A cost saving and load balancing task scheduling model for computational biology in heterogeneous cloud datacenters

Abstract

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.