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Traffic-Aware VDC Embedding in data center： A Case Study of FatTree

China Communications 2014年第7期11卷 142-152页

作者： LUO Shouxi YU Hongfang LI Lemin LIAO Dan SUN Gang 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

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.

关键词： virtual data center embedding switch capacity fat-tree

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Multi-Dimensional Aware Scheduling for Co-optimizing Utilization in data center

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China Communications 2011年第6期8卷 19-27页

作者：孙鑫徐鹏双锴苏森 State Key Laboratory of Networking and Switching Techology Beijing University of Posts and Telecommunications

Resource Scheduling is crucial to data centers. However, most previous works focus only on one-dimensional resource models which ignoring the fact that multiple resources simultaneously utilized, including CPU, memory and network bandwidth. As cloud computing allows uncoordinated and heterogeneous users to share a data center, competition for multiple resources has become increasingly severe. Motivated by the differences on integrated utilization obtained from different packing schemes, in this paper we take the scheduling problem as a multi-dimensional combinatorial optimization problem with constraint satisfaction. With NP hardness, we present Multiple attribute decision based Integrated Resource Scheduling (MIRS), and a novel heuristic algorithm to gain the approximate optimal solution. Refers to simulation results, in face of various workload sets, our algorithm has significant superiorities in terms of efficiency and performance compared with previous methods.

关键词： virtual data center resource scheduling multiple attribute decision making efficiency performance

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