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Summary: This paper presents a Platform of Extensible Workflow Simulation Service (Pewss), which we have developed to provide a cloud service for aiding research work in workflow scheduling. The simulation has been a major tool for performance evaluation and comparison in workflow scheduling research. However, researchers usually have to develop their own simulation programs with limited functionality, simply outputting summarized performance results. Pewss has been developed for easing and improving current practices in conducting performance simulations during studying of existing workflow scheduling algorithms or designing of new scheduling algorithms. Pewss has been designed based on the Software as a Service (SaaS) model, adopting a multiuser Web‐based client/server architecture. Conducting simulation experiments on Pewss, researchers simply have to implement the scheduling algorithm under study instead of a whole simulation environment, allowing them to focus on their research work without spending unnecessary efforts on the simulation implementation details. Pewss provides the visualization of a workflow execution schedule based on simulation results, offering a convenient way for researchers to gain an insight into the effectiveness, characteristics, and performance bottleneck of scheduling algorithms. As a multiuser environment, Pewss also provides functionality for researchers to facilitate comparative performance analysis and collaborative research works effectively. Pewss has been used in our research work on task‐parallel workflow scheduling and has been planned to be extended to support other types of workflow scheduling research problems, eg, mixed‐parallel workflows. [ABSTRACT FROM AUTHOR]

Parallel computation has been widely applied in a variety of large-scale scientific and engineering applications. Many studies indicate that exploiting both task and data parallelisms, i.e. mixed-parallel workflows, to solve large computational problems can get better efficacy compared with either pure task parallelism or pure data parallelism. Scheduling traditional workflows of pure task parallelism on parallel systems has long been known to be an NP-complete problem. Mixed-parallel workflow scheduling has to deal with an additional challenging issue of processor allocation. In this paper, we explore the processor allocation issue in scheduling mixed-parallel workflows of moldable tasks, called M-task, and propose an Iterative Allocation Expanding and Shrinking (IAES) approach. Compared to previous approaches, our IAES has two distinguishing features. The first is allocating more processors to the tasks on allocated critical paths for effectively reducing the makespan of workflow execution. The second is allowing the processor allocation of an M-task to shrink during the iterative procedure, resulting in a more flexible and effective process for finding better allocation. The proposed IAES approach has been evaluated with a series of simulation experiments and compared to several well-known previous methods, including CPR, CPA, MCPA, and MCPA2. The experimental results indicate that our IAES approach outperforms those previous methods significantly in most situations, especially when nodes of the same layer in a workflow might have unequal workloads. [ABSTRACT FROM AUTHOR]

Workflow scheduling has long been an important research topic in the field of parallel computing. Clustering-based methods are one of the major types of workflow scheduling approaches which have been shown superior to other kinds of methods in many cases due to their advantage of minimizing inter-task communication costs. Most previous research dealt with single workflow scheduling and focused on how to cluster the tasks within a workflow into a set of task groups. Recent research showed that utilizing idle time gaps between scheduled tasks is a promising direction for efficient multiple workflow scheduling. Since executing multiple workflows simultaneously is an inevitable need in modern shared parallel computing platforms, efficient task group allocation becomes a critical issue. In this paper, we studied such issue and proposed an innovative dual-criteria task group allocation method which considers both task group's finish time and potential resource utilization to effectively improve overall multi-workflow execution performance. In addition, an adaptive task group rearrangement mechanism was adopted to further improve performance. The proposed method has been evaluated with a series of simulation experiments and compared to previous approaches. The experimental results show that our method outperforms previous approaches across different workload conditions and workflow properties in terms of average makespan. The performance improvement ranges from 5 to 29 % for different conditions, achieving the largest performance improvement for workflows of smaller CCR. [ABSTRACT FROM AUTHOR]

This paper proposes waiting ratio as a basis in evaluating various scheduling methods for dynamic workloads consisting of multi-processor jobs on parallel computers. We evaluate commonly used methods as well as several methods proposed in this paper by simulation studies. The results indicate that some commonly used methods do not improve the waiting ratios as expected by intuition, while some methods proposed in this paper do greatly improve waiting ratios more than 10 times for some workload data, promising in leading to more reasonable waiting time and better user's satisfaction. [ABSTRACT FROM AUTHOR]