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. 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. As the flow demands between VMs change, however, a great cost will be paid for the VM migration. In this paper, we propose a new paradigm for VM migration by dynamically constructing adaptive topologies 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. For periodic traffic, we develop a novel progressive-decompose-rounding algorithm to schedule VM migration in polynomial time with a proved approximation ratio. For highly dynamic flows, we design an online decision-maker (ODM) algorithm with proved performance bound. Extensive trace-based simulations show that PDR and ODM can achieve about four times flow throughput among VMs with less than 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 solutions.