文献类型：期刊文献

英文题名：Bulk Savings for Bulk Transfers: Minimizing the Energy-Cost for Geo-Distributed Data Centers

作者：Lu, Xingjian[1];Kong, Fanxin[2];Liu, Xue[3];Yin, Jianwei[4];Xiang, Qiao[5];Yu, Huiqun[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Univ Penn, Dept Comp & Informat Sci, 200 S 33Rd St, Philadelphia, PA 19104 USA;[3]McGill Univ, Sch Comp Sci, Montreal, PQ H3A 2A7, Canada;[4]Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China;[5]Yale Univ, Dept Comp Sci, POB 2158, New Haven, CT 06520 USA

年份：2020

卷号：8

期号：1

起止页码：73

外文期刊名：IEEE TRANSACTIONS ON CLOUD COMPUTING

收录：;EI(收录号：20173504105957);WOS:【SCI-EXPANDED(收录号:WOS:000519700800007)】；

基金：This work was partially supported by the NSF of China under grant No. 61602175, the National Key Research and Development Plan under grant No. 2016YFA0502300, the National Science and Technology Supporting Program of China under grant No. 2015BAH18F02, the Model Information Service Industry Program of Guangdong Province under grunt No. GDEID2010IS049, the Fundamental Research Funds for the Central Universities under grant Nos. 222201514331 and ZH1726108, and the Special Funds for Informatization Development in Shanghai under grant No. 201602008.

语种：英文

外文关键词：Data center; bulk data transfer; energy-cost; geographical load balance

摘要：With the fast proliferation of cloud computing, major cloud service providers, e.g., Amazon, Google, Facebook, etc., have been deploying more and more geographically distributed data centers to provide customers with better reliability and quality of services. A basic demand in such a geo-distributed data center system is to transfer bulk volumes of data from one data center to another. Geographic distribution and large delay-tolerance of such inter-data-center bulk data transfers provide cloud service providers opportunities to optimize the operating cost. Most existing studies on inter-data-center bulk data transfers focus on minimizing the network bandwidth cost. However, the energy-cost of the bulk data transfers, which also accounts for a large proportion of operating cost in the data centers, still remains unexplored. This is an important problem, especially in the multi-electricity-market environment, where the electricity price exhibits both spatial and temporal diversities. In this paper, we systematically study the problem of how to route and schedule inter-data-center bulk data transfers to minimize the energy-cost for geo-distributed data centers. We model this problem as a min-cost multi-commodity flow problem and develop an efficient two-stage optimization method to solve it. Extensive evaluations with real-life inter-data-center network and electricity prices show that our method brings significant energy-cost savings over existing bulk data transfer methods.