Cache Aware Task Scheduling Algorithm for Heterogeneous 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. For building tradeoffs model this work assume that different task will have different execution path, I/O access, memory, active processing, and cache requirement. Considering such assumption this paper present cache aware workload scheduling (CATS) algorithm by minimizing energy dissipation and utilizing cache resource more efficiently. The CATS model achieves much lesser execution time and energy consumption when compared with existing multiobjective based and DVFS-based workload scheduling algorithm.