AIMS Mathematics, 2020, 5(4): 3966-3989. doi: 10.3934/math.2020256

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An optimal resource allocation scheme for virtual machine placement of deploying enterprise applications into the cloud

School of Economics and Management, Yanshan University, Qinhuangdao, 066004, China

The emergence of cloud computing can help enterprises reduce their hardware and software investment and save their own operation and maintenance costs, thus more and more enterprises deploy their applications into the cloud. Generally, components of enterprise applications are resided in virtual machines and then hosted by physical machines. In order to achieve the efficiency and utilization of physical machines, reasonable virtual machines placement becomes very important. In this paper we propose a scheme of resource allocation model for virtual machines placement and investigate it with convex optimization approach. We also present a heuristic algorithm to achieve the optimal resource allocation and discuss its equilibrium and stability by applying the asymptotic stability of the continuous dynamic system of Lyapunov stability theory. Finally, we give some numerical examples to illustrate the performance of the resource allocation scheme and confirm its convergence with a certain number of iterations.
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1. N. Alsaeed, M. Saleh, Towards cloud computing services for higher educational institutions: Concepts & literature review, IEEE International Conference on Cloud Computing (ICCC), 2015, 1-7, Riyadh, Saudi Arabia.

2. P. M. Mell, T. Grance, The NIST definition of cloud computing, National Institute of Standards & Technology, 2011.

3. M. Reza, Framework on large public sector implementation of cloud computing, IEEE International Conference on Cloud Computing and Social Networking (ICCCSN), 2012, 1-4, Bandung, Indonesia.

4. S. Li, Y. Zhang, W. Sun, Optimal resource allocation model and algorithm for elastic enterprise applications migration to the cloud, Mathematics, 7 (2019), 1-20.

5. S. Li, W. Sun, Utility maximisation for resource allocation of migrating enterprise applications into the cloud, Enterp. Inf. Syst., 2020.

6. P. D. Bharathi, P. Prakash, M. V. K. Kiran, Energy efficient strategy for task allocation and VM placement in cloud environment, IEEE Innovations in Power and Advanced Computing Technologies (i-PACT), 2017, 1-6, Vellore, India.

7. B. Zhang, Z. Qian, W. Huang, et al. Minimizing communication traffic in data centers with poweraware VM placement, IEEE Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 2012, Palermo, Italy.

8. F. B. Hassen, Z. Brahmi, H. Toumi, VM placement algorithm based on recruitment process within ant colonies, IEEE International Conference on Digital Economy (ICDEc), 2016, Carthage, Tunisia.

9. M. Sindelar, P. K. Sitaraman, P. Shenoy, Sharing-aware algorithms for virtual machine colocation, ACM Symposium on Parallelism in Algorithms and Architectures, June 04-06, 2011, 367-378, San Jose, California, USA.

10. A. Beloglazov, J. Abawajy, R. Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing, Futur. Gener. Comp. Syst., 28 (2012), 755-768.    

11. S. B. Shaw, A. K. Singh, Use of proactive and reactive hotspot detection technique to reduce the number of virtual machine migration and energy consumption in cloud data center, Comput. Electr. Eng., 47 (2015), 241-254.    

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

13. Z. Xiao, W. Song, Q. Chen, Dynamic resource allocation using virtual machines for cloud computing environment, IEEE Trans. Parallel Distrib. Syst., 24 (2013), 1107-1117.    

14. A. Khosravi, L. L. H. Andrew, R. Buyya, Dynamic VM placement method for minimizing energy and carbon cost in geographically distributed cloud data centers, IEEE Trans. Sustainable Comput., 2 (2017), 183-196.    

15. S. K. Mishra, D. Puthal, B. Sahoo, et al. An adaptive task allocation technique for green cloud computing, J. Supercomput., 74 (2018), 370-385.    

16. E. Mohammadi, M. Karimi, S. R. Heikalabad. A novel virtual machine placement in cloud computing, Aust. J. Basic Appl. Sci., 5 (2011), 1549-1555.

17. S. Rahman, A. Gupta, M. Tornatore, et al. Dynamic workload migration over backbone network to minimize data center electricity cost, IEEE Trans. Green Commun. Netw., 2 (2018), 570-579.    

18. X. Meng, V. Pappas, L. Zhang, Improving the scalability of data center networks with traffic-aware virtual machine placement, Proceedings IEEE INFOCOM, 2010, 1-9, San Diego, CA, USA.

19. W. Wang, B. Liang, B. Li, Multi-resource fair allocation in heterogeneous cloud computing systems, IEEE Trans. Parallel Distrib. Syst., 26 (2015), 2822-2835.    

20. G. Wei, A. V. Vasilakos, Y. Zheng, et al. A game-theoretic method of fair resource allocation for cloud computing services, J. Supercomput., 54 (2010), 252-269.    

21. K. Wang, W. Quan, N. Cheng, et al. Betweenness centrality based software defined routing: Observation from practical Internet datasets, ACM Trans. Internet. Technol., 19 (2019), 1-19.

22. F. Song, Z. Ai, Y. Zhou, et al. Smart collaborative automation for receive buffer control in multipath industrial networks, IEEE Trans. Ind. Inform., 16 (2020), 1385-1394.    

23. Z. Ai, Y. Zhou, F. Song, A smart collaborative routing protocol for reliable data diffusion in IoT scenarios, Sensors, 18 (2018), 1-21.    

24. F. Song, M. Zhu, Y. Zhou, et al. Smart collaborative tracking for ubiquitous power IoT in edgecloud interplay domain, IEEE Int. Things J., 2020.

25. K. Wang, H. Yin, W. Quan, et al. Enabling collaborative edge computing for software defined vehicular networks, IEEE Netw., 32 (2018), 112-117.

26. F. Lin, X. Lv, I. You, et al. A novel utility based resource management scheme in vehicular social edge computing, IEEE Access, 6 (2018), 66673-66684.    

27. G. H. S. Carvalho, I. Woungang, A. Anpalagan, et al. Intercloud and hetNet for mobile cloud computing in 5G systems: Design issues, challenges, and optimization, IEEE Netw., 31 (2017), 80-89.

28. Z. Ai, Y. Liu, F. Song, et al. A smart collaborative charging algorithm for mobile power distribution in 5G networks, IEEE Access, 6 (2018), 28668-28679.    

29. F. Song, Y. Zhou, L. Chang, et al. Modeling space-terrestrial integrated networks with smart collaborative theory, IEEE Netw., 33 (2019), 51-57.    

30. F. Song, Y. Zhou, Y. Wang, et al. Smart collaborative distribution for privacy enhancement in moving target defense, Inf. Sci., 479 (2019), 593-606.    

31. C. Helene, G. L. Louet, J. M. Menaud, Virtual machine placement for hybrid cloud using constraint programming, IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS), 2017, 326-333.

32. M. S. P. Mohamed, S. R. Swarnammal, An efficient framework to handle integrated VM workloads in heterogeneous cloud infrastructure, Soft Comput., 21 (2017), 3367-3376.    

33. S. Chaisiri, B. S. Lee, D. Niyato, Optimization of resource provisioning cost in cloud computing, IEEE Trans. Serv. Comput., 5 (2012), 164-177.    

34. X. Zheng, Y. Xia, Exploring mixed integer programming reformulations for virtual machine placement with disk anti-colocation constraints, Perform. Eval., 135 (2019), 1-18.

35. B. Xu, Z. Peng, F. Xiao, et al. Dynamic deployment of virtual machines in cloud computing using multi-objective optimization, Soft Comput., 19 (2015), 2265-2273.    

36. D. Zhao, J. Zhou, K. Li, An energy-aware algorithm for virtual machine placement in cloud computing, IEEE Access, 7 (2019), 55659-55668.    

37. M. A. Kaaouache, S. Bouamama, An energy-efficient VM placement method for cloud data centers using a hybrid genetic algorithm, J. Syst. Inf. Technol., 20 (2018), 430-445.    

38. F. Stefanello, V. Aggarwal, L. S. Buriol, et al. Hybrid algorithms for placement of virtual machines across geo-separated data centers, J. Comb. Optim., 38 (2019), 748-793.    

39. X. Liu, Z. Zhan, J. D. Deng, et al. An energy efficient ant colony system for virtual machine placement in cloud computing, IEEE Trans. Evol. Comput., 22 (2018), 113-128.    

40. F. Alharbi, Y. Tian, M. Tang, et al. An ant colony system for energy-efficient dynamic virtual machine placement in data centers, Expert Syst. Appl., 120 (2019), 228-238.    

41. X. Wang, Z. Liu, An energy-aware VMs placement algorithm in cloud computing environment, IEEE Second International Conference on Intelligent System Design and Engineering Application, 2012, 627-630, Sanya, Hainan, China.

42. M. A. Kaaouache, S. Bouamama, Solving bin packing problem with a hybrid genetic algorithm for VM placement in cloud, Procedia Comput. Sci., 60 (2015), 1061-1069.    

43. J. Xu, J. A. B. Fortes, Multi-objective virtual machine placement in virtualized data center environments, IEEE/ACM International Conference on Green Computing and Communications & International Conference on Cyber, Physical and Social Computing, 2010, 179-188, Hangzhou, China.

44. S. Dörterler, M. Dörterler, S. Ozdemir, Multi-objective virtual machine placement optimization for cloud computing, IEEE International Symposium on Networks, Computers and Communications (ISNCC), 2017, Marrakech, Morocco.

45. D. Jayasinghe, C. Pu, T. Eilam, et al. Improving performance and availability of services hosted on IaaS clouds with structural constraint-aware virtual machine placement, IEEE International Conference on Services Computing, 2011, 72-79, Washington, DC, USA.

46. W. Fang, X. Liang, S. Li, et al. VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers, Comput. Netw., 57 (2013), 179-196.    

47. L. Guo, T. Yan, S. Zhao, et al. Dynamic performance optimization for cloud computing using M/M/m queueing system, J. Appl. Math., 2014 (2014), 1-8.

48. Z. Xiao, J. Jiang, Y. Zhu, et al. A solution of dynamic VMs placement problem for energy consumption optimization based on evolutionary game theory, J. Syst. Softw., 101 (2015), 260-272.    

49. F. Song, D. Huang, H. Zhou, et al. An optimization-based scheme for efficient virtual machine placement, Int. J. Parallel Program., 42 (2014), 853-872.    

50. D. Huang, L. Yi, F. Song, et al. A secure cost-effective migration of enterprise applications to the cloud, Int. J. Commun. Syst., 27 (2014), 3996-4013.    

51. M. Chiang, S. H. Low, A. R. Calderbank, et al. Layering as optimization decomposition: a mathematical theory of network architectures, Proc. IEEE, 95 (2007), 255-312.    

52. S. Li, W. Sun, Q. L. Li, Utility maximization for bandwidth allocation in peer-to-peer file-sharing networks, J. Ind. Manag. Optim., 16 (2020), 1099-1117.

53. W. E. Boyce, R. C. DiPrima, Elementary Differential Equations and Boundary Value Problems, Hoboken: John Wiley & Sons, 2005.

54. Q. V. Pham, W. J. Hwang, Network utility maximization based congestion control over wireless networks: A survey and potential directives, IEEE Commun. Surv. Tut., 19 (2017), 1173-1200.    

55. J. Kennedy, R. C. Eberhart, Particle swarm optimization, Proceeding of the 1995 IEEE International Conference on Neural Networks (ICNN), 1995, 1942-1948.

56. S. Li, W. Sun, J. Liu, A mechanism of bandwidth allocation for peer-to-peer file-sharing networks via particle swarm optimization, J. Intell. Fuzzy Syst., 35 (2018), 2269-2280.    

57. M. Clerc, J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evol. Comput., 6 (2002), 58-73.    

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