On Optimizing Resource Allocation and Application Placement Costs in Cloud Systems

Cihan Seçinti, Tolga Ovatman

2014

Abstract

Resource utilization problem has been widely studied for cloud systems where a number of virtualized resources are shared among applications hosted as services. Resource utilization optimization can be confronted at two levels: allocating resources to virtual machines(VM) across physical machines or assigning applications to virtual machines present in the host. With the improving capabilities on virtualization technologies, realizing resource allocation at both levels is becoming more viable using VM reconfiguration and live-migration of applications. In this paper we investigate applying resource allocation optimization at these two levels and the emerging trade-off in deciding the appropriate technique to be used. We first analyze the effect of gradually increasing the amount of resources assigned to a virtual machine using VM reconfiguration and compare our results with fully assigning host's resources without reconfiguration. Later, we investigate the amount of utilization revenue when application live-migration is used for applications having smaller/larger performance needs. Finally, we compare the host utilization for different amounts of cost rates between live-migration and reconfiguration. Consequently our analysis results identify the cost rate and application granularity levels where it is optimal to apply live-migration or VM reconfiguration.

References

  1. Bennani, M. N. and Menasc, D. A. (2005). Resource allocation for autonomic data centers using analytic performance models. In Autonomic Computing, 2005. ICAC 2005. Proceedings. Second International Conference on, pages 229-240.
  2. Borovskiy, V., Wust, J., Schwarz, C., Koch, W., and Zeier, A. (2011). A linear programming approach for optimizing workload distribution in a cloud. In CLOUD COMPUTING 2011, The Second International Conference on Cloud Computing, GRIDs, and Virtualization, pages 127-132.
  3. Chen, W., Qiao, X., Wei, J., and Huang, T. (2012). A profit-aware virtual machine deployment optimization framework for cloud platform providers. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, pages 17-24.
  4. Clark, C., Fraser, K., Hand, S., Hansen, J. G., Jul, E., Limpach, C., Pratt, I., and Warfield, A. (2005). Live migration of virtual machines. In Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2, NSDI'05, pages 273-286, Berkeley, CA, USA. USENIX Association.
  5. He, S., Guo, L., Ghanem, M., and Guo, Y. (2012). Improving resource utilisation in the cloud environment using multivariate probabilistic models. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, pages 574-581.
  6. Hwang, I. and Pedram, M. (2012). Portfolio theory-based resource assignment in a cloud computing system. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, pages 582-589.
  7. Kikuchi, S. and Matsumoto, Y. (2012). Impact of live migration on multi-tier application performance in clouds. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, pages 261-268.
  8. Ruiz-Alvarez, A. and Alvarez, M. (2012). A model and decision procedure for data storage in cloud computing. In Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on, pages 572-579. IEEE.
  9. Van den Bossche, R., Vanmechelen, K., and Broeckhove, J. (2010). Cost-optimal scheduling in hybrid iaas clouds for deadline constrained workloads. In Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on, pages 228-235. IEEE.
  10. Verma, A., Kumar, G., and Koller, R. (2010). The cost of reconfiguration in a cloud. In Proceedings of the 11th International Middleware Conference Industrial track, Middleware Industrial Track 7810, pages 11-16, New York, NY, USA. ACM.
  11. Verma, A., Kumar, G., Koller, R., and Sen, A. (2011). Cosmig: Modeling the impact of reconfiguration in a cloud. In Modeling, Analysis Simulation of Computer and Telecommunication Systems (MASCOTS), 2011 IEEE 19th International Symposium on, pages 3-11.
  12. Wang, Y., Chen, S., and Pedram, M. (2013). Service level agreement-based joint application environment assignment and resource allocation in cloud computing systems. In Green Technologies Conference, 2013 IEEE, pages 167-174.
  13. Yang, K., Gu, J., Zhao, T., and Sun, G. (2011). An optimized control strategy for load balancing based on live migration of virtual machine. In Chinagrid Conference (ChinaGrid), 2011 Sixth Annual, pages 141- 146.
  14. Zhu, X. and Singhal, S. (2001). Optimal resource assignment in internet data centers. In Modeling, Analysis and Simulation of Computer and Telecommunication Systems, 2001. Proceedings. Ninth International Symposium on, pages 61-69.
Download


Paper Citation


in Harvard Style

Seçinti C. and Ovatman T. (2014). On Optimizing Resource Allocation and Application Placement Costs in Cloud Systems . In Proceedings of the 4th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-019-2, pages 535-542. DOI: 10.5220/0004849605350542


in Bibtex Style

@conference{closer14,
author={Cihan Seçinti and Tolga Ovatman},
title={On Optimizing Resource Allocation and Application Placement Costs in Cloud Systems},
booktitle={Proceedings of the 4th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2014},
pages={535-542},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004849605350542},
isbn={978-989-758-019-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - On Optimizing Resource Allocation and Application Placement Costs in Cloud Systems
SN - 978-989-758-019-2
AU - Seçinti C.
AU - Ovatman T.
PY - 2014
SP - 535
EP - 542
DO - 10.5220/0004849605350542