Traffic-Aware VDC Embedding in Data Center： A Case Study of FatTree

作     者：LUO Shouxi YU Hongfang LI Lemin LIAO Dan SUN Gang 

作者机构：Key Laboratory of Optical Fiber Sensing and Communications Ministry of Education University of Electronic Science and Technology of China Chengdu 611731 China Institute of Electronic and Information Engineering in Dongguan UESTC Dongguan 523808 China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2014年第11卷第7期

页      面：142-152页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 08[工学] 0835[工学-软件工程] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was partially supported by the National Grand Fundamental Research 973 Program of China under Grant (No. 2013CB329103)  Natural Science Foundation of China grant (No. 61271171)  the Fundamental Research Funds for the Central Universities (ZYGX2013J002  ZYGX2012J004  ZYGX2010J002  ZYGX2010J009)  Guangdong Science and Technology Project (2012B090500003  2012B091000163  2012556031) 

主　　题：virtual data center embedding switch capacity fat-tree 

摘      要：Virtualization is a common technology for resource sharing in data center. To make efficient use of data center resources, the key challenge is to map customer demands （modeled as virtual data center, VDC） to the physical data center effectively. In this paper, we focus on this problem. Distinct with previous works, our study of VDC embedding problem is under the assumption that switch resource is the bottleneck of data center networks （DCNs）. To this end, we not only propose relative cost to evaluate embedding strategy, decouple embedding problem into VM placement with marginal resource assignment and virtual link mapping with decided source-destination based on the property of fat-tree, but also design the traffic aware embedding algorithm （TAE） and first fit virtual link mapping （FFLM） to map virtual data center requests to a physical data center. Simulation results show that TAE＋FFLM could increase acceptance rate and reduce network cost （about 49% in the case） at the same time. The traffie aware embedding algorithm reduces the load of core-link traffic and brings the optimization opportunity for data center network energy conservation.