Virtualization technology provides a new way to improve resource utilization and cloud service throughput. However, the randomness of task arrival, tight coupling between resource load imbalance and node heterogeneity, high computing power, and other factors have hindered the energy consumption optimization and cost reduction objectives of the existing technology. Consequently, task scheduling failure cannot be easily eliminated, and cloud computing performance is decreased dramatically. In this study, a strong agile response task scheduling optimization algorithm is proposed on the basis of the peak energy consumption of data centers and the time span of task scheduling. Agile response optimization techniques are also adopted. From the perspective of task failure rate, the proposed algorithm can be used to investigate the strong agile response optimization model, explore the probability density function of the task request queue overflow, and request a timeout to avoid network congestion. Experimental results indicate that the proposed algorithm can achieve the stability and efficiency of task scheduling and effectively improve the throughput of the cloud computing system.

虚拟化技术提供了一种提高资源利用率和云服务吞吐量的新方法。然而，任务到达的随机性，资源负载不平衡与节点异构性之间的紧密耦合，高计算能力以及其他因素阻碍了现有技术的能耗优化和降低成本的目标。因此，任务调度失败无法轻松消除，并且云计算性能急剧下降。基于数据中心的峰值能耗和任务调度的时间跨度，提出了一种强大的敏捷响应任务调度优化算法。还采用了敏捷响应优化技术。从任务失败率的角度来看，该算法可用于研究强大的敏捷响应优化模型，探索任务请求队列溢出的概率密度函数，并请求超时以避免网络拥塞。实验结果表明，该算法可以实现任务调度的稳定性和效率，并有效提高云计算系统的吞吐量。