Energy Aware Resource Utilization Technique for Workflow Scheduling in Cloud Computing Environment

Abstract

Heterogeneous multicore computational environment are increasingly being used for executing scientific workload. Heterogeneous computational framework aid is reducing energy dissipation for executing real-time data intensive workload by employing Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS). However, reducing energy and improving performance is becoming major constraint in modelling workload scheduling model in heterogeneous computational environment. Recently, number of multi-objective based workload scheduling aimed at minimizing power budget and meeting task deadline constraint. However, these model induce significant overhead when demand and number of processing core increases. For addressing research problem this work assume that different task will have different execution path, I/O access, memory, active processing, and cache requirement. Thus, this paper present Energy Aware Resource Utilization (EARU) model by minimizing energy dissipation and utilizing cache resource more efficiently. The EARU model achieves much lesser execution time, energy consumption, and power consumption when compared with existing multi-objective based and DVFS-based workload scheduling algorithm.