Browsing by Author "Huang, Kuo-Chan"

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A distributed server architecture supporting dynamic resource provisioning for BPM-oriented workflow management systems
(2010-08-01)
Tsai, Ching-Hong
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Huang, Kuo-Chan
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Wang, Feng-Jian
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Chen, Chun-Hao
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資訊工程學系
Workflow management systems have been widely used in many business process management (BPM) applications. There are also a lot of companies offering commercial software solutions for BPM. However, most of them adopt a simple client/server architecture with one single centralized workflow-management server only. As the number of incoming workflow requests increases, the single workflow-management server might become the performance bottleneck, leading to unacceptable response time. Development of parallel servers might be a possible solution. However, a parallel server architecture with a fixed-number of servers cannot efficiently utilize computing resources under time-varying system workloads. This paper presents a distributed workflow-management server architecture which adopts dynamic resource provisioning mechanisms to deal with the probable performance bottleneck. We implemented a prototype system of the proposed architecture based on a commercial workflow management system, Agentflow. A series of experiments were conducted on the prototype system for performance evaluation. The experimental results indicate that the proposed architecture can deliver scalable performance and effectively maintain stable request response time under a wide range of incoming workflow request workloads. (C) 2010 Elsevier Inc. All rights reserved.
An iterative expanding and shrinking process for processor allocation in mixed-parallel workflow scheduling
(2016-07-20)
Huang, Kuo-Chan
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Wu, Wei-Ya
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Wang, Feng-Jian
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Liu, Hsiao-Ching
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Hung, Chun-Hao
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.
Dynamic Provisioning of a Parallel Workflow Management System
(2008)
Tsai, Ching-Hong
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Huang, Kuo-Chan
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Wang, Feng-Jian
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資訊工程學系
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Department of Computer Science
Most work/flow management systems nowadays are based on centralized client/server architecture. Under this architecture, the response time of request might increase unacceptably when the number of users who login to the system increase quickly and a large amount of requests are sent to the centralized server within a short time period. Parallel server architecture could help to resolve the performance bottleneck of a single server. However, a static parallel architecture with a fixed number of servers is not efficient at resource utilization because the numbers of users and their requests usually change time by time, especially for big and fast changes. This paper presents an effective architecture of dynamic resource-provisioning and then the implementation for a parallel workflow management system. There are a series of experiments conducted and the results indicate that it is an effective approach to handling the time-varying workloads in real world WfMS.
Dynamic Resource Provisioning for Interactive Workflow Applications on Cloud Computing Platform
(2010)
Zhou, Hui-Zhen
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Huang, Kuo-Chan
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Wang, Feng-Jian
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資訊工程學系
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Department of Computer Science
Cloud computing opens new opportunities for application providers because with the policy "add as needed and pay as used" they can economize the cost for computing resources. In cloud environments, issues such as resource allocation and dynamic resource provisioning based on users' QoS constraints are yet to be addressed for interactive workflow applications. This paper develops an effective load metric, remaining tasks, for interactive workflow applications. Based on this metric load dispatching and dynamic resource provisioning approaches are proposed which outperform existing methods under a series of simulation evaluations. Experimental results show that the proposed approaches offer application providers better maintenance of QoS-satisfied response time under time-varying workload, at the minimum cost of resource usage.
Online Scheduling of Workflow Applications in Grid Environment
(2010)
Hsu, Chih-Chiang
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Huang, Kuo-Chan
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Wang, Feng-Jian
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資訊工程學系
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Department of Computer Science
Scheduling workflow applications in grid environments is a great challenge, because it is an NP-complete problem. Many heuristic methods have been presented in the literature and most of them deal with a single workflow application at a time. In recent years, there are several heuristic methods proposed to deal with concurrent workflows or online workflows, but they do not work with workflows composed of data-parallel tasks. In this paper, we present an online scheduling approach for multiple mixed-parallel workflows in grid environments. The proposed approach was evaluated with a series of simulation experiments and the results show that the proposed approach delivers good performance and outperforms other methods under various workloads.
Online scheduling of workflow applications in grid environments
(2011-06-01)
Hsu, Chih-Chiang
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Huang, Kuo-Chan
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Wang, Feng-Jian
;
資訊工程學系
;
Department of Computer Science
Scheduling workflow applications in grid environments is a great challenge, because it is an NP-complete problem. Many heuristic methods have been presented in the literature and most of them deal with a single workflow application at a time. In recent years, several heuristic methods have been proposed to deal with concurrent workflows or online workflows, but they do not work with workflows composed of data-parallel tasks. In this paper, we present an online scheduling approach for multiple mixed-parallel workflows in grid environments. The proposed approach was evaluated with a series of simulation experiments and the results show that the proposed approach delivers good performance and outperforms other methods under various workloads. (C) 2010 Elsevier B.V. All rights reserved.
Scheduling online mixed-parallel workflows of rigid tasks in heterogeneous multi-cluster environments
(2016-07)
Wang, Yi-Rong
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Huang, Kuo-Chan
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Wang, Feng-Jian
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資訊工程學系
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Department of Computer Science
Workflow scheduling on parallel systems has long been known to be a NP-complete problem. As modern grid and cloud computing platforms emerge, it becomes indispensable to schedule mixed-parallel workflows in an online manner in a speed-heterogeneous multi-cluster environment. However, most existing scheduling algorithms were not developed for online mixed-parallel workflows of rigid data parallel tasks and multi-cluster environments, therefore they cannot handle the problem efficiently. In this paper, we propose a scheduling framework, named Mixed-Parallel Online Workflow Scheduling (MOWS), which divides the entire scheduling process into four phases: task prioritizing, waiting queue scheduling, task rearrangement, and task allocation. Based on this framework, we developed four new methods: shortest-workflow-first, priority-based backfilling, preemptive task execution and All-EFT task allocation, for scheduling online mixed-parallel workflows of rigid tasks in speed-heterogeneous multi cluster environments. To evaluate the proposed scheduling methods, we conducted a series of simulation studies and made comparisons with previously proposed approaches in the literature. The experimental results indicate that each of the four proposed methods outperforms existing approaches significantly and all these approaches in MOWS together can achieve more than 20% performance improvement in terms of average turnaround time. (C) 2016 Elsevier B.V. All rights reserved.