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.

资源分配是云方案中新兴的研究领域之一。云计算在分布式环境中提供了共享的资源池。它支持基于实用程序的计算功能。在可扩展的云计算环境中，虚拟机上的有效任务配置是主要关注点。任务供应分别将性能指标的总完成时间（ms），平均开始时间，平均完成时间，平均执行时间，调度时间和模拟时间分别最小化。调度是一个重要的问题，当考虑各种参数时，该问题将变得更加复杂。虚拟机级别调度中的关键问题是实时场景中的执行时间开销和可伸缩性。我们的目标是使用神经生物学启发的GA-ANN技术在数据中心内的虚拟机上制定最佳的任务计划。这项工作提出了一种基于遗传方法和人工神经网络的调度程序。所提出的方法在适当的虚拟机上执行任务的最佳调度。通过减少失败的任务数来提高系统的可靠性。提出的工作使用遗传算法生成巨大的数据集，并使用通过遗传方法生成的数据集训练神经模型。使用反向传播以98％的精度提高了模型的精度。使用可伸缩的云计算环境执行该组实验。将本文介绍的生物启发技术与自然启发，生物启发的成本意识BB-BC，GA-Cost，和基于GA-Exe的高效任务调度技术。使用实际工作负载日志和综合数据集获得结果。结果表明，拟议的GA-ANN生物启发式预测方法优于自然启发式调度方法。使用各种性能指标分别比较了该算法的总完成时间，平均开始时间，平均完成时间以及故障率，执行时间和调度时间。提出的模型减少了82.63％的故障率，成功完成的任务数量增加了26.81％，执行时间增加了10.66％，调度时间减少了69.94％。随着迭代次数和任务数量的增加，调度时间缩短了85.76％。因此，提出的GA-ANN调度技术优于GA成本GA EXE，