources, is dynamically scaled based on resource demand of workload. However, many traditional scaling methods are unaware of an exact resource demand of workload. They gradually scale resources according to current service level with loose demand estimations or even with no estimation. Additionally, to provide the ability to make precise demand estimations, some other methods are proposed. They artificially represent system situation with a general model, but the model may not well reflect the reality because it is often difficult to describe the real situation of a system. For both of these methods, resources cannot be exactly scaled to the demand when demand changes, and there is a time delay before resources are scaled to the demand. This scaling delay will incur a performance degradation when workload increase, and will cause an energy waste when workload decrease. In this paper, we strive to efficiently estimate the actual demand of workload and achieve fast resource scaling in elastic clusters. Unlike traditional methods which make great efforts to understand the complex system situation, we only concentrate on the information of past actual resource demands. This information is actually the most straightforward and valid reflection to the real situation of a specific system, so it contains valuable knowledge for estimating the actual resource demand of new incoming workload. Therefore, we propose an agile method to directly estimate resource demand based on that knowledge, thus achieving a high accuracy. Specifically, our method directly learns that knowledge through a learning method-random forests, so it does not need artificial system analyses which are both complex and time-consuming. In addition, it is efficient to build random forests and make resource estimations in our method. Thus, our method can be efficiently and agilely performed in elastic clusters to reduce the scaling delay and achieve fast resource scaling. (C) 2018 Elsevier Inc. All rights reserved.
关键词	Green computing Elastic cluster Demand estimation Resource scaling Resource management
DOI	10.1016/j.suscom.2018.03.001
收录类别	SCI
语种	英语
资助项目	NSFC[61572232] ; Science and Technology Planning Project of Guangzhou[201604016100] ; Science and Technology Planning Project of Nansha[2016CX007] ; Open Research Fund of Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences[CARCH201705]
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Information Systems
WOS记录号	WOS:000446122000015
出版者	ELSEVIER SCIENCE BV
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/2XEOYT63/4805
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Deng, Yuhui
作者单位	1.Jinan Univ, Dept Comp Sci, Guangzhou 510632, Guangdong, Peoples R China 2.Chinese Acad Sci, Inst Comp, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Hu, Cheng,Deng, Yuhui. Fast resource scaling in elastic clusters with an agile method for demand estimation[J]. SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS,2018,19:165-173.
APA	Hu, Cheng,&Deng, Yuhui.(2018).Fast resource scaling in elastic clusters with an agile method for demand estimation.SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS,19,165-173.
MLA	Hu, Cheng,et al."Fast resource scaling in elastic clusters with an agile method for demand estimation".SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 19(2018):165-173.