Optimizing virtual machine consolidation in virtualized datacenters using resource sensitivity

In virtualized datacenters (vDCs), dynamic consolidation of virtual machines (VMs) is used to achieve both energy-efficiency and load balancing among different physical machines (PMs). Using VM live migrations, we can consolidate VMs on a smaller number of hosts to power down unused PMs and save energy. Most migration schemes are however oblivious to the characteristics of services that run inside VMs, and thus may lead to migrations where VMs competing for the same resource type are packed on the same PM. As a result, VMs may suffer from significant resource contention and noticeable degradation in their performance. Using resource sensitivity values of VMs (i.e., quantitative measures to reflect how much a VM is sensitive to its requested resources such as CPU, Mem, and Disk), we have designed a novel VM consolidation approach to optimize placement of VMs on available PMs. We validated our approach using five well-known applications/benchmarks with various resource demand signatures: Varying from pure CPU/Mem/Disk-intensive to mixtures of them. Our extensive numerical evaluation illustrates that, for the same power consumption, our approach improve the performance of cloud services by 9 - 12%, on average, when compared with current sensitivity oblivious approaches.