Resource provisioning in scalable cloud using bio-inspired artificial neural network model

Abstract

Resource assignment is one of the emerging research area in the cloud scenario. Cloud computing provides a shared pool of resources in a distributed environment. It supports the features of utility-based computing. Efficient task provisioning on virtual machines is the major concern in an extensible cloud computing environment. The task provisioning minimizes the performance metrics total completion time (ms), average start time, average finish time, average execution time, scheduling time, and simulation time respectively. The scheduling is an important problem which becomes more complicated when various parameters consider. The key issue in virtual machine level scheduling is execution time overhead and scalability in a real-time scenario. Our objective is to make an optimal schedule of tasks on a virtual machine inside the datacenter using neural-bio inspired GA-ANN technique. This work presents a scheduler based on a genetic approach and an artificial neural network. The presented approach performs optimal scheduling of tasks on an appropriate virtual machine. The reliability of the system improves by reducing the number of tasks failed. The presented work uses a genetic algorithm to generated huge data sets and trains the neural model using the data set generated by using a genetic approach. The accuracy of the model is improved using back propagation with 98% accuracy. The set of experiments are performed using a scalable cloud computing environment. The presented bio-inspired technique is compared against nature-inspired, bio-inspired cost-aware BB–BC, GA-Cost, and GA-Exe based efficient task scheduling techniques. The results are obtained using real workload logs and synthetic data sets. Results indicate that the proposed GA-ANN bio-inspired predictive approach outperforms the considered nature-inspired scheduling approaches. The proposed algorithm is compared using various performance metrics total completion time, average start time, average finish time, and the fault rate, execution time, and scheduling time respectively. The proposed model reduces the fault rate by 82.63%, successfully completed tasks count improves by 26.81% and execution time improves by 10.66% and scheduling time improves by 69.94%. The scheduling time improves by 85.76% with an increasing number of iterations and constant numbers of tasks. Hence the presented GA-ANN scheduling technique outperformed the GA cost, GA EXE, and BB–BC COST scheduling approaches.