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Novel Client-Cloud Architecture for Scalable Instance-Intensive Workflow Systems

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Web Information Systems Engineering – WISE 2013 (WISE 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8181))

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Abstract

Though workflow technology is relatively mature and has been one of the most popular components of process aware systems over the last two decades, few workflow architectures can efficiently support a large number of concurrent workflow instances, i.e. instance-intensive workflows. The basic requirements include high throughput, elastic scalability, and cost-effectiveness. This paper proposes a novel client-cloud architecture which takes advantages of cloud computing to support instance-intensive workflows, presents an application level real-time resource utilization estimation model, and identifies two primary principles to ensure the sustainable scalability, namely: (1) the time for a load balancer checking must be less than the decaying time of a server instance when it is overloaded, (2) the sampling time for an alarming service plus the launching time of new server instance must be less than the decaying time of a server instance when it is overloaded. Based on the above, we design and implement the SwinFlow-Cloud prototype. Finally, we deploy and evaluate the prototype on Amazon Web Services cloud. The results show that the prototype is able to satisfy all the basic requirements for instance-intensive workflows.

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References

  1. Aalst, W.V.D., Hee, K.M.V.: Workflow Management: Models, Methods, and Systems, vol. 368. MIT Press (2004)

    Google Scholar 

  2. Buyya, R., Venugopal, S.: The Gridbus Toolkit for Service Oriented Grid and Utility Computing: An Overview and Status Report. In: Proceedings of the 1st IEEE International Workshop on Grid Economics and Business Models (GECON 2004), pp. 19–66. IEEE Computer Society, Seoul (2004)

    Google Scholar 

  3. The CLOUDS Lab: Cloudbus Workflow Engine, http://www.cloudbus.org/workflow/ (accessed on August 18, 2013)

  4. Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM - 50th Anniversary Issue: 1958 - 2008 51(1), 107–113 (2008)

    Article  Google Scholar 

  5. Hollingsworth, D.: Workflow Management Coalition: The Workflow Reference Model, Workflow Management Coalition, Winchester, Hampshire, UK. pp. 1–55 (1995)

    Google Scholar 

  6. Liu, X., Yang, Y., Jiang, Y., Chen, J.: Preventing Temporal Violations in Scientific Workflows: Where and How. IEEE Transactions on Software Engineering 37(6), 805–825 (2011)

    Article  Google Scholar 

  7. Liu, X., Yuan, D., Zhang, G., Chen, J., Yang, Y.: SwinDeW-C: A Peer-to-Peer based Cloud Workflow System. In: Furht, B., Escalante, A. (eds.) Handbook of Cloud Computing, pp. 309–332. Springer US (2010)

    Google Scholar 

  8. Liu, X., Yuan, D., Zhang, G., Li, W., Cao, D., He, Q., Chen, J., Yang, Y.: The Design of Cloud Workflow Systems. Springer (2012)

    Google Scholar 

  9. Mao, M., Humphrey, M.: Auto-Scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflows. In: International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Seattle, USA, pp. 1–12 (2011)

    Google Scholar 

  10. Shen, Z., Subbiah, S., Gu, X., Wilkes, J.: CloudScale: Elastic Resource Scaling for Multi-Tenant Cloud Systems. In: The 2nd ACM Symposium on Cloud Computing (SoCC 2011), pp. 1–14. ACM, Cascais (2011)

    Google Scholar 

  11. Vaquero, L.M., Rodero-Merino, L., Buyya, R.: Dynamically Scaling Applications in the Cloud. ACM SIGCOMM Computer Communication Review 41(1), 45–52 (2011)

    Article  Google Scholar 

  12. Yan, J., Yang, Y., Raikundalia, G.K.: A Decentralised Architecture for Workflow Support. In: Proceedings of the 7th International Symposium on Future Software Technology (ISFST 2002), pp. 23–25. Software Engineers Association, Wuhan (2002)

    Google Scholar 

  13. Yan, J., Yang, Y., Raikundalia, G.K.: SwinDeW—A p2p-Based Decentralized Workflow Management System. IEEE Transactions on Systems, Man, and Cybernrtics—Part A: Systems and Humans 36(5), 922–935 (2006)

    Article  Google Scholar 

  14. Yang, Y., Liu, K., Chen, J., Lignier, J., Jin, H.: Peer-to-Peer Based Grid Workflow Runtime Environment of SwinDeW-G. In: Proceedings of the 3rd IEEE International Conference on e-Science and Grid Computing, pp. 51–58. IEEE Computer Society, Bangalore (2007)

    Google Scholar 

  15. Zhang, C., De Sterck, H.: CloudWF: A Computational Workflow System for Clouds Based on Hadoop. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) Cloud Computing. LNCS, vol. 5931, pp. 393–404. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

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Cao, D., Liu, X., Yang, Y. (2013). Novel Client-Cloud Architecture for Scalable Instance-Intensive Workflow Systems. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds) Web Information Systems Engineering – WISE 2013. WISE 2013. Lecture Notes in Computer Science, vol 8181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41154-0_20

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  • DOI: https://doi.org/10.1007/978-3-642-41154-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41153-3

  • Online ISBN: 978-3-642-41154-0

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