Energy efficiency is considered as a crucial objective in cloud data centers as it reduces cost and meets the standard set in green computing. Task scheduling an important problem becomes more complex and critical under energy efficiency consideration. Key issues in recent research on energy efficient task scheduling are execution overhead and scalability. Machine learning has been widely employed for energy efficient task scheduling problem but mostly used to predict resource consumption only instead of deciding the schedule itself. However, we used the neural network to decide which resource should be assigned to given task independently. In this paper, we proposed an energy efficient independent task scheduler using supervised neural networks with the aim to reduce makespan, energy consumption, execution overhead and number of active racks. Proposed artificial neural network-based scheduler takes incoming task and current cloud environment state as input and predict the best computing resource for given task as output which compiles our aim. We used genetic algorithm to generate a huge dataset (∼18 million training instances) and trained our neural network on this dataset using back propagation algorithm with 99.9% accuracy. We simulated experiments on heavily loaded and lightly loaded cloud environment and compared with well-known approaches: Genetic algorithm, MinMIN-MINMin heuristic and Linear regression based energy efficient task schedulers. Results clearly indicate that proposed work outperforms considered algorithms. In heavily (lightly) loaded environment, it improves makespan by 59% (64%), energy consumption by 45% (71%), execution overhead by 88% (43%) respectively and number of active racks by 70%.

能源效率被认为是云数据中心的关键目标，因为它可以降低成本并符合绿色计算中设定的标准。在能源效率的考虑下，重要的任务计划变得更加复杂和关键。近期有关节能任务调度的研究的关键问题是执行开销和可伸缩性。机器学习已被广​​泛用于节能任务调度问题，但大多用于预测资源消耗，而不是确定调度本身。但是，我们使用神经网络来决定应将哪些资源独立分配给给定任务。在本文中，我们提出了一种使用监督神经网络的节能独立任务计划程序，目的是减少工期，能耗，执行开销和活动机架的数量。拟议的基于人工神经网络的调度程序将传入任务和当前云环境状态作为输入，并预测给定任务的最佳计算资源作为输出，这符合我们的目标。我们使用遗传算法生成了一个庞大的数据集（约1800万个训练实例），并使用反向传播算法以99.9％的精度在该数据集上训练了我们的神经网络。我们在重负载和轻负载的云环境上模拟了实验，并与众所周知的方法进行了比较：遗传算法，MinMIN-MINMin启发式算法和基于线性回归的节能任务调度程序。结果清楚地表明，建议的工作要优于算法。在重载（轻载）环境中，可将制造跨度提高59％（64％），能耗降低45％（71％），