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Workflow Performance and Scalability Analysis Using the Layered Queuing Modeling Methodology

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Association for Computing Machinery

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The design and implementation of a workflow management system is typically a large and complex task. Decisions need to be made about the hardware and software platforms, the data structures, the algorithms, and network interconnection of various modules utilized by various users and administrators. These decisions are further complicated by requirements such as flexibility, robustness, modifiability, availability, performance, and usability. As the size of workflow systems increases, organizations are finding that the standard server/client architectures, and off-the-shelf solutions are not adequate. We can further see that in the farther future, very large-scale workflow systems (VLSW) will become more complex, and more prevalent. Thus, one further requirement is an emphasis of this document: scalability. For the purposes of our scalable workflow investigations, we describe a framework, a taxonomy, a model, and a methodology to investigate the performance of various workflow architectures as the size of the system (number of workcases) grows very large.First, this paper presents a novel workflow architectural framework and taxonomy. In fact, most current workflow architectures fall into only one of the many categories of this taxonomy: the centralized server/client category. The paper next explains a performance analysis methodology useful for exploring this taxonomy. The methodology deploys a layered queuing model, and performs mathematical analysis on this model using a modified MOL (method of layers) combined with a linearization algorithm. Finally, the paper utilizes this methodology to compare and contrast the various architectural categories, providing interesting results about performance as the number of workcases increases. Our analytic results suggest that (a) for VLSW performance determination, software architecture is as important as hardware architecture, and (b) alternatives to the client server architecture provide significantly better scalability.

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Kim, Kwang-Hoon; Ellis, Clarence A. (2001): Workflow Performance and Scalability Analysis Using the Layered Queuing Modeling Methodology. Proceedings of the 2001 ACM International Conference on Supporting Group Work. DOI: 10.1145/500286.500308. New York, NY, USA: Association for Computing Machinery. pp. 135–143. Boulder, Colorado, USA

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architectural performance analysis, architectural framework, scalability, performance analytic model, performance, hardware contention model, architectural quality attribute, method of layer (MOL), taxonomy of workflow architectures, software contention model

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Number of citations to item: 9

  • L I N Chuang, Q U Yang, R E N Fengyuan, Dan C. Marinescu (2002): Performance Equivalent Analysis of Workflow Systems Based on Stochastic Petri Net Models, In: Lecture Notes in Computer Science, doi:10.1007/3-540-45785-2_5
  • Zhuo-yuan Xiang, Tian-hen Pan (2009): Research on Flexibility of the Workflow Reference Model, In: 2009 Second International Workshop on Knowledge Discovery and Data Mining, doi:10.1109/wkdd.2009.91
  • Huan Zhou, Chuang Lin, Kun Meng, Yarui Chen (2014): Stochastic Workflow Nets Based Workflow Pattern Modeling, In: Chinese Journal of Electronics 1(23), doi:10.23919/cje.2014.10848006
  • Zhaoli Zhang, Zongkai Yang, Qingtang Liu (2008): Performance analysis of composite web service, In: 2008 IEEE International Conference on Granular Computing, doi:10.1109/grc.2008.4664709
  • Mangala Gowri Nanda, Neeran Karnik (2003): Synchronization analysis for decentralizing composite Web services, In: Proceedings of the 2003 ACM symposium on Applied computing, doi:10.1145/952532.952612
  • Clarence A. Ellis, Aubrey J. Rembert, Kwang-Hoon Kim, Jacques Wainer (2006): Beyond Workflow Mining, In: Lecture Notes in Computer Science, doi:10.1007/11841760_5
  • Kwang-Hoon Kim (2007): A layered workflow knowledge Grid/P2P architecture and its models for future generation workflow systems, In: Future Generation Computer Systems 3(23), doi:10.1016/j.future.2006.05.005
  • Tom Hill, Sam Supakkul, Lawrence Chung (2009): Confirming and Reconfirming Architectural Decisions on Scalability: A Goal-Driven Simulation Approach, In: Lecture Notes in Computer Science, doi:10.1007/978-3-642-05290-3_45
  • Liam O'Brien, Paul Brebner, Jon Gray (2008): Business transformation to SOA, In: Proceedings of the 2nd international workshop on Systems development in SOA environments, doi:10.1145/1370916.1370925
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