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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]

With innovations in the Internet, it is becoming increasingly relied upon. In the last decade, research on peer-to-peer (P2P) technology has become even more popular. As more people use P2P systems, the scalability and flexibility of the systems must be considered. In this study, an arrangement graph is used to form a P2P overlay, the Arrangement-Graph Overlay (AGO), to reduce system overhead and bind routing hops. The proposed AGO utilizes the properties of the arrangement graph, i.e., that each node has a unique ID and IDs between adjacent nodes differ by only one digit, to form the overlay network and develop a routing algorithm. The routing hops of the proposed AGO system can be bound within a certain number because of the diameter of the arrangement graph. Experimental results show that the proposed AGO system can greatly reduce system overhead and perform routing in a constant number of hops, even in a large-scale network environment. The experimental results also show that the AGO system consumes less bandwidth, which is an important consideration in P2P systems. [ABSTRACT FROM AUTHOR]

With the rapid increment of the heterogeneity of hardware devices, cluster computing has to encounter the problem of handling heterogeneous resources for exploiting the utilization of system resources. This paper introduces a new job allocation strategy based on multi-clusters in diskless environments. By adopting Ganglia as the resource monitor and Condor as the queue system, a heterogeneous multi-cluster system is also constructed with and without storage devices for evaluating the system performance. The proposed algorithm is called the Well-Balanced Allocation Strategy (WBAS) in which the scheduler dispatches MPI-based jobs to appropriate resources across multi-clusters. The strategy focuses on dispatching jobs to nodes with similar performance, thus equalizing execution times among all the required nodes. The WBAS is implemented on the constructed heterogeneous multi-cluster system to evaluate the performance of the scheduling strategy. The experimental results show that the proposed strategy performs well and could efficiently improve the system performance. [ABSTRACT FROM AUTHOR]

This paper proposes a new duplication-based task scheduling algorithm for distributed heterogeneous computing (DHC) systems. For such systems, many researchers have focused on solving the NP-complete problem of scheduling directed acyclic task graphs to minimize the makespan. However, the heterogeneity of computational resources and communication mechanisms poses some major obstacles to achieving high parallel efficiency. This paper proposes a heuristic strategy called the Dominant Predecessor Duplication (DPD) scheduling algorithm, which allows for system heterogeneities and communication bandwidth to exploit the potential of parallel processing. This algorithm can improve system utilization and avoid redundant resource consumption, resulting in better schedules. Experimental results show that the system heterogeneities and program structures of applications affect scheduling performance, and that our presented algorithm is better able to avoid these problems than those presented in previous literature. Here, we show that our algorithm can be applied to design efficient distributed systems to overcome performance bottlenecks caused by system heterogeneities. [ABSTRACT FROM AUTHOR]