Liao Dan

Traffic-aware VDC embedding in data center: A case study of fattree

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 traffic aware embedding algorithm reduces the load of core-link traffic and brings the optimization opportunity for data center network energy conservation.