EcoUp: Towards Economical Datacenter Upgrading
文献类型：期刊论文
| 作者 | Yan, Guihai; Ma, Jun; Han, Yinhe; Li, Xiaowei |
| 刊名 | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
 |
| 出版日期 | 2016-07-01 |
| 卷号 | 27期号:7页码:1968-1981 |
| 关键词 | Datacenter upgrading cost efficiency performance prediction recommender systems collaborative filtering |
| ISSN号 | 1045-9219 |
| DOI | 10.1109/TPDS.2015.2477827 |
| 英文摘要 | 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 It's a common practice in virtually all tiers of datacenters to continuously upgrade the datacenters, i.e. replacing outdated and failed servers with more advanced and efficient ones.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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000378263800009 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/8345]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。