Energy-efficient Task Scheduling in Data Centers

Yousri Mhedheb, Achim Streit

Abstract

A data center is often also a Cloud center, which delivers its computational and storage capacity as services. To enable on-demand resource provision with elasticity and high reliability, the host machines in data centers are usually virtualized, which brings a challenging research topic, i.e., how to schedule the virtual machines (VM) on the hosts for energy efficiency. The goal of this Work is to ameliorate, through scheduling, the energy-efficiency of data center. To support this work a novel VM scheduling mechanism design and implementation will be proposed. This mechanism addresses on both load-balancing and temperature-awareness with a final goal of reducing the energy consumption of a data centre. Our scheduling scheme selects a physical machine to host a virtual machine based on the user requirements, the load on the hosts and the temperature of the hosts, while maintaining the quality of the service. The proposed scheduling mechanism on CloudSim will be finally validated, a well-known simulator that models data centers provisioning Infrastructure as a Service. For a comparative study, we also implemented other scheduling algorithms i.e., non power control, DVFS and power aware ThrMu. The experimental results show that the proposed scheduling scheme, combining the power-aware with the thermal-aware scheduling strategies, significantly reduces the energy consumption of a given Data Center because of its thermal-aware strategy and the support of VM migration mechanisms.

References

  1. Kahn, S., Bilal, K., Zhang, L., Li, H., Hayat, K., Madani, S., Min-Allah, N., Wang, L., Chen, D., Iqbal, M., Xu, C., and Zomaya, A. (2013). Quantitative Comparisons of the State of the Art Data Center Architectures. Concurrency and Computation: Practice & Experience. DOI=10.1002/cpe.2963.
  2. Keahey, K. and Freeman, T. (2008). Science Clouds: Early Experiences in Cloud Computing for Scientific Applications. In Proceedings of the First Workshop on Cloud Computing and its Applications.
  3. Kim, D., Kim, H., Jeon, M., Seo, E., and Lee, J. (2008). Guest-Aware Priority-based Virtual Machine Scheduling for Highly Consolidated Server. In Proceedings of the 14th International Conference on Parallel and Distributed Computing (Euro-Par 2008), pages 285- 294.
  4. Knauth, T. and Fetzer, C. (2012). Energy-aware scheduling for infrastructure clouds. In Proceedings of the IEEE International Conference on Cloud Computing Technology and Science, pages 58-65.
  5. Kolodziej, J., Khan, S., Wang, L., Kisiel-Dorohinicki, M., and Madani, S. (2012). Security, Energy, and Performance-aware Resource Allocation Mechanisms for Computational Grids. Future Generation Computer Systems. DOI: 10.1016/j.future.2012.09.009.
  6. Kolodziej, J., Khan, S., Wang, L., and Zomaya, A. (2013). Energy Efficient Genetic-Based Schedulers in Computational Grids. Concurrency and Computation: Practice & Experience. DOI=10.1002/cpe.2839.
  7. Li, K., Xu, G., Zhao, G., Dong, Y., and Wang, D. (2011). Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization. In Proceedings of the Six Annual Chinagrid Conference, pages 3-9.
  8. Mell, P. and Grance, T. The NIST Definition of Cloud Computing. [Online]. http://csrc.nist.gov/publications/ drafts/800-145/Draft-SP-800-145 cloud-definition.pdf.
  9. Menzel, M. and Ranjan, R. (2012). CloudGenius: Decision Support for Web Service Cloud Migration. In Proceedings of the International ACM Conference on World Wide Web (WWW 2012), Lyon, France.
  10. Mhedheb, Y., Jrad, F., Tao, J., Kolodziej, J., and Streit, A. (2013). Load and Thermal-Aware VM Scheduling on the Cloud. In Algorithms and Architectures for Parallel Processing, pages 101-114.
  11. Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., and Zagorodnov, D. (2008). The Eucalyptus Open-source Cloud-computing System. In Proceedings of Cloud Computing and Its Applications. Available: http://eucalyptus.cs. ucsb.edu/wiki/Presentations.
  12. Openstack (2013). OpenStack Cloud Software. [Online] http://openstack.org/.
  13. Ranjan, R., Buyya, R., and Harwood, A. (2005). A Case for Cooperative and Incentive Based Coupling of Distributed Clusters. In Proceedings of the 7th IEEE International Conference on Cluster Computing (Cluster 2005), pages 1-11, Boston, Massachusetts, USA.
  14. Ranjan, R., Harwood, A., and Buyya, R. (2006). A SLABased Coordinated Super scheduling Scheme and Performance for Computational Grids. In Proceedings of the 8th IEEE International Conference on Cluster Computing (Cluster 2006), pages 1-8, Barcelona, Spain.
  15. Skadron, K., Abdelzaher, T., and Stan, M. R. (2002). Control-theoretic techniques and thermal-rc modeling for accurate and localized dynamic thermal management. In Proceedings of the 8th International Symposium on High-Performance Computer Architecture, HPCA 7802, pages 17-, Washington, DC, USA. IEEE Computer Society.
  16. Sotomayor, B., Montero, R., Llorente, I., and Foster, I. (2008). Capacity Leasing in Cloud Systems using the OpenNebula Engine. In The First Workshop on Cloud Computing and its Applications.
  17. spec08. SpecPower08. [Online] http://www.spec.org.
  18. Takouna, I., Dawoud, W., and Meinel, C. (2011). Efficient Virtual Machine Scheduling-policy for Virtualized heterogeneous Multicore Systems. In Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA2011).
  19. Wang, L. and Khan, S. (2013). Review of performance metrics for green data centers: a taxonomy study. The Journal of Supercomputing, 63(3):639-656.
  20. Wang, L., Laszewski, G., Younge, A., He, X., Kunze, M., Tao, J., and Fu, C. (2010). Cloud Computing: a Perspective Study. New Generation Computing, 28(2):137-146.
  21. Wang, L., von Laszewski, G., Huang, F., Dayal, J., Frulani, T., and Fox, G. (2011). Task scheduling with ann-based temperature prediction in a data center: a simulation-based study. Engineering with Computers, 27(4):381-391.
  22. Wang, Y., Wang, X., and Chen, Y. (2012). Energyefficient virtual machine scheduling in performanceasymmetric multi-core architectures. In Proceedings of the 8th international conference on Network and service management and 2012 workshop on systems virtualiztion management, pages 288-294.
Download


Paper Citation


in Harvard Style

Mhedheb Y. and Streit A. (2016). Energy-efficient Task Scheduling in Data Centers . In Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-182-3, pages 273-282. DOI: 10.5220/0005880802730282


in Bibtex Style

@conference{closer16,
author={Yousri Mhedheb and Achim Streit},
title={Energy-efficient Task Scheduling in Data Centers},
booktitle={Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2016},
pages={273-282},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005880802730282},
isbn={978-989-758-182-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Energy-efficient Task Scheduling in Data Centers
SN - 978-989-758-182-3
AU - Mhedheb Y.
AU - Streit A.
PY - 2016
SP - 273
EP - 282
DO - 10.5220/0005880802730282