In recent years, with the continuous development of internet of things and cloud computing technologies, data intensive applications have gotten more and more attention. In the distributed cloud environment, the access of massive data is often the bottleneck of its performance. It is very significant to propose a suitable data deployment algorithm for improving the utilization of cloud server and the efficiency of task scheduling. In order to reduce data access cost and data deployment time, an optimal data deployment algorithm is proposed in this paper. By modeling and analyzing the data deployment problem, the problem is solved by using the improved genetic algorithm. After the data are well deployed, aiming at improving the efficiency of task scheduling, a task progress aware scheduling algorithm is proposed in this paper in order to make the speculative execution mechanism more accurate. Firstly, the threshold to detect the slow tasks and fast nodes are set. Then, the slow tasks and fast nodes are detected by calculating the remaining time of the tasks and the real-time processing ability of the nodes, respectively. Finally, the backup execution of the slow tasks is performed on the fast nodes. While satisfying the load balancing of the system, the experimental results show that the proposed algorithms can obviously reduce data access cost, service-level agreement (SLA) default rate and the execution time of the system and optimize data deployment for improving scheduling efficiency in distributed clouds.

中文翻译：

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基于成本和时间的数据部署，以提高分布式云中的调度效率

近年来，随着物联网和云计算技术的不断发展，数据密集型应用越来越受到关注。在分布式云环境中，海量数据的访问通常是其性能的瓶颈。提出合适的数据部署算法对提高云服务器的利用率和任务调度效率非常重要。为了减少数据访问成本和数据部署时间，提出了一种优化的数据部署算法。通过对数据部署问题进行建模和分析，使用改进的遗传算法解决了该问题。妥善部署数据后，旨在提高任务调度的效率，为了提高投机执行机制的准确性，提出了一种任务进度感知调度算法。首先，设置检测慢任务和快节点的阈值。然后，分别通过计算任务的剩余时间和节点的实时处理能力来检测慢速任务和快速节点。最后，慢任务的备份执行在快速节点上执行。实验结果表明，该算法在满足系统负载均衡的同时，可以明显降低数据访问成本，服务水平协议默认率和系统执行时间，并优化数据部署，提高分布式调度效率。云。设置检测慢任务和快节点的阈值。然后，分别通过计算任务的剩余时间和节点的实时处理能力来检测慢速任务和快速节点。最后，慢任务的备份执行在快速节点上执行。实验结果表明，该算法在满足系统负载均衡的同时，可以明显降低数据访问成本，服务水平协议默认率和系统执行时间，并优化数据部署，提高分布式调度效率。云。设置检测慢任务和快节点的阈值。然后，分别通过计算任务的剩余时间和节点的实时处理能力来检测慢速任务和快速节点。最后，慢任务的备份执行在快速节点上执行。实验结果表明，该算法在满足系统负载均衡的同时，可以明显降低数据访问成本，服务水平协议默认率和系统执行时间，并优化数据部署，提高分布式调度效率。云。慢速任务的备份执行在快速节点上执行。实验结果表明，该算法在满足系统负载均衡的同时，可以明显降低数据访问成本，服务水平协议默认率和系统执行时间，并优化数据部署，提高分布式调度效率。云。慢速任务的备份执行在快速节点上执行。实验结果表明，该算法在满足系统负载均衡的同时，可以明显降低数据访问成本，服务水平协议默认率和系统执行时间，并优化数据部署，提高分布式调度效率。云。