Energy-Aware Scheduling Scheme Using Workload-Aware Consolidation Technique in Cloud Data Centres

To reduce energy consumption in cloud data centres, in this paper, we propose two algorithms called the Energy-aware Sched- uling algorithm using Workload-aware Conso- lidation Technique （ESWCT） and the Energy- aware Live Migration algorithm using Work- load-aware Consolidation Technique （ELMWCT）. As opposed to traditional energy-aware sche- duling algorithms, which often focus on only one-dimensional resource, the two algorithms are based on the fact that multiple resources （su- ch as CPU, memory and network bandwidth） are shared by users concurrently in cloud data centres and heterogeneous workloads have diffe- rent resource consumption characteristics. Both algorithms investigate the problem of consoli- dating 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