Skip to main content
SpringerLink
Log in

Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter: A Panacea to Green Computing

  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters. Meanwhile, datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous levels. In cloud datacenter, Virtual Machines (VMs) need to be allocated on various Physical Machines (PMs) in order to minimize resource wastage and increase energy efficiency. Resource allocation problem is NP-hard. Hence finding an exact solution is complicated especially for large-scale datacenters. In this context, this paper proposes an Energy-oriented Flower Pollination Algorithm (E-FPA) for VM allocation in cloud datacenter environments. A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a PM. The allocation uses a strategy called Dynamic Switching Probability (DSP). The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local search. It considers a processor, storage, and memory constraints of a PM while prioritizing energy-oriented allocation for a set of VMs. Simulations performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware (GAPA) by 21.8%, Order of Exchange Migration (OEM) ant colony system by 21.5%, and First Fit Decreasing (FFD) by 24.9%. Therefore, E-FPA significantly improves datacenter performance and thus, enhances environmental sustainability.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Younge A J, von Laszewski G, Wang L, Lopez-Alarcon S, Carithers W. Efficient resource management for cloud computing environments. Proceeding of IEEE International on Green Computing, Chicago, Illinois, USA, 2010, 357–364.

    Google Scholar 

  2. Xiong A, Xu P, Cheng X. Energy efficient multisource of virtual machine based on PSO in cloud data center. Mathematical Problems in Engineering, 2014, 3, 86–99.

    Google Scholar 

  3. Oppong E, Khaddaj S, Elasriss H E. Cloud computing: Resource management and service allocation. Proceeding of IEEE International Symposium on Distributed Computing and Applications to Business, Engineering & Science, Kingston upon Thames, Surrey, UK, 2013, 142–145.

    Google Scholar 

  4. Beloglazov A, Abawajy J, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 2012, 28, 755–768.

    Article  Google Scholar 

  5. Foster I, Zhao Y, Raicu I, Lu S. Cloud computing and grid computing 360-degree compared. Grid Computing Environments Workshop, Austin, Texas, USA, 2008, 1–10.

    Google Scholar 

  6. Usman M J, Ismail A S, Chizari H, Gital A Y, Aliyu A. A conceptual framwork for realizing energy efficient resource allocation in cloud data centre. Indian Journal of Science and Technology, 2016, 46, 210–221.

    Google Scholar 

  7. Wadhwa B, Verma A. Energy and carbon efficient VM placement and migration technique for green cloud datacenters. Proceeding of IEEE International Conference on Advances in Computing, Communications and Informatics, New Delhi, India, 2014, 189–183.

    Google Scholar 

  8. Michael A M, Krieger K. Server Power Measurement. Patent 7768254, Washington DC, USA, 2010.

    Google Scholar 

  9. Han G, Que W, Jia G, Zhang W. Resource-utilization-aware energy efficient server consolidation algorithm for green computing in IIOT. Journal of Network and Computer Applications, 2017, 103, 205–214.

    Article  Google Scholar 

  10. Buyya R, Beloglazov A, Abawajy J. Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges. International Conference on Parallel and Distributed Processing Techniques and Applications, 2010, https://doi.org/arxiv.org/abs/1006.0308.

    Google Scholar 

  11. Madni H, Shafie A L M, Yahaya C, Abdulhamid S M. An appraisal of meta-heuristic resource allocation techniques for IaaS cloud. Indian Journal of Science and Technology, 2016, 4, 157–163.

    Google Scholar 

  12. Beloglazov A. Energy-efficient Management of Virtual Machines in Data Centers for Cloud Computing, PhD Thesis, University of Melbourne, Australia, 2013.

    Google Scholar 

  13. Quang-Hung N, Nien P D, Nam N H, Tuong N H, Thoai N. A genetic algorithm for power-aware virtual machine allocation in private cloud. Information and Communication Technology, 2013, 7804, 183–191.

    Google Scholar 

  14. Rodero I, Jaramillo J, Quiroz A, Parashar M, Guim F, Poole S. Energy-efficient application-aware online provisioning for virtualized clouds and data centers. Proceeding of IEEE International on Green Computing, Chicago, USA, 2010, 31–45.

    Google Scholar 

  15. Sharma N K, Reddy G M. Novel energy efficient virtual machine allocation at data center using genetic algorithm. International Conference on Signal Processing, Communication and Networking, Chennai, India, 2015, 111–115.

    Google Scholar 

  16. Deore S S, Patil A N, Bhargava R. Energy-efficient scheduling scheme for virtual machines in cloud computing. International Journal of Computer Applications, 2012, 56, 123–131.

    Article  Google Scholar 

  17. Moganarangan N, Babukarthik R, Bhuvaneswari S, Basha M S, Dhavachelvan P. Novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach. Journal of King Saud University — Computer and Information Sciences, 2016, 28, 55–67.

    Article  Google Scholar 

  18. Phan D H, Suzuki J, Carroll R, Balasubramaniam S, Donnelly W, Botvich D. Evolutionary multiobjective optimization for green clouds. Proceedings of 14th Annual Conference Companion on Genetic and Evolutionary Computation, Philadelphia, Pennsylvania, USA, 2012, 19–26.

    Google Scholar 

  19. Shu W, Wang W, Wang Y. A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing. Journal on Wireless Communications and Networking, 2014, 1, 64–73.

    Article  Google Scholar 

  20. Kansal N J, Chana I. Energy-aware virtual machine migration for cloud computing - A firefly optimization approach. Journal of Grid Computing, 2016, 2, 327–345.

    Article  Google Scholar 

  21. Tsai C W, Rodrigues J J P C. Metaheuristic scheduling for cloud: A survey. IEEE Systems Journal, 2014, 8, 279–291.

    Article  Google Scholar 

  22. Joseph C T, Chandrasekaran K, Cyriac R. A novel family genetic approach for virtual machine allocation. Procedia Computer Science, 2015, 46, 558–565.

    Article  Google Scholar 

  23. Wu G, Tang M, Tian Y C, Li W. Energy-efficient virtual machine placement in data centers by genetic algorithm. International Conference on Neural Information Processing, 2012, 7665, 315–323.

    Google Scholar 

  24. Wang X, Wang Y, Zhu H. Energy-efficient multi-job scheduling model for cloud computing and its genetic algorithm. Mathematical Problems in Engineering, 2012, https://doi.org/10.1155/2012/589243.

    Google Scholar 

  25. Pacini E, Mateos C, Garino C G. Dynamic scheduling based on particle swarm optimization for cloud-based scientific experiments. CLEI Electronic Journal, 2014, 14, 2.

    Google Scholar 

  26. Wang S, Liu Z, Zheng Z, Sun Q, Yang F. Particle swarm optimization for energy-aware virtual machine placement optimization in virtualized data centers. Proceeding of IEEE International Conference on Parallel and Distributed Systems, Seoul, South Korea, 2013, 102–109.

    Google Scholar 

  27. Liu X F, Zhan Z H, Deng J D, Li Y, Gu T, Zhang J. An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Transactions on Evolutionary Computation, 2018, 22, 113–128.

    Article  Google Scholar 

  28. Kaur T, Chana I. Energy efficiency techniques in cloud computing: A survey and taxonomy. ACM Computing Surveys, 2015, 48, 22.

    Article  Google Scholar 

  29. Vouk A M. Cloud computing — Issues, research and implementations. Journal of Computing and Information Technology, 2008, 16, 235–246.

    Article  Google Scholar 

  30. Li B, Li J, Huai J, Wo T, Li Q, Zhong L. EnaCloud: An energy-saving application live placement approach for cloud computing environments. Proceeding of IEEE International Conference on Cloud Computing, Banglore, India, 2009, 17–24.

    Google Scholar 

  31. Shu W, Wang W, Wang Y. A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing. Journal on Wireless Communications and Networking, 2014, https://doi.org/10.1186/1687-1499-2014-64.

    Google Scholar 

  32. Yang X S. Flower pollination algorithm for global optimization. Unconventional Computation and Natural Computation, 2012, 7445, 240–249.

    Article  MATH  Google Scholar 

  33. Yang X S, Karamanoglu M, He, X. Multi-objective flower algorithm for optimization. Procedia Computer Science, 2013, 18, 861–868.

    Article  Google Scholar 

  34. Abdelaziz A Y, Ali E S, Elazim S M A. Flower pollination algorithm and loss sensitivity factors for optimal sizing and placement of capacitors in radial distribution systems. International Journal of Electrical Power & Energy Systems, 2016, 78, 207–214.

    Article  Google Scholar 

  35. Abdel-Raouf O, El-Henawy I, Abdel-Baset M. A novel hybrid flower pollination algorithm with chaotic harmony search for solving sudoku puzzles. International Journal of Modern Education and Computer Science, 2014, 3, 38–43.

    Article  Google Scholar 

  36. Ochoa A, Gonzalez S, Margain L, Padilla T, Castillo O, Melin P. Implementing flower multi-objective algorithm for selection of university academic credits. Proceeding of IEEE International Conference on Nature and Biologically Inspired Computing Cloud Computing, Porto, Portugal, 2014, 7–11.

    Google Scholar 

  37. Pan J S, Dao T K, Pan T S, Nguyen T T, Chu S C, Roddick J F. An improvement of flower pollination algorithm for node localization optimization in WSN. Journal of Information Hiding and Multimedia Signal Processing, 2017, 8, 486–499.

    Google Scholar 

  38. Babu M, Jaisiva S. Optimal reactive power flow by flower pollination algorithm. Asian Journal of Applied Science and Technology, 2017, 3, 137–141.

    Google Scholar 

  39. Xiong A P, Xu C X. Energy efficient multiresource allocation of virtual machine based on PSO in cloud data center. Mathematical Problems in Engineering, 2014, 3, 86–99.

    Google Scholar 

  40. Lin W, Xu S, He L, Li J. Multi-resource scheduling and power simulation for cloud computing. Information Sciences, 2017, 397-398, 168–186.

    Article  Google Scholar 

  41. Jamil M, Yang X S. A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 2013, https://doi.org/10.1504/IJMMNO.2013.055204.

    Google Scholar 

  42. Wang R, Zhou Y. Flower pollination algorithm with dimension by dimension improvement. Mathematical Problems in Engineering, 2014, https://doi.org/10.1155/2014/481791.

    Google Scholar 

  43. Park K S, Pai V S. CoMon: A mostly-scalable monitoring system for PlanetLab. Operating Systems Review, 2006, 1, 65–74.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Maths, Bauchi State University Gadau, Bauchi, 81007, Nigeria

    Mohammed Joda Usman & Ahmed Aliyu

  2. Department of Computer Science, Universiti Teknology Malaysia, Skudai Johor, 81310, Malaysia

    Abdul Samad Ismail

  3. School of Computing and Technology, Park Campus, University of Gloucestershire, Cheltenham, GL50 4SH, UK

    Hassan Chizari

  4. Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, 00233, Ghana

    Gaddafi Abdul-Salaam

  5. Department of Maths and Computer, Federal College of Education Technical Gombe, Gombe, 072158, Nigeria

    Ali Muhammad Usman

  6. Department of Maths, Abubakar Tafawa Balewa University Bauchi, Bauchi, 81027, Nigeria

    Abdulsalam Yau Gital

  7. Department of Computer and Information Technology, Northumbria University, Newcastle, NE1 8ST, UK

    Omprakash Kaiwartya

Authors
  1. Mohammed Joda Usman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Samad Ismail
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hassan Chizari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gaddafi Abdul-Salaam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ali Muhammad Usman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Abdulsalam Yau Gital
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Omprakash Kaiwartya
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ahmed Aliyu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammed Joda Usman.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Usman, M.J., Ismail, A.S., Chizari, H. et al. Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter: A Panacea to Green Computing. J Bionic Eng 16, 354–366 (2019). https://doi.org/10.1007/s42235-019-0030-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42235-019-0030-7

Keywords

Search

Navigation