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Most previous workflow scheduling research focused on scheduling a single workflow on parallel systems. Recent researches show that utilizing idle time slots between scheduled tasks is a promising direction for efficient multiple workflow scheduling. Stavrinides and Karatza proposed a list scheduling approach to efficient utilization of the idle time slots through bin packing techniques. In this paper, we elaborate on this direction and develop a new approach to further improve multiple workflow scheduling performance through two techniques. The first, in contrast with the list scheduling approach, is clustering the tasks within workflows into groups before allocation. This can reduce inter-task communication cost and thus improve workflow execution performance. The second technique tries to make a balance between tasks' start time and the fitness of idle time slots when allocating task groups. The proposed approach has been evaluated with a series of simulation experiments and compared to the previous method. The results show that our approach outperforms the previous method significantly, up to 51% performance improvement in terms of average makespan.

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.