Traffic-aware virtual machine migration in topology-adaptive DCN

Abstract

Virtual machine (VM) migration is a key technique for network resource optimization in modern data center networks (DCNs). Previous work generally focuses on how to place the VMs efficiently in a static network topology by migrating the VMs with large traffic demands to close servers. When the VM demands change, however, a great cost will be paid on the VM migration. With the advance of software-defined network (SDN), recent studies have shown great potential to implement an adaptive network topology at a low cost. Taking advantage of the topology adaptability, in this paper, we propose a new paradigm for VM migration by dynamically constructing a topology based on the VM demands to lower the cost of both VM migration and communication. We formulate the traffic-aware VM migration problem in an adaptive topology and show its NP-hardness. Then we develop a novel progressive-decompose-rounding (PDR) algorithm to solve this problem in polynomial time with a proved approximation ratio. Extensive trace-based simulations show that PDR can achieve higher flow throughput among VMs with only a quarter of the migration cost compared to other state-of-art VM migration solutions. We finally implement an OpenvSwitch-based testbed and demonstrate the efficiency of our solution.