Publication

Abstract : In recent days, most of the cloud users request data center in the cloud environment by applying an exhaustive data-centric workflows which leads to the major energy consumption. The major energy breaks out from the data center and makes way to CO2 emission which impacts the global warming. In this paper, we introduce optimized energy utilization in deployment and forecast (OEUDF) for data-intensive workflows in virtualized cloud systems which help to reduce the energy in the cloud workflow environment. In this approach, initially, we compute the optimal data-accessing energy path (ODEP) which helps us to deploy and configure the virtual machines; secondly, it computes the rank, according to that it will schedule the workflow activities in the cloud environment. If any unscheduled activities are in the submission pool, then OEUDF finds the suitable virtual machine and reconfigures the data center by minimizing the energy utilization. The experiment result indicates that the proposed algorithm gradually reduces the energy consumption.