Consolidating complementary VMs with spatial/temporal-awareness in cloud datacenters

In cloud datacenters, effective resource provisioning is needed to maximize energy efficiency and utilization of cloud resources while guaranteeing the Service Level Agreement (SLA) for tenants. Previous resource provisioning strategies either allocate physical resources to virtual machines (VMs) based on static VM resource demands or dynamically handle the variations in VM resource requirements through live VM migrations. However, the former fail to maximize energy efficiency and resource utilization while the latter produce high migration overhead. To handle these problems, we propose an initial VM allocation mechanism that consolidates complementary VMs with spatial/temporal-awareness. Complementary VMs are the VMs whose total demand of each resource dimension (in the spatial space) nearly reaches their host's capacity during VM lifetime period (in the temporal space). Based on our observation of the existence of VM resource utilization patterns, the mechanism predicts the lifetime resource utilization patterns of short-term VMs or periodical resource utilization patterns of long-term VMs. Based on the predicted patterns, it coordinates the requirements of different resources and consolidates complementary VMs in the same physical machine (PM). This mechanism reduces the number of PMs needed to provide VM service hence increases energy efficiency and resource utilization and also reduces the number of VM migrations and SLA violations. Simulation based on two real traces and real-world testbed experiments show that our initial VM allocation mechanism significantly reduces the number of PMs used, SLA violations and VM migrations of the previous resource provisioning strategies.