Power efficient resource provisioning for cloud infrastructure using bio-inspired artificial neural network model

Abstract

High-performance computing changes the way of computing. More than one-decade, the cloud computing paradigm has changed the way of computing, communication, and technology. The efficient resource provisioning or task scheduler policy improvement is a challenging issue in the service-oriented computing paradigm. This article work focuses on task scheduler policy improvement for better cloud application performance. The task scheduling algorithm aims to improve the performance of real-time applications in the cloud by reducing task waiting time, execution time, and power consumption. The proposed model is inspired by an Artificial Neural Network (ANN) based system with a training model using a genetic algorithm. Results exhibit that the proposed GA-ANN policy outperforms the (Big-Bang Big-Crunch cost-aware), Genetic cost-aware, and other existing approaches. The results show that the proposed GA-ANN model performs better than existing approaches taking power consumption, total completion time(ms), average start time (ms), and average completion time(ms) as performance metrics. The proposed GA-ANN model is validated using real-time user requests (standard workload file) from workload traces (San Diego Supercomputer Center (SDSC) Blue Horizon logs), and fabricated data sets. The proposed model improves power efficiency by 13 %, scheduling time by 77.14 % and total execution time by 36 %. Hence the proposed GA-ANN technique provides performance as compared to existing systems.