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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aalst, W.V.D., Hee, K.M.V.: Workflow Management: Models, Methods, and Systems, vol. 368. MIT Press (2004)
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)
The CLOUDS Lab: Cloudbus Workflow Engine, http://www.cloudbus.org/workflow/ (accessed on August 18, 2013)
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)
Hollingsworth, D.: Workflow Management Coalition: The Workflow Reference Model, Workflow Management Coalition, Winchester, Hampshire, UK. pp. 1–55 (1995)
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)
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)
Liu, X., Yuan, D., Zhang, G., Li, W., Cao, D., He, Q., Chen, J., Yang, Y.: The Design of Cloud Workflow Systems. Springer (2012)
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)
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)
Vaquero, L.M., Rodero-Merino, L., Buyya, R.: Dynamically Scaling Applications in the Cloud. ACM SIGCOMM Computer Communication Review 41(1), 45–52 (2011)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)