Advertisement

Improving the Energy Efficiency in Cloud Computing Data Centres Through Resource Allocation Techniques

  • Belén Bermejo
  • Sonja Filiposka
  • Carlos Juiz
  • Beatriz Gómez
  • Carlos Guerrero
Chapter

Abstract

The growth of power consumption in Cloud Computing systems is one of the current concerns of systems designers. In previous years, several studies have been carried out in order to find new techniques to decrease the cloud power consumption. These techniques range from decisions on locations for data centres to techniques that enable efficient resource management. Resource Allocation, as a process of Resource Management, assigns available resources throughout the data centre in an efficient manner, minimizing the power consumption and maximizing the system performance. The contribution presented in this chapter is an overview of the Resource Management and Resource Allocation techniques, which contribute to the reduction of energy consumption without compromising the cloud user and provider constraints. We will present key concepts regarding energy consumption optimization in cloud data centres. Moreover, two practical cases are presented to illustrate the theoretical concepts of Resource Allocation. Finally, we discuss the open challenges that Resource Management must face in the coming years.

Keywords

Cloud Computing Resource Management Resource Allocation Energy Efficiency Data centres 

References

  1. 1.
    Akhter, N., & Othman, M. (2002). Energy aware resource allocation of cloud data center: Review and open issues. Cluster Computing, 19(3), 1163–1182. https://doi.org/10.1007/s10586-016-0579-4.
  2. 2.
    Akhter, N., & Othman, M. (2016). Energy aware resource allocation of cloud data center: Review and open issues. Cluster Computing, 19(3), 1163–1182. https://doi.org/10.1007/s10586-016-0579-4.
  3. 3.
    Al-Qawasmeh, A. M., Pasricha, S., Maciejewski, A. A., & Siegel, H. J. (2015). Power and thermal-aware workload allocation in heterogeneous data centers. IEEE Transactions on Computers, 64(2), 477–491. https://doi.org/10.1109/TC.2013.116.
  4. 4.
    Arjona Aroca, J., et al. (2015). Power-efficient assignment of virtual machines to physical machines. Future Generation Computer Systems, 54, pp.82–94. https://doi.org/10.1016/j.future.2015.01.006.
  5. 5.
    Armbrust, M., Fox, A., Griffth, R., Joseph, A. D., Katz, R., Konwinski, A., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58. https://doi.org/10.1145/1721654.1721672.
  6. 6.
    Arroba, P., et al. (2016). DVFS-Aware consolidation for energy-efficient clouds. Parallel Architectures and Compilation Techniques - In Conference Proceedings (pp. 494–495) PACT.Google Scholar
  7. 7.
    Archer, J., Boehm, A. (2009). Security guidance for critical areas of focus in cloud computing, Cloud Security Alliance 2, 1(76). Retrieved from https://downloads.cloudsecurityalliance.org/initiatives/guidance/csaguide.v3.0.pdf.
  8. 8.
    Arévalos, S., López-Pires, F., & Barán, B. (2016). A comparative evaluation of algorithms for auction-based cloud pricing prediction. In IEEE International Conference on Cloud Engineering (pp. 99–108).Google Scholar
  9. 9.
    Barroso, L. A., & Hölzle, U. (2007). The case of energy-proportional computing. Computer, 40(12), 33–37. https://doi.org/10.1109/MC.2007.443.
  10. 10.
    Baruchi, A., Toshimi Midorikawa, E., & Netto, M. A. (2014). Improving virtual machine live migration via application-level workload analysis. In: 10th International Conference on Network and Service Management (CNSM) and Workshop. https://doi.org/10.1109/CNSM.2014.7014153.
  11. 11.
    Beloglazov, A., & Buyya, R. (2010). Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing CCGRID ’10. Melbourne. https://doi.org/10.1109/CCGRID.2010.46.
  12. 12.
    Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28(5), 755–768. https://doi.org/10.1016/j.future.2011.04.017.
  13. 13.
    Beloglazov, A., & Buyya, R. (2012). Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurrency Computation Practice and Experience, 24(13), 1397–1420.CrossRefGoogle Scholar
  14. 14.
    Beloglazov, A., Buyya, R., Choon Lee, Y., & Zomaya, A. (2011). A taxonomy and survey of energy-efficient data centers and cloud computing systems. Advances in Computers, 82, pp. 47–111. Retrieved from http://beloglazov.info/papers/2011-advances-in-computers-taxonomy.pdf.Google Scholar
  15. 15.
    Buyya, R., & Sulistio, A. (2008). Service and utility oriented distributed computing systems: Challenges and opportunities for modeling and simulation communities. In 41st Annual Simulation Symposium. Retrieved from http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4494407.
  16. 16.
    Buyya, R., Vecchiola, C., & Selvi, S. T. (2013). Mastering cloud computing: Foundations and applications programming. Retrieved from http://store.elsevier.com/Mastering-Cloud-Computing/Rajkumar-Buyya/isbn-9780124095397/. ISBN:9780124095397
  17. 17.
    Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems, 25(6), 599–616. https://doi.org/10.1016/j.future.2008.12.001.
  18. 18.
    Calheiros, R.N., & Buyya, R. (2015). Energy-efficient scheduling of urgent bag-of-tasks applications in clouds through DVFS. In Proceedings of the International Conference on Cloud Computing Technology and Science, Cloud Com. pp. 342–349.Google Scholar
  19. 19.
    Cao, J., Wi, Y. and Li, M. (2012). Energy efficient allocation of virtual machines in cloud computing environments based on demand forecast. In International Conference on Grid and Pervasive Computing. https://doi.org/10.1007/978-3-642-30767-6_12.
  20. 20.
    Chen, H., Liu, X., Xu, H., & Wang, C. (2016). Cloud service broker based on dynamic game theory for bilateral SLA negotiation in cloud environment. International Journal of Grid and Distributed Computing, 9(9), 251–268. Retrieved from http://www.sersc.org/journals/IJGDC/vol9_no9/22.pdf.
  21. 21.
    Chen, H., Chiang, R.H.L. & Storey, V.C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), pp. 1165–1188. Retrieved from http://dl.acm.org/citation.cfm?id=2481683.Google Scholar
  22. 22.
    Dabbagh, M., et al. (2015). Energy-efficient resource allocation and provisioning framework for cloud data centers. IEEE Transactions on Network and Service Management, 12(3), 377–391.CrossRefGoogle Scholar
  23. 23.
    Emerson Network Power. (2014). New strategies for cutting data center energy costs and boosting capacity. Retrieved from http://www.emersonnetworkpower.com/documentation/en-us/latest-thinking/edc/documents/whiteGoogle Scholar
  24. 24.
    Ferreto, T. C., Netto, M. A., Calheiros, R. N., & De Rose, C. A. (2011). Server consolidation with migration control for virtualized data centers. Future Generation Computer Systems, 27(8), 1027–1034.CrossRefGoogle Scholar
  25. 25.
    Ficco, M., Esposito, C., Palmieri, F., & Castiglione, A. (2016). A coral-reefs and Game Theory-based approach for optimizing elastic cloud resource allocation. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2016.05.025.
  26. 26.
    Filiposka, S., Mishev, A., & Juiz, C. (2015). Community-based VM placement framework. The Journal of Supercomputing, 71(12), 4504–4528. https://doi.org/10.1007/s11227-015-1546-1.
  27. 27.
    Filiposka, S., Mishev, A., & Juiz, C. (2016). Balancing performances in online VM placement in ICT Innovations 2015. (pp. 153–162). Springer International Publishing.Google Scholar
  28. 28.
    Furuncu, E., & Sogukpinar, I. (2015). Scalable risk assessment method for cloud computing using game theory (CCRAM). Computer Standards & Interfaces, 38, 44–50. https://doi.org/10.1016/j.csi.2014.08.007.
  29. 29.
    Gerardus, J. (2011). Inter-cloud live migration of virtualization systems. U.S. Patent patent no. US20120311568 A1. Retrieved from https://www.google.ch/patents/US20120311568.
  30. 30.
    Geronimo, A., Brundo, R., & Becker, C. (2016). Order@Cloud: AVM organisation framework based on multi-objectives placement ranking. In Network Operations and Management Symposium NOMS, 2016 IEEE/IFIP. pp. 529–535. IEEE.Google Scholar
  31. 31.
    Goiri, I., Le, K., Beauchea, R., Nguyen, T., Haque, M., Guitart, J., et al. (2011). GreenSlot: Scheduling energy consumption in green datacenters. In: 24th ACM/IEEE International Supercomputing Conference for High Performance Computing, Networking, Storage and Analysis (SC’11). WA, USA: Seattle.Google Scholar
  32. 32.
    Hoelzle, U., & Barroso, L.A. (2009). The datacenter as a computer: An introduction to the design of warehouse-scale machines. Retrieved from http://dl.acm.org/citation.cfm?id=1643608.
  33. 33.
    Jennings, B., & Stadler, R. (2015). Resource Management in Clouds: Survey and Research Challenges. Journal of Network and Systems Management, 23(3), 567–619. https://doi.org/10.1007/s10922-014-9307-7.
  34. 34.
    Kitada, K., et al. (2016). Dynamic Power simulator utilizing computational fluid dynamics and machine learning for proposing task allocation in a data center. In pp. 87–94.Google Scholar
  35. 35.
    Khosravi, A., Garg, S. K., & Buyya, R. (2013). Energy and carbon efficient placement of virtual machines in distributed cloud data centers. pp. 317–328.Google Scholar
  36. 36.
    Mcbay, C., Parr, G., & Mcclean, S (2016). Energy saving in data center servers using optimal scheduling to ensure QoS. In pp. 57–60.Google Scholar
  37. 37.
    Mangla, N., Singh, M., & Rana, S. K. (2016). Resource scheduling in cloud environment: A Survey. Advances in Science and Technology Research Journal, 10(30), 38–50. https://doi.org/10.12913/22998624/62746.
  38. 38.
    Manvi, S. S., & Shyam, G. K. (2014). Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey. Journal of Network and Computer Applications, 41. https://doi.org/10.1016/j.jnca.2013.10.004.
  39. 39.
    Marinescu, D. C. (2013) Cloud computing: Theory and practice. Retrieved from http://www.sciencedirect.com/science/book/9780124046276.
  40. 40.
    Mastelic, T., & Brandic, I. (2015). Recent trends in energy-efficient cloud computing. IEEE Cloud Computing, 2(1), 40–47.CrossRefGoogle Scholar
  41. 41.
    Mell, P., & Grance, T. (2011). 800-145: The NIST Definition of Cloud Computing. Gaithersburg.Google Scholar
  42. 42.
    Palanisamy, B., Singh, A., & Liu, L. (2015). Cost-effective resource provisioning for mapreduce in a cloud. IEEE Transaction on Parallel and Distributed Systems, 26(5), 1265–1279.CrossRefGoogle Scholar
  43. 43.
    Papazoglou, M. P., Traveso, P., Dustdar, S., & Leymann, F. (2007). Service-oriented computing: State of the art and research challenges. IEEE Computer, 40(11), 38–45. https://doi.org/10.1109/MC.2007.400.
  44. 44.
    Park, J. G., Kim, J. M., Choi, H. & Woo, Y. C. (2009). Virtual machine migration in self-managing virtualized server environments. In11th International Conference on Advanced Communication Technology 2009. Retrieved from http://ieeexplore.ieee.org/document/4809490/.
  45. 45.
    Peng, M. et al. (2015). Energy-efficient resource assignment and power allocation in heterogeneous cloud radio access networks. In IEEE Transactions on Vehicular Technology. pp. 5275–5287.Google Scholar
  46. 46.
    Quang-Hung, N., Nien, P. D., Nam, N. H., Tuong, N. H., & Thoai, N. A. (2013). A genetic algorithm for power-aware virtual machine allocation in private cloud, In Information and Communication Technology-EurAsia Conference. Yogyakarta, Indonesia. https://doi.org/10.10071/978-3-642-36818-9_19.
  47. 47.
    Rak, M., Venticinque, S., & Mahr, T. (2011). Cloud application monitoring: The mOSAIC approach. In 1 Third IEEE International Conference on Cloud Computing Technology and Science. https://doi.org/10.1109/CloudCom.2011.117.
  48. 48.
    Raycroft, P., Jansen, R., Jarus, M., & Brenner, P. R. (2014). Performance bounded energy efficient virtual machine allocation in the global cloud. Sustainable Computing: Informatics and Systems, 4(1), 1–9. https://doi.org/10.1016/j.suscom.2013.07.001.
  49. 49.
    RightScale (2016). Cloud Computing Trends: 2016 State of the Cloud Survey. Retrieved from http://www.rightscale.com/blog/cloud-industry-insights/cloud-computing-trends-2016-state-cloud-survey.
  50. 50.
    Rodriguez, M. A., & Buyya, R. (2014). Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. In IEEE Transactions on Cloud Computing, 2(2), pp. 222–235. Retrieved from http://doi.ieeecomputersociety.org/.
  51. 51.
    Sahal, R. & Omara, F. A. (2014). Effective virtual machine configuration for cloud environment. In 9th International Conference on Informatics and Systems. https://doi.org/10.1109/INFOS.2014.7036720.
  52. 52.
    Serrano, D., Bouchenak, S., Kouki, Y., de Oliveira, F. A., Ledoux, T., Sopena, J., et al. (2016). SLA guarantees for cloud services. Future Generation Computer Systems, 54, 233–246. https://doi.org/10.1016/j.future.2015.03.018.
  53. 53.
    Sharifil, M., Salimi, H., & Najafzadeh, M. (2011). Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques. The Journal of Supercomputing, 61(1), 46–66. https://doi.org/10.1007/s11227-011-0658-5.
  54. 54.
    Singh, S., & Chana, I. (2016). A survey on resource scheduling in cloud computing: Issues and challenges. Journal of Grid Computing, 14(2), 217–264. https://doi.org/10.1007/s10723-015-9359-2.
  55. 55.
    Singh, S., & Chana, I. (2016). Cloud resource provisioning: survey, status and future research directions. Knowledge and Information Systems, 49(3), 1005–1069. https://doi.org/10.1007/s10115-016-0922-3.
  56. 56.
    Sola-Morena, J. M., Gilly, K., & Juiz, C. (2014). Sustainability in web server systems. Computers in Industry, 65(3), 401–407. https://doi.org/10.1016/j.compind.2013.11.009.
  57. 57.
    Strunk, A. (2012). Costs of virtual machine live migration: A survey. In IEEE Eighth World Congress on Services. Retrieved from http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6274069.
  58. 58.
    Vinothina, V., Dean, R. S., & Ganapathi, P. (2014). A survey on resource allocation strategies in cloud computing. International Journal of Advanced Computer Science and Applications, 3(6), pp. 97–104. Retrieved from http://thesai.org/Downloads/Volume3No6/Paper%2016-A%20Survey%20on%20Resource%20Allocation%20Strategies%20in%20Cloud%20Computing.pdf.
  59. 59.
    Voorsluys, W., Broberg, J., Venugopal, S., & Buyya, R. (2009). Cost of virtual machine live migration in clouds: A performance evaluation. In 1st International Conference on Cloud Computing. Retrieved from http://dl.acm.org/citation.cfm?id=1695684.
  60. 60.
    von Kistowski, J., Schreck, M., & Kounev, S., (2016). Predicting power consumption in virtualized environments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 79–93.Google Scholar
  61. 61.
    Wood, T. et al. (2009). Sandpiper: Black-box and gray-box resource management for virtual machines. Computer Networks, 53(17), pp. 2923–2938. https://doi.org/10.1016/j.comnet.2009.04.014.
  62. 62.
    Xu, X., Hu, H., Hu, N., & Ying, W. (2012). Cloud Task and Virtual Machine Allocation Strategy in Cloud Computing Environment. Network Computing and Information Security (pp. 113–120). Berlin Heidelberg: Springer.CrossRefGoogle Scholar
  63. 63.
    Xu, M., Dastjerdi, A. V., & Buyya, R. (2016). Energy efficient scheduling of cloud application components with brownout. CoRR, (August), pp. 1–12.Google Scholar
  64. 64.
    Zhan, Z.-H., Liu, X.-F., Gong, Y.-J., Zhang, J., Chung, H. S.-H., & Li, Y. (2015). Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Surveys (CSUR), 47(4). https://doi.org/10.1145/2788397.

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Belén Bermejo
    • 1
  • Sonja Filiposka
    • 2
  • Carlos Juiz
    • 1
  • Beatriz Gómez
    • 1
  • Carlos Guerrero
    • 1
  1. 1.Computer Science DepartmentUniversity of the Balearic IslandsPalmaSpain
  2. 2.Faculty of Computer Science and EngineeringUniversity Ss. Cyril and MethodiusSkopjeMacedonia

Personalised recommendations