A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach

ACO ant colony optimization; CS cuckoo search; VSF voltage scaling factor; EcPSO extended compact particle swarm optimization Abstract Cloud computing slowly gained an important role in scientific application, on-demand facility of virtualized resources is provided as a service with the help of virtualization without any additional waiting time. Energy consumption is reduced for job scheduling problems based on makespan constraint which in turn leads to significant decrease in the energy cost. Additionally, there is an increase in complexity for scheduling problems mainly because the application is not based on makespan constraint. In this paper we propose a new Hybrid algorithm combining the benefits of ACO and cuckoo search algorithm. It is focused on the voltage scaling factor for reduction of energy consumption. Performance of the Hybrid algorithm is considerably increased from 45 tasks onward when compared to ACO. Energy consumed by Hybrid algorithm is measured and energy improvement is evaluated up to 35 tasks. Energy consumption is the same as ACO algorithm because as the number of tasks increases (45 to 70) there is a considerable decrease in the energy consumption rate. Makespan of Hybrid algorithm based on number of tasks is compared with ACO algorithm. Further we have analyzed the energy consumption for a number of processors and its improvement rate – up to 6 processors, energy consumption is considerably reduced and the energy consumption tends to be in steady state with further increase in the number of processors. 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).