Energy-aware scheduling scheme using workload-aware consolidation technique in cloud data centres

Abstract

To reduce energy consumption in cloud data centres, in this paper, we propose two algorithms called the Energy-aware Scheduling algorithm using Workload-aware Consolidation Technique (ESWCT) and the Energy-aware Live Migration algorithm using Workload-aware Consolidation Technique (ELMWCT). As opposed to traditional energy-aware scheduling algorithms, which often focus on only one-dimensional resource, the two algorithms are based on the fact that multiple resources (such as CPU, memory and network bandwidth) are shared by users concurrently in cloud data centres and heterogeneous workloads have different resource consumption characteristics. Both algorithms investigate the problem of consolidating heterogeneous workloads. They try to execute all Virtual Machines (VMs) with the minimum amount of Physical Machines (PMs), and then power off unused physical servers to reduce power consumption. Simulation results show that both algorithms efficiently utilise the resources in cloud data centres, and the multidimensional resources have good balanced utilizations, which demonstrate their promising energy saving capability.