Smart elastic scheduling algorithm for virtual machine migration in cloud computing

  • Heba Nashaat
  • Nesma Ashry
  • Rawya RizkEmail author


Cloud Computing has the facility to transform a large part of information technology into services in which computer resources are virtualized and made available as a utility service. From here comes the importance of scheduling virtual resources to get the maximum utilization of physical resources. This paper presents two cooperative algorithms: a Smart Elastic Scheduling Algorithm (SESA) and an Adaptive Worst Fit Decreasing Virtual Machine Placement (AWFDVP) algorithm. The proposed algorithms work to dynamically distribute the cloud system’s physical resources to obtain a load-balanced consolidated system with minimal used power, memory, and processing time. SESA arranges VMs in clusters based on their memory and CPU parameters’ value. Then it deals with the colocated VMs that share some of their memory pages and located on the same physical machine, as a group. Then the migration decision is made based on the evaluation for the entire system by AWFDVP. This process minimizes the number of migrations among the system, saves the consumed power, and prevents performance degradation for the VM while preserving the load-balance state of the entire system. SESA reduces the power consumption in the cloud system by 28.1%, the number of migrations by 57.77%, and performance degradation by 57.1%.


Cloud computing Colocated virtual machines Live migration Load balancing Resource scheduling 


  1. 1.
    Gorelik E (2013) Cloud computing models, comparison of cloud computing service and deployment models. The MIT Sloan School of Management and The MIT Engineering Systems, Massachusetts Institute of TechnologyGoogle Scholar
  2. 2.
    Hashem W, Nashaat H, Rizk R (2017) Honey bee based load balancing in cloud computing. KSII Trans Internet Inf Syst (TIIS) 11:5694Google Scholar
  3. 3.
    Gamal M, Rizk R, Mahdi H (2017) Bio-inspired load balancing algorithm in cloud computing. In: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics (AISI), Cairo, Egypt, pp 579–589Google Scholar
  4. 4.
    López-Pires F, Barán B (2017) Many-objective virtual machine placement. J Grid Comput 15(2):161–176CrossRefGoogle Scholar
  5. 5.
    Strunk A (2012) Costs of virtual machine live migration: a survey. In: Proceedings of IEEE 8th World Congress on Services (SERVICES), Honolulu, HI, USA, pp 323–329Google Scholar
  6. 6.
    Mishra M, Das A, Kulkarni P, Sahoo A (2012) Dynamic resource management using virtual machine migrations. IEE018E Commun Mag 50(9):34–40CrossRefGoogle Scholar
  7. 7.
    Ren R, Tang X, Li Y, Cai W (2017) Competitiveness of dynamic bin packing for online cloud server allocation. IEEE/ACM Trans Netw 25(3):1324–1331CrossRefGoogle Scholar
  8. 8.
    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. J Concurr Comput Pract Exp 24(13):1397–1420CrossRefGoogle Scholar
  9. 9.
    Deshp U, Wang X, Gopalan K (2011) Live gang migration of virtual machines. In: Proceedings of the 20th International Symposium on High Performance Distributed Computing, San Joes, CA, USA, pp 135–146Google Scholar
  10. 10.
    Zhen X, Weijia S, Qi C (2013) Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans Parallel Distrib Syst 24(6):1107–1117CrossRefGoogle Scholar
  11. 11.
    Sheng D, Cho-Li W (2013) Dynamic optimization of multiattribute resource allocation in self-organizing clouds. IEEE Trans Parallel Distrib Syst 24(3):464–478CrossRefGoogle Scholar
  12. 12.
    Gouda KC, Radhika TV, Akshatha M (2013) Priority based resource allocation model for cloud computing (IJSETR). Int J Sci Eng Technol Res 2(1):215Google Scholar
  13. 13.
    Abirami SP, Ramanathan S (2012) Linear scheduling strategy for resource allocation in cloud environment. Int J Cloud Comput Serv Archit (IJCCSA) 2(1):9Google Scholar
  14. 14.
    Omara FA, Khattab SM, Sahal R (2014) Optimum resource allocation of database in cloud computing. Egypt Inform J 15(1):1CrossRefGoogle Scholar
  15. 15.
    Abar S, Lemarinier P, Theodoropoulos GK, O’Hare GMP (2014) Automated dynamic resource provisioning and monitoring in virtualized large-scale datacenter. In: Proceedings of IEEE 28th International Conference on Advanced Information Networking and Applications (AINA), Victoria, Canada, BC, pp 961–970Google Scholar
  16. 16.
    Yexi J, Chang-Shing P, Tao L, Chang RN (2013) Cloud analytics for capacity planning and instant VM provisioning. IEEE Trans Netw Serv Manag 10(3):312–325CrossRefGoogle Scholar
  17. 17.
    Minarolli D, Freisleben B (2014) Distributed resource allocation to virtual machines via artificial neural networks. In: Proceedings of the 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), Torino, Italy, pp 490–499Google Scholar
  18. 18.
    Mandal U, Habib M, Shuqiang Z, Mukherjee B, Tornatore M (2013) Greening the cloud using renewable-energy-aware service migration. J IEEE Netw 27(6):36–43CrossRefGoogle Scholar
  19. 19.
    Jie Z, Ng TSE, Sripanidkulchai K, Zhaolei L (2013) Pacer: a progress management system for live virtual machine migration in cloud computing. IEEE Trans Netw Serv Manag 10(4):369–382CrossRefGoogle Scholar
  20. 20.
    Singh S, Chana I (2016) A survey on resource scheduling in cloud computing: issues and challenges. J Grid Comput 14(2):217–264CrossRefGoogle Scholar
  21. 21.
    Rasmussen MATRV (2008) Round robin scheduling—a survey. Eur J Oper Res 188(3):617–636MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Hottmar V, Adamec B (2012) Analytical model of a weighted round robin service system. J Electr Comput Eng 2012:374961MathSciNetzbMATHGoogle Scholar
  23. 23.
    Chen B, Fu X, Zhang X, Su L, Wu D (2007) Design and implementation of intranet security audit system based on load balancing. In: Proceedings of IEEE International Conference on Granular Computing, Fremont, CA, USA, pp 588–588Google Scholar
  24. 24.
    Hielscher K-SJ, German R (2003) A low-cost infrastructure for high precision high volume performance measurements of web clusters. In: Proceedings of the 13th International Conference on Computer Performance Evaluation. Modelling Techniques and Tools, Urbana, IL, USAGoogle Scholar
  25. 25.
    Lu X, Zhang Z (2015) A virtual machine dynamic migration scheduling model based on MBFD algorithm. Int J Comput Theory Eng 7(4):278–282Google Scholar
  26. 26.
    Chowdhury MR, Mahmud MR, Rahman RM (2015) Implementation and performance analysis of various VM placement strategies in CloudSim. J Cloud Comput 4(1):21CrossRefGoogle Scholar
  27. 27.
    Jain AK, Maheswari S (2012) Survey of recent clustering techniques in data mining. Int Arch Appl Sci Technol 3(2):68–75Google Scholar
  28. 28.
    Baswade AM, Nalwade PS (2013) Selection of initial centroids for k-means algorithm. Int J Comput Sci Mob Comput (IJCSMC) 2(7):161–164Google Scholar
  29. 29.
    Khoshkholghi MA, Derahman MN, Abdullah A, Subramaniam S, Othman M (2017) Energy-efficient algorithms for dynamic virtual machine consolidation in cloud data centers. IEEE Access 5:10709–10722CrossRefGoogle Scholar
  30. 30.
    Ashry N, Nashaat H, Rizk R (2018) AMS: adaptive migration scheme in cloud computing. In: Proceedings of the 3rd International Conference on Intelligent Systems and Informatics (AISI2018), Cairo, Egypt, vol 845. Springer, pp 357–369Google Scholar
  31. 31.
    Melhem SB, Agarwal A, Goel N, Zaman M (2017) Markov prediction model for host load detection and VM placement in live migration. IEEE Access 6:7190–7205CrossRefGoogle Scholar
  32. 32.
    Chang Y, Gu Ch, Luo F, Fan G, Fu W (2018) Energy efficient resource selection and allocation strategy for virtual machine consolidation in cloud datacenters. IEICE Trans Inf Syst E101.D(7):1816–1827CrossRefGoogle Scholar
  33. 33.
    Beloglazov Planetlab workload traces. Accessed Nov 2018
  34. 34.
    Arianyan E, Taheri H, Sharifian S, Tarighi M (2018) New six-phase on-line resource management process for energy and SLA efficient consolidation in cloud data centers. Int Arab J Inf Technol 15(1):10–20Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentPort Said UniversityPort SaidEgypt

Personalised recommendations