Task Placement in a Cloud with Case-based Reasoning

Eric Schulte-Zurhausen, Mirjam Minor


Moving workflow management to the cloud raises novel, exciting opportunities for rapid scalability of workflow execution. Instead of running a fixed number of workflow engines on an invariant cluster of physical machines, both physical and virtual resources can be scaled rapidly. Furthermore, the actual state of the resources gained from cloud monitoring tools can be used to schedule workload, migrate workload or conduct split and join operations for workload at run time. However, having so many options for distributing workload forms a computationally complex configuration problem which we call the task placement problem. In this paper, we present a case-based framework addressing the task placement problem by interleaving workflow management and cloud management. In addition to traditional workflow and cloud management operations it provides a set of task internal operations for workload distribution.


  1. Aamodt, A. and Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1):39-59.
  2. Jiang, J. W., Lan, T., Ha, S., Chen, M., and Chiang, M. (2012). Joint VM placement and routing for data center traffic engineering. In INFOCOM, 2012 Proceedings IEEE, page 28762880.
  3. Jin, L.-j., Casati, F., Sayal, M., and Shan, M.-C. (2001). Load balancing in distributed workflow management system. In Proceedings of the 2001 ACM symposium on Applied computing, page 522530.
  4. Maurer, M., Brandic, I., and Sakellariou, R. (2013). Adaptive resource configuration for cloud infrastructure management. Future Generation Computer Systems, 29(2):472-487.
  5. Minor, M., Schmalen, D., Koldehoff, A., and Bergmann, R. (2007). Structural adaptation of workflows supported by a suspension mechanism and by case-based reasoning. In Reddy, S. M., editor, Proceedings of the 16th IEEE Internazional Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE'07), June 18 - 20, 2007, Paris, France, pages 370-375. IEEE Computer Society, Los Alamitos, California. Best Paper.
  6. Sharma, B., Chudnovsky, V., Hellerstein, J. L., Rifaat, R., and Das, C. R. (2011). Modeling and synthesizing task placement constraints in google compute clusters. In Proceedings of the 2Nd ACM Symposium on Cloud Computing, SOCC 7811, page 3:13:14, New York, NY, USA. ACM.
  7. Tang, C., Steinder, M., Spreitzer, M., and Pacifici, G. (2007). A scalable application placement controller for enterprise data centers. In Proceedings of the 16th International Conference on World Wide Web, WWW 7807, page 331340, New York, NY, USA. ACM.
  8. US Department of Commerce, N. (2011). Final version of NIST cloud computing definition published. Final Version of NIST Cloud Computing Definition Published.
  9. fWorkflow Management Coalitiong (1999). Workflow management coalition glossary & terminology. last access 05-23-2007.
  10. Wu, Z., Liu, X., Ni, Z., Yuan, D., and Yang, Y. (2013). A market-oriented hierarchical scheduling strategy in cloud workflow systems. The Journal of Supercomputing, 63(1):256-293.

Paper Citation

in Harvard Style

Schulte-Zurhausen E. and Minor M. (2014). Task Placement in a Cloud with Case-based Reasoning . In Proceedings of the 4th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-019-2, pages 323-328. DOI: 10.5220/0004944203230328

in Bibtex Style

author={Eric Schulte-Zurhausen and Mirjam Minor},
title={Task Placement in a Cloud with Case-based Reasoning},
booktitle={Proceedings of the 4th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},

in EndNote Style

JO - Proceedings of the 4th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Task Placement in a Cloud with Case-based Reasoning
SN - 978-989-758-019-2
AU - Schulte-Zurhausen E.
AU - Minor M.
PY - 2014
SP - 323
EP - 328
DO - 10.5220/0004944203230328