Cloud-based scientific workflow systems can play an important role in the development of cost-effective bioinformatics analysis applications. There are differences in the cost control and performance of many kinds of servers in heterogeneous cloud data centers for bioinformatics workflows running, which can lead to imbalance between operational/maintenance management costs and quality of service of server clusters. A task scheduling model that responds to the peaks and valleys of task sequencing—the number of tasks that arrive in a given unit of time—is related to indicators such as cost saving, load balancing and system performance (average task wait time, average response time and throughput). This study proposes a large-scale cost-saving and load-balancing scheduling model, called HDCBS, for the optimization of system throughput. First, queuing theory is used to model each computing node as an independent queuing system and to obtain the average system wait time and average task response time. Then, using convex optimization theory, a task assignment solution is proposed with a load-balancing mechanism. The validity of the task scheduling model is verified by simulation experiments, and the model performance is further validated through a comparison with other frequently used scheduling methods. The simulation results show that the credibility of HDCBS is greater than 95% in task scheduling.

中文翻译：

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异构云数据中心计算生物学成本节约与负载均衡任务调度模型

基于云的科学工作流系统可以在开发具有成本效益的生物信息学分析应用程序中发挥重要作用。运行生物信息学工作流的异构云数据中心中多种服务器的成本控制和性能存在差异，这可能导致运行/维护管理成本与服务器集群服务质量之间的不平衡。响应任务排序峰谷的任务调度模型——给定时间单位内到达的任务数——与成本节约、负载均衡和系统性能（平均任务等待时间、平均响应）等指标相关时间和吞吐量）。本研究提出了一种大规模节省成本和负载平衡的调度模型，称为 HDCBS，用于优化系统吞吐量。第一的，排队论用于将每个计算节点建模为一个独立的排队系统，并获得平均系统等待时间和平均任务响应时间。然后，利用凸优化理论，提出了具有负载平衡机制的任务分配解决方案。通过仿真实验验证了任务调度模型的有效性，并通过与其他常用调度方法的比较进一步验证了模型性能。仿真结果表明，HDCBS在任务调度中的可信度大于95%。通过仿真实验验证了任务调度模型的有效性，并通过与其他常用调度方法的比较进一步验证了模型性能。仿真结果表明，HDCBS在任务调度中的可信度大于95%。通过仿真实验验证了任务调度模型的有效性，并通过与其他常用调度方法的比较进一步验证了模型性能。仿真结果表明，HDCBS在任务调度中的可信度大于95%。