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Novel probabilistic resource migration algorithm for cross-cloud live migration of virtual machines in public cloud

  • Souvik Pal
  • Raghvendra Kumar
  • Le Hoang Son
  • Krishnan Saravanan
  • Mohamed Abdel-Basset
  • Gunasekaran Manogaran
  • Pham Huy ThongEmail author
Article

Abstract

In cloud computing environment, cross-cloud live migration of virtual machines (VMs) is a major concern in these days. Cloud computing provides the users with huge, versatile and on-demand access to a bulk of customizable and configurable registered physical devices or things. It helps organizations or enterprises to share data efficiently by privately owned cloud or by the third-party servers. This type of sharing of bulky data through cloud is more efficient and reliable. In an enterprise environment, one of the essential capabilities of cloud infrastructure is VM migration. VM live migration basically involves the transference of instances that includes the operating system, runtime memory pages and active CPU states from source hub to the destination hub. In this paper, we have discussed on resource allocation algorithm which performs better in utilization of CPU, time and memory. Our proposed algorithm deals with the effective utilization of unoccupied memory, and we have also measured VM memory stack flow of total memory for cloud computing architecture.

Keywords

Virtual machines Virtualization Virtual machines instance Cross-cloud 

Notes

References

  1. 1.
    Abderrahim W, Choukair Z (2017) The three-dimensional model for dependability integration in cloud computing. Ann Telecommun 72(5–6):371–384Google Scholar
  2. 2.
    Adhikary T, Das AK, Razzaque MA, Almogren A, Alrubaian M, Hassan MM (2016) Quality of service aware reliable task scheduling in vehicular cloud computing. Mob Netw Appl 21(3):482–493Google Scholar
  3. 3.
    Agarwal A, Raina S (2012) Live migration of virtual machines in cloud. Int J Sci Res Publ 2(6):45–52Google Scholar
  4. 4.
    Alkhanak EN, Lee SP, Khan SUR (2015) Cost-aware challenges for workflow scheduling approaches in cloud computing environments: taxonomy and opportunities. Future Gener Comput Syst 50:3–21Google Scholar
  5. 5.
    Almutairi A, Sarfraz MI, Ghafoor A (2018) Risk-aware management of virtual resources in access controlled service-oriented cloud datacenters. IEEE Trans Cloud Comput 6(1):168–181Google Scholar
  6. 6.
    Babukarthik RG, Raju R, Dhavachelvan P (2012) Energy-aware scheduling using hybrid algorithm for cloud computing. In: 2012 Third International Conference on Computing Communication & Networking Technologies (ICCCNT). IEEE, pp 1–6Google Scholar
  7. 7.
    Breitgand D, Kutiel G, Raz D (2011) Cost-aware live migration of services in the cloud. In: Hot-ICE’11 Proceedings of the 11th USENIX Conference on Hot Topics in Management of Internet, cloud, and Enterprise Networks and Services, p 3Google Scholar
  8. 8.
    Callau-Zori M, Samoila L, Orgerie AC, Pierre G (2017) An experiment-driven energy consumption model for virtual machine management systems. Sustain Comput Inform Syst 18:163–174Google Scholar
  9. 9.
    Chen X, Zhang J, Li J, Li X (2013) Resource virtualization methodology for on-demand allocation in cloud computing systems. SOCA 7(2):77–100Google Scholar
  10. 10.
    Dave A, Patel B, Bhatt G (2016) Load balancing in cloud computing using optimization techniques: a study. In: International Conference on Communication and Electronics Systems (ICCES). IEEE, pp 1–6Google Scholar
  11. 11.
    Doss S, Nayyar A, Suseendran G, Tanwar S, Khanna A, Son LH, Thong PH (2018) APD-JFAD: accurate prevention and detection of jelly fish attack in MANET. IEEE Access 6:56954–56965Google Scholar
  12. 12.
    Fukai T, Shinagawa T, Kato K (2018) Live migration in bare-metal clouds. IEEE Trans Cloud Comput.  https://doi.org/10.1109/TCC.2018.2848981 Google Scholar
  13. 13.
    Gai K, Qiu M, Zhao H (2016) Cost-aware multimedia data allocation for heterogeneous memory using genetic algorithm in cloud computing. IEEE Trans Cloud Comput.  https://doi.org/10.1109/TCC.2016.2594172 Google Scholar
  14. 14.
    Giap CN, Son LH, Chiclana F (2018) Dynamic structural neural network. J Intell Fuzzy Syst 34:2479–2490Google Scholar
  15. 15.
    Hai DT, Son H, Vinh LT (2017) Novel fuzzy clustering scheme for 3D wireless sensor networks. Appl Soft Comput 54:141–149Google Scholar
  16. 16.
    Hemanth DJ, Anitha J, Son LH (2018) Brain signal based human emotion analysis by circular back propagation and Deep Kohonen neural networks. Comput Electr Eng 68:170–180Google Scholar
  17. 17.
    Hemanth DJ, Anitha J, Son LH, Mittal M (2018) Diabetic retinopathy diagnosis from retinal images using modified Hopfield neural network. J Med Syst 42(12):247Google Scholar
  18. 18.
    Hemanth J, Anitha J, Naaji A, Geman O, Popescu D, Son LH (2018) A Modified deep convolutional neural network for abnormal brain image classification. IEEE Access 7(1):4275–4283Google Scholar
  19. 19.
    Hirofuchi T, Lebre A, Pouilloux L (2018) SimGrid VM: virtual machine support for a simulation framework of distributed systems. IEEE Trans Cloud Comput 6(1):221–234Google Scholar
  20. 20.
    Jung G, Gnanasambandam N, Mukherjee T (2012) Synchronous parallel processing of big-data analytics services to optimize performance in federated clouds. In: 2012 IEEE 5th International Conference Cloud Computing (CLOUD). IEEE, pp 811–818Google Scholar
  21. 21.
    Kapil D, Pilli E, Joshi R (2013) Live virtual machine migration techniques: survey and research challenges. In: 2013 3rd IEEE International Advance Computing Conference (IACC), p p 78–83Google Scholar
  22. 22.
    Kapoor R, Gupta R, Kumar R, Son LH, Jha S (2019) New scheme for underwater acoustically wireless transmission using direct sequence code division multiple access in MIMO systems. Wirel Netw.  https://doi.org/10.1007/s11276-018-1750-z Google Scholar
  23. 23.
    Kapoor R, Gupta R, Son LH, Jha S, Kumar R (2018) Boosting performance of power quality event identification with KL divergence measure and standard deviation. Measurement 126:134–142Google Scholar
  24. 24.
    Kapoor R, Gupta R, Son LH, Jha S, Kumar R (2018) Detection of power quality event using histogram of oriented gradients and support vector machine. Measurement 120:52–75Google Scholar
  25. 25.
    Lin W, Liang C, Wang JZ, Buyya R (2014) Bandwidth-aware divisible task scheduling for cloud computing. Softw Pract Exp 44(2):163–174Google Scholar
  26. 26.
    Liu H, Abraham A, Snášel V, McLoone S (2012) Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments. Inf Sci 192:228–243Google Scholar
  27. 27.
    Long HV, Ali M, Khan M, Tu DN (2019) A novel approach for fuzzy clustering based on neutrosophic association matrix. Comput Ind Eng.  https://doi.org/10.1016/j.cie.2018.11.007 Google Scholar
  28. 28.
    Malekloo MH, Kara N, El Barachi M (2018) An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments. Sustain Comput Inform Syst 17:9–24Google Scholar
  29. 29.
    Malik V, Barde C (2015) Live migration of virtual machines in cloud environment using prediction of CPU usage. Int J Comput Appl 117(23):124–131Google Scholar
  30. 30.
    Mishra SK, Puthal D, Sahoo B, Jayaraman PP, Jun S, Zomaya AY, Ranjan R (2018) Energy-efficient VM-placement in cloud data center. Sustain Comput Inform Syst 20:48–55Google Scholar
  31. 31.
    Osanaiye O, Chen S, Yan Z, Lu R, Choo K, Dlodlo M (2017) From cloud to fog computing: a review and a conceptual live VM migration framework. IEEE Access 5:8284–8300Google Scholar
  32. 32.
    Panda SK, Jana PK (2015) Efficient task scheduling algorithms for heterogeneous multi-cloud environment. J Supercomput 71(4):1505–1533Google Scholar
  33. 33.
    Phan LT, Zhang Z, Zheng Q, Loo BT, Lee I (2011) An empirical analysis of scheduling techniques for real-time cloud-based data processing. In: 2011 IEEE International Conference on Service-Oriented Computing and Applications (SOCA). IEEE, pp 1–8Google Scholar
  34. 34.
    Phuong PTM, Thong PH, Son LH (2018) Theoretical analysis of picture fuzzy clustering: convergence and property. J Comput Sci Cybern 34(1):17–32Google Scholar
  35. 35.
    Robinson YH, Julie EG, Saravanan K, Kumar R, Son LH (2019) FD-AOMDV: fault-tolerant disjoint ad-hoc on-demand multipath distance vector routing algorithm in mobile ad-hoc networks. J Ambient Intell Humaniz Comput.  https://doi.org/10.1007/s12652-018-1126-3 Google Scholar
  36. 36.
    Sampaio AM, Barbosa JG (2013) Optimizing energy-efficiency in high-available scientific cloud environments. In: 2013 Third International Conference on Cloud and Green Computing (CGC). IEEE, pp 76–83Google Scholar
  37. 37.
    Saravanan K, Anusuya E, Kumar R, Son LH (2018) Real-time water quality monitoring using Internet of Things in SCADA. Environ Monit Assess 190(9):556Google Scholar
  38. 38.
    Saravanan K, Aswini S, Kumar R, Son LH (2019) How to prevent maritime border collision for fisheries? A design of real-time automatic identification system. Earth Sci Inf.  https://doi.org/10.1007/s12145-018-0371-5 Google Scholar
  39. 39.
    Satpathy A, Addya SK, Turuk AK, Majhi B, Sahoo G (2017) Crow search based virtual machine placement strategy in cloud data centers with live migration. Comput Electr Eng.  https://doi.org/10.1016/j.compeleceng.2017.12.032 Google Scholar
  40. 40.
    Seo D, Jeon YB, Lee SH, Lee KH (2016) Cloud computing for ubiquitous computing on M2 M and IoT environment mobile application. Clust Comput 19(2):1001–1013Google Scholar
  41. 41.
    Sharma P, Lee S, Guo T, Irwin D, Shenoy P (2018) Managing risk in a derivative IaaS cloud. IEEE Trans Parallel Distrib Syst 29(8):1750–1765Google Scholar
  42. 42.
    Singh K, Singh K, Son LH, Aziz A (2018) Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Comput Netw 138:90–107Google Scholar
  43. 43.
    Singh N, Son LH, Chiclana F, Jean-Pierre M (2019) A new fusion of salp swarm with sine cosine for optimization of non-linear functions. Engineering with Computers.  https://doi.org/10.1007/s00366-018-00696-8 Google Scholar
  44. 44.
    Singh RM, Paul S, Kumar A (2014) Task scheduling in cloud computing. Int J Comput Sci Inf Technol: IJCSIT 5(6):7940–7944Google Scholar
  45. 45.
    Sofia AS, Ganesh Kumar P (2018) Multi-objective task scheduling to minimize energy consumption and makespan of cloud computing using NSGA-II. J Netw Syst Manage 26(2):463–485Google Scholar
  46. 46.
    Son LH (2015) A novel kernel fuzzy clustering algorithm for geo-demographic analysis. Inf Sci Inform Comput Sci Intell Syst Appl Int J 317(C):202–223Google Scholar
  47. 47.
    Son LH (2016) Generalized picture distance measure and applications to picture fuzzy clustering. Appl Soft Comput 46(C):284–295Google Scholar
  48. 48.
    Son LH, Hai PV (2016) A novel multiple fuzzy clustering method based on internal clustering validation measures with gradient descent. Int J Fuzzy Syst 18(5):894–903MathSciNetGoogle Scholar
  49. 49.
    Son LH, Jha S, Kumar R, Chatterjee JM, Khari M (2019) Collaborative handshaking approaches between internet of computing and internet of things towards a smart world: a review from 2009–2017. Telecommun Syst.  https://doi.org/10.1007/s11235-018-0481-x Google Scholar
  50. 50.
    Son LH, Tien ND (2017) Tune up fuzzy C-means for big data: some novel hybrid clustering algorithms based on initial selection and incremental clustering. Int J Fuzzy Syst 19(5):1585–1602MathSciNetGoogle Scholar
  51. 51.
    Son LH, Tuan TM (2016) A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation. Expert Syst Appl 46:380–393Google Scholar
  52. 52.
    Son LH, Fujita H (2019) Neural-fuzzy with representative sets for prediction of student performance. Appl Intell 49(1):172–187Google Scholar
  53. 53.
    Son LH, Thong PH (2017) Some novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting from satellite image sequences. Appl Intell 46(1):1–15Google Scholar
  54. 54.
    Son LH, Tuan TM (2017) Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints. Eng Appl Artif Intell 59:186–195Google Scholar
  55. 55.
    Stavrinides GL, Karatza HD (2015) A cost-effective and qos-aware approach to scheduling real-time workflow applications in paas and saas clouds. In: 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, pp 231–239Google Scholar
  56. 56.
    Tam NT, Hai DT, Son LH, Vinh LT (2018) Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wireless Netw 24(5):1477–1490Google Scholar
  57. 57.
    Thong PH, Son LH (2016) Picture fuzzy clustering: a new computational intelligence method. Soft Comput 20(9):3549–3562zbMATHGoogle Scholar
  58. 58.
    Thong PH, Son LH (2016) A novel automatic picture fuzzy clustering method based on particle swarm optimization and picture composite cardinality. Knowl-Based Syst 109:48–60Google Scholar
  59. 59.
    Thong PH, Son LH (2016) Picture fuzzy clustering for complex data. Eng Appl Artif Intell 56:121–130Google Scholar
  60. 60.
    Tsai JT, Fang JC, Chou JH (2013) Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput Oper Res 40(12):3045–3055zbMATHGoogle Scholar
  61. 61.
    Tsakalozos K, Verroios V, Roussopoulos M, Delis A (2017) Live VM migration under time-constrains in share-nothing IaaS-clouds. IEEE Trans Parallel Distrib Syst 28(8):2285–2298Google Scholar
  62. 62.
    Tuan TM, Ngan TT, Son LH (2016) A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental X-ray image segmentation. Appl Intell 45(2):402–428Google Scholar
  63. 63.
    Wang L, Gelenbe E (2018) Adaptive dispatching of tasks in the cloud. IEEE Trans Cloud Comput 6(1):33–45Google Scholar
  64. 64.
    Xavier VA, Annadurai S (2018) Chaotic social spider algorithm for load balance aware task scheduling in cloud computing. Clust Comput.  https://doi.org/10.1007/s10586-018-1823-x Google Scholar
  65. 65.
    Xiong et al (2017) Layered virtual machine migration algorithm for network resource balancing in cloud computing. Front Comput Sci 8(2):187–198Google Scholar
  66. 66.
    Ye K, Jiang X, Huang D, Chen J, Wang B (2011) Live migration of multiple virtual machines with resource reservation in cloud computing environments. In: 2011 IEEE 4th International Conference on Cloud Computing, pp 48–53Google Scholar
  67. 67.
    Zhang F, Liu G, Fu X, Yahyapour R (2018) A survey on virtual machine migration: challenges, techniques, and open issues. IEEE Commun Surv Tutor 20(2):1206–1243Google Scholar
  68. 68.
    Zuo L, Shu L, Dong S, Chen Y, Yan L (2017) A multi-objective hybrid cloud resource scheduling method based on deadline and cost constraints. IEEE Access 5:22067–22080Google Scholar
  69. 69.
    Zuo L, Shu L, Dong S, Zhu C, Zhou Z (2017) Dynamically weighted load evaluation method based on self-adaptive threshold in cloud computing. Mob Netw Appl 22(1):4–18Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJIS CollegeKalyaniIndia
  2. 2.Department of Computer Science and EngineeringLNCT CollegeJabalpurIndia
  3. 3.Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam
  4. 4.VNU Information Technology InstituteVietnam National UniversityHanoiVietnam
  5. 5.Department of Computer Science and EngineeringAnna University Regional CampusTirunelveliIndia
  6. 6.Department of Operations Research, Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt
  7. 7.University of CaliforniaDavisUSA
  8. 8.Division of Data ScienceTon Duc Thang UniversityHo Chi Minh CityVietnam
  9. 9.Faculty of Information TechnologyTon Duc Thang UniversityHo Chi Minh CityVietnam

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