An Energy-Aware Resource Allocation Heuristics for VM Scheduling in Cloud

Energy consumption has become a major concern to the widespread deployment of cloud data centers. Many techniques have been devised to help reduce energy consumption for cloud data centers that consist of a large number of identical servers, including dynamic allocation of active servers, consolidating diverse applications, and adjusting the CPU frequency of an active server. However, these techniques normally have a high migration and low resource utilization. CPU and memory is the dominant factors of the performance and energy consumption and whose allocation determines the energy efficiency of cloud system. Leveraging these techniques, we focus on the problem of VM placement, propose a heuristic greedy algorithm to implement VM deployment and live migration to maximize total resource utilization and minimize energy consumption, which is based on energy-aware and quadratic exponential smoothing method to predict the workloads. Our heuristic algorithm makes CPU-intensive services and memory-intensive services mapped to the same physical server more complementary. The experiment results show that there is significant improvement in the aspect of energy saving, workload balancing and scalability, compared with single-objective approaches based on CPU utilization.