Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Energy-Efficient Resource Management of Virtual Machine in Cloud Infrastructure

  • Chapter
  • First Online:
New Frontiers in Cloud Computing and Internet of Things

Part of the book series: Internet of Things ((ITTCC))

  • 520 Accesses

Abstract

Large organizations and business centers use cloud services as a computing technology for their business purposes. Nevertheless, the use of cloud computing has resulted in creation of huge data centers. The major issues that occur in data centers are managing the infrastructural resources, maintaining the cost of applications (tasks), security, and high usage of energy. It represents cloud computing provides resources based on the principle of virtualization and pay-as-you-go model. The resources such as storage, CPU, network, and memory that are available in virtual machine need to be monitored frequently. This resources management has become a wide area of research. The optimization algorithm called Genetically Enhanced Shuffling Frog Leaping Algorithm (GESFLA) is implemented for the VM allocation and execution of tasks. The idea behind the proposed work is to address some of the issues such as minimizing the power consumption, costs of the running application, and to optimize the resource usage. Cloudsim toolkit is used to find the efficiency of this proposed algorithm with a Genetic Algorithm and Particle Swarm Optimization (GAPSO). Experiments are conducted using PlanetLab workload and Google Cluster Datasets which is very huge data. The experimental results indicate GESFLA’s superiority over GAPSO in terms of resource usage ratio, time to migrate the VMs, and total energy consumption.The proposed algorithm increases the performance of data center by maximizing resource utilization by 16% and migration time by 17%. Also, energy consumption is reduced in comparison with the existing algorithm GAPSO by 6%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. H. Priyanka, Analytics of application resource utilization within the virtual machine. Int. J. Sci. Res. 5(4), 1690–1693 (2016)

    MathSciNet  Google Scholar 

  2. H. Hajj, W. El-Hajj, M. Dabbagh, T.R. Arabi, An algorithm centric energy-aware design methodology. IEEE Trans. Very Large Scale Integr. Syst. 22(11), 2431–2435 (2014)

    Article  Google Scholar 

  3. F. Ramezani, J. Lu, F.K. Hussain, Task-based system load balancing in cloud computing using particle swarm optimization. Int. J. Parallel Prog., Springer 42, 739–754 (2014)

    Article  Google Scholar 

  4. L. Guo, S. Zhao, S. Shen, C. Jiang, Task scheduling optimization in cloud computing based on a heuristic algorithm. J. Networks 7(3), 547–553 (2012)

    Article  Google Scholar 

  5. H. Priyanka, M. Cherian, The challenges in virtual machine live migration and resource management. Int. J. Eng. Res. Technol. 8(11), 5 (2020)

    Google Scholar 

  6. N.K. Sharma, G. Ram Mohana Reddy, Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans. Serv. Comput. 12(1), 158–171 (2019)

    Article  Google Scholar 

  7. Y. Ge, G. Wei, GA-based task scheduler for the cloud computing systems, in Proceedings of the International Conference on Web Information Systems and Mining (WISM ’10), vol. 2, (IEEE, 2010), pp. 181–186

    Google Scholar 

  8. G. Giftson Samuel, C. Christober Asi Rajan, Hybrid: Particle Swarm Optimization–Genetic Algorithm and Particle Swarm Optimization–Shuffled Frog Leaping Algorithm for long-term generator maintenance scheduling. Int. J. Elect. Power Energy Syst., Elsevier 65, 432–442 (2015)

    Article  Google Scholar 

  9. E.G. Coffman Jr., M.R. Garey, D.S. Johnson, Approximation algorithms for bin packing: A survey, in Approximation Algorithms for NP-Hard Problems, ed. by D. S. Hochbaum, (PWS Publishing Co, Boston, 1997), pp. 46–93

    Google Scholar 

  10. Y. Ajiro, A. Tanaka, Improving packing algorithms for server consolidation, in Proceedings of the 33rd International Computer Measurement Group Conference, December 2–7, 2007, San Diego, CA, USA, (DBLP, 2007), pp. 399–406

    Google Scholar 

  11. A. Beloglazov, R. Buyya, Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans. Parallel Distrib. Syst. 24(7), 1366–1379 (2013)

    Article  Google Scholar 

  12. K. Dasgupta, B. Mandal, P. Dutta, J.K. Mandal, S. Dam, A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technol. 10, 340–347 (2013)

    Article  Google Scholar 

  13. A.-P. Xiong, C.-X. Xu, Energy efficient multiresource allocation of virtual machine based on PSO in cloud data center. Math. Probl. Eng. 2014, 1–8 (2014)

    Google Scholar 

  14. W.-T. Wen, C.-D. Wang, D.-S. Wu, Y.-Y. Xie, An ACO-based scheduling strategy on load balancing in cloud computing environment, in Ninth IEEE International Conference on Frontier of Computer Science and Technology, vol. 6, (IEEE, 2015), pp. 364–369

    Google Scholar 

  15. S. Wang, Z. Liu, Z. Zheng, Q. Sun, F. Yang, Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centers, in Proceedings of the IEEE International Conference on Parallel and Distributed Systems, (IEEE, 2013), pp. 102–109

    Google Scholar 

  16. H. Xu, B. Yang, W. Qi, E. Ahene, A multi-objective optimization approach to workflow scheduling in clouds considering fault recovery. KSII Trans. Internet Inf. Syst. 10(3), 976–994 (2016)

    Google Scholar 

  17. S. Chitra, B. Madhusudhanan, G.R. Sakthidharan, P. Saravanan, Local minima jump PSO for workflow scheduling in cloud computing environments, in Advances in Computer Science and its Applications. Lecture Notes in Electrical Engineering, vol. 279, (Springer, 2014), pp. 1225–1234

    Chapter  Google Scholar 

  18. C. Reiss, J. Wilkes, J.L. Hellerstein, Google cluster-usage traces: Format+ schema. Technical report at https://github.com/google/clusterdata, Google, Mountain View, CA, USA, Revised 2014-11-17 for version 2.2, Nov. 2011

  19. Y. Mao, X. Chen, X. Li, Max–Min task scheduling algorithm for load balance in cloud computing, in Proceedings of International Conference on Computer Science and Information Technology. Advances in Intelligent Systems and Computing, ed. by S. Patnaik, X. Li, vol. 255, (Springer, New Delhi, 2012)

    Google Scholar 

  20. B. Santhosh, D.H. Manjaiah, A hybrid AvgTask-Min and Max-Min algorithm for scheduling tasks in cloud computing, in International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, (IEEE, 2015), pp. 325–328. https://doi.org/10.1109/ICCICCT.2015.7475298

    Chapter  Google Scholar 

  21. J. Wilkes, More Google cluster data. Google research blog (Nov. 2011). Posted at http://googleresearch.blogspot.com/2011/11/more-googlecluster-data.html

  22. J. Wilkes, Google cluster-usage traces v3. Technical report at https://github.com/google/cluster-data, Google, Mountain View, CA, USA, Nov. 2019

  23. H. Priyanka, M. Cherian, Efficient utilization of resources of virtual machines through monitoring the cloud data center, in International Conference on Communication, Computing and Electronics Systems. Lecture Notes in Electrical Engineering, ed. by V. Bindhu, J. Chen, J. Tavares, vol. 637, (Springer, Singapore, 2020), pp. 645–653

    Chapter  Google Scholar 

  24. H. Priyanka, M. Cherian, Novel approach to virtual machine migration in cloud computing environment – A survey. Int. J. Sci. Rep. 7(1), 81–84 (2018)

    Google Scholar 

  25. L. Weining, F. Ta, Live migration of virtual machine based on recovering system and CPU scheduling, in 6th IEEE joint International Information Technology and Artificial Intelligence Conference, Piscataway, NJ, USA, (IEEE, 2009), pp. 303–305

    Google Scholar 

  26. S.B. Melhem, A. Agarwal, N. Goel, M. Zaman, Markov prediction model for host load detection and VM placement in live migration. IEEE Access 6, 7190–7205 (2017)

    Article  Google Scholar 

  27. A. Kishor, R. Niyogi, Multi-objective load balancing in distributed computing environment: An evolutionary computing approach, in Proceedings of the 35th Annual ACM Symposium on Applied Computing, (Association for Computing Machinery, Brno, 2020), pp. 170–172

    Chapter  Google Scholar 

  28. A. Beloglazov, R. Buyya, Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers, in Concurrency and Computation: Practice and Experience, vol. 24, No. 13, (Wiley Press, New York, 2011). https://doi.org/10.1002/cpe.1867

    Chapter  Google Scholar 

  29. R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F. de Rose, R. Buyya, CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)

    Article  Google Scholar 

  30. J. Singh, J. Chen, Optimizing energy consumption for cloud computing: A cluster and migration based approach (CMBA), in Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence, vol. 22, No. 11, (Association for Computing Machinery (ACM), 2019), pp. 28–32

    Chapter  Google Scholar 

  31. S. Rahman, A. Gupta, M. Tornatore, B. Mukherjee, Dynamic workload migration over backbone network to minimize data center electricity cost. IEEE Trans. Green Commun. Netw. 2, 570–579 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSE R&D Centre, Dr. Ambedkar Institute of Technology, Bengaluru, Karnataka, India

    H. Priyanka

  2. Department of CSE, Dr. Ambedkar Institute of Technology, Bengaluru, Karnataka, India

    Mary Cherian

Authors
  1. H. Priyanka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mary Cherian
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computing and Information Systems, University of Melbourne, Parkville, VIC, Australia

    Rajkumar Buyya

  2. Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

  3. University of Calabria, Rende, Cosenza, Italy

    Giancarlo Fortino

  4. Department of Computer Science and Communication, Østfold University College, Halden, Norway

    Sanjay Misra

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Priyanka, H., Cherian, M. (2022). Energy-Efficient Resource Management of Virtual Machine in Cloud Infrastructure. In: Buyya, R., Garg, L., Fortino, G., Misra, S. (eds) New Frontiers in Cloud Computing and Internet of Things. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-05528-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-05528-7_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05527-0

  • Online ISBN: 978-3-031-05528-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation