Scheduling Workflows with Reduced Energy Consumption for Big Data Applications

Abstract

The consumption of energy and the cost have a great impact in influencing the consumption of resource and economy. Dueto this fact, the two pivotal factors-the consumption of energy and the cost- play a vital role in contributing to the execution of application in cloud computing systems. This article highlights various issues of minimizing the consumption of energy of a cost budgeted Directed Acyclic Graph (DAG) that can be applied in heterogeneous computing systems. In this paper, cost and energy aware scheduling is proposed using Reduced Energy Consumption using the Available Budget Pre-assignment (RECABP) technique for the cloud scheduler in the reduction of the execution cost of workflow.RECABP also enables in reducing the energy consumption. In addition to the minimization of the execution of workflow cost and the consumption of energy, the time complexity analysis is performed to ascertain that the complexity of RECABP algorithm is polynomial. The proposed RECABP algorithm is implemented using CloudSim. The implementation demonstrates that the proposed RECABP algorithm outspaces the related well-known approaches in terms of the following parameters - computation cost, execution time, bandwidth preference, time preference and energy consumption.