Multi-factor nature inspired SLA-aware energy efficient resource management for cloud environments

Cloud computing provides different types of resources to users on-demand which are hosted in cloud data centers. Aforesaid services are provided at the expense of large energy consumption. Energy consumption increases the expenditure budget, greenhouse gases, and CO2 emissions. To handle this issue, researchers have come up with various server-level energy-efficient techniques. Though the proposed techniques attempt to reduce energy consumption, they only consider the energy consumption of the CPU during the task placement process. However, researchers have recently noted that memory is also one of the higher energy consumption components and it should be considered in task placement. Moreover, existing techniques ignore the SLA violations that are encountered due to workload. To address the aforementioned issues, we propose two novel nature-inspired techniques which consider the energy consumption of both CPU and memory during the VM placement process. Proposed novel techniques are based on artificial bee colony and particle swarm optimization which haven’t been used to place VM while considering energy consumption of CPU and memory. Moreover, to handle the issue of resultant SLA violations, we also provide the SLA-aware variants of the proposed energy-efficient techniques, which try to lower SLA violations faced because of excessive task consolidation. The results depict that the proposed energy-efficient techniques perform better than the existing state-of-the-art techniques, whereas proposed SLA variants also reduce the SLA violations.