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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. • In this work, neural-bio task scheduling approach is proposed for cloud architecture. • The proposed learning-based GA-ANN model achieves a globally optimal solution. • The proposed solution invokes human-like thinking and decision making ability in the system in cloud architecture. • The GA-ANN model improves the performance using time (ms), tasks count, and fault rate. • The proposed algorithm proves an optimal results in the real world application of cloud. [ABSTRACT FROM AUTHOR]

• In this work a power efficient task scheduling algorithm is proposed for cloud architecture to improve the perform of cloud system in all and cloud application. • The proposed work is a learning based model using ANN and genetic algorithm to achieve global best solution is least time with high utilization and least execution time. • The proposed solution invokes human-like thinking and decision making ability in the system by implementing a machine learning model and bio-inspired genetic algorithm for training the model at infrastructure as a service layer in cloud architecture. • The proposed GA-ANN model improves the performance of the optimal scheduling problem using power consumption, execution time and average finish time as a performance metric. 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. [ABSTRACT FROM AUTHOR]