Cloud Auto-scaling

Public cloud computing has almost all of the capabilities one could ask for; how can we automatically choose/provision/manage the cloud resources to match our requirements?

Key publications:

  1. I.K. Kim, W. Wang, Y. Qi and M. Humphrey. Empirical Evaluation of Workload Forecasting Techniques for Predictive Cloud Resource Scaling. Proceedings of 8th IEEE International Conference on Cloud Computing (Cloud 2016). June 27-July 2, 2016, San Francisco CA USA.
  2. A. Ruiz-Alvarez, I.K. Kim,, and M. Humphrey. Toward Optimal Resource Provisioning for Cloud MapReduce and Hybrid Cloud Applications. Proceedings of 8th IEEE International Conference on Cloud Computing (Cloud 2015). June 27-July 2, 2015, NYC NY USA.
  3. I.K. Kim, W. Wang, and M. Humphrey. PICS: A Public IaaS Cloud Simulator. Proceedings of 8th IEEE International Conference on Cloud Computing (Cloud 2015). June 27-July 2, 2015, NYC NY USA.
  4. I.K. Kim, J. Steele, Y. Qi, and M. Humphrey. Comprehensive Elastic Resource Management to Ensure Predictable Performance for Scientific Applications on Public IaaS Clouds. Proceedings of the 7th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2014). Dec 2014.
  5. M. Mao and M. Humphrey. Scaling and Scheduling to Maximize Application Performance within Budget Constraints in Cloud Workflows. Proceedings of the 27th IEEE International Parallel and Distributed Symposium (IPDPS). May 20-24, 2013. Cambridge, MA.
  6. M. Mao and M. Humphrey. A Performance Study on the VM Startup Time in the Cloud. Proceedings of IEEE 5th International Conference on Cloud Computing (Cloud 2012). June 24-29, 2012, Honolulu, Hawaii.
  7. A. Ruiz-Alvarez and M. Humphrey. A Model and Decision Procedure for Data Storage in Cloud Computing. Proceedings of the IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid’12), May 13-16, 2012, Ottawa Canada.
  8. M. Mao and M. Humphrey. Auto-Scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflows. Proceedings of Supercomputing 2011, Seattle, WA, Nov 15-20, 2011.
  9. A. Ruiz-Alvarez and M. Humphrey. An Automated Approach to Cloud Storage Service Selection. Proceedings of the 2nd Workshop on Scientific Cloud Computing (ScienceCloud 2011). June 8, 2011.
  10. Z. Hill and M. Humphrey. CSAL: A Cloud Storage Abstraction Layer to Enable Portable Cloud Applications (work in progress). 2nd IEEE International Conference on Cloud Computing Technology and Science . November 30-December 3, 2010 . Indianapolis, Indiana.
  11. M. Mao, Jie Li and M. Humphrey. Cloud Auto-Scaling with Deadline and Budget Constraints. In Proceedings of 11th ACM/IEEE International Conference on Grid Computing (Grid 2010). Oct 25-28, 2010. Brussels, Belgium.
  12. Z. Hill, M. Mao, J. Li, A. Ruiz-Alvarez, and M. Humphrey. Early Observations on the Performance of Windows Azure. In 1st workshop on Scientific Cloud Computing. Chicago, Illinois, June 21, 2010.
  13. S.-M. Park and M. Humphrey. Predictable Time-Sharing for DryadLINQ Cluster. In Proceedings of IEEE International Conference on Autonomic Computing and Communications (ICAC), June 7-11, 2010, Washington DC, USA
  14. Sang-Min Park, Marty Humphrey, Predictable High Performance Computing Using Feedback Control and Admission Control, IEEE Transactions on Parallel and Distributed Systems, 14 May. 2010. IEEE computer Society Digital Library. IEEE Computer Society, http://doi.ieeecomputersociety.org/10.1109/TPDS.2010.100
  15. Z. Hill and M. Humphrey. A Quantitative Analysis of High Performance Computing with Amazon’s EC2 Infrastructure. In Proceedings of the 10th IEEE/ ACM International Conference on Grid Computing (Grid 2009). Oct 13-15 2009. Banff, Alberta, Canada.