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Bandwidth allocation for communicating virtual machines in cloud data centers

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Abstract

High-performance computing in a cloud environment may require massive data transfer among some of the virtual machines (VMs). These VMs are deployed in physical machines (hosts) of a data center. The data transfer among the communicating VMs may use the same shared communication links of the data center. Hence, it is important to have efficient bandwidth allocation policies for different data transfer requests (DTRs) which result in better utilization of bandwidth and fair allocation among the DTRs. In this paper, a few bandwidth allocation policies are proposed and their performances are analyzed. While designing these policies, the objective is the maximization of throughput and bandwidth utilization while minimizing the service time and turnaround time. Some of the policies are based on integer linear programming (ILP) which runs in exponential time while others are based on polynomial-time heuristics. Experimental results show that the performances of heuristic-based policies are comparable to those given by ILP-based exponential time policies.

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References

  1. Nurmi D, Wolski R, Grzegorczyk C, Obertelli G, Soman S, Youseff L, Zagorodnov D (2009) The eucalyptus open-source cloud-computing system. In: 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, 2009. CCGRID’09. IEEE, pp 124–131

  2. Amazon EC (2010) Amazon elastic compute cloud (amazon ec2). Amazon Elastic Compute Cloud (Amazon EC2)

  3. Chai X-Z, Cao J (2012) Cloud computing oriented workflow technology. J Chin Comput Syst 33(1):90–95

    Google Scholar 

  4. Luo H, Liu X, Liu J, Wang F (2016) Where to fix temporal violations: a novel handling point selection strategy for business cloud workflows. In: 2016 IEEE International Conference on Services Computing (SCC). IEEE, pp 155–162

  5. Rimba P, Tran AB, Weber I, Staples M, Ponomarev A, Xu X (2017) Comparing blockchain and cloud services for business process execution. In: 2017 IEEE International Conference on Software Architecture (ICSA). IEEE, pp 257–260

  6. Sadooghi I, Martin JH, Li T, Brandstatter K, Maheshwari K, de Lacerda Ruivo TPP, Garzoglio G, Timm S, Zhao Y, Raicu I (2015) Understanding the performance and potential of cloud computing for scientific applications. IEEE Trans Cloud Comput 5(2):358–371

    Google Scholar 

  7. Galante G, De Bona LCE, Mury AR, Schulze B, da Rosa Righi R (2016) An analysis of public clouds elasticity in the execution of scientific applications: a survey. J Grid Comput 14(2):193–216

    Google Scholar 

  8. Saha P (2018) Exploring resource fairness and container orchestration in a cloud environment for scientific computing workloads. PhD thesis, State University of New York at Binghamton

  9. Wang Y, Li J, Wang HH (2019) Cluster and cloud computing framework for scientific metrology in flow control. Cluster Comput 22(1):1189–1198

    Google Scholar 

  10. Feller E, Ramakrishnan L, Morin C (2015) Performance and energy efficiency of big data applications in cloud environments: a hadoop case study. J Parallel Distrib Comput 79:80–89

    Google Scholar 

  11. Petri I, Diaz-Montes J, Rana O, Punceva M, Rodero I, Parashar M (2015) Modelling and implementing social community clouds. IEEE Trans Serv Comput 10(3):410–422

    Google Scholar 

  12. Elhoseny M, Abdelaziz A, Salama AS, Riad AM, Muhammad K (2018) A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Gener Comput Syst 86:1383–1394

    Google Scholar 

  13. Fazio M, Celesti A, Puliafito A, Villari M (2015) Big data storage in the cloud for smart environment monitoring. Procedia Comput Sci 52:500–506

    Google Scholar 

  14. Stergiou C, Psannis KE (2017) Recent advances delivered by mobile cloud computing and internet of things for big data applications: a survey. Int J Netw Manag 27(3):e1930

    Google Scholar 

  15. Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of “big data” on cloud computing: review and open research issues. Inf Syst 47:98–115

    Google Scholar 

  16. Yang C, Manzhu Y, Fei H, Jiang Y, Li Y (2017) Utilizing cloud computing to address big geospatial data challenges. Comput Environ Urban Syst 61:120–128

    Google Scholar 

  17. Kakderi C, Komninos N, Tsarchopoulos P (2019) Smart cities and cloud computing: introduction to the special issue. J Smart Cities 1(2):1–3

    Google Scholar 

  18. Pires FL, Barán B (2015) A virtual machine placement taxonomy. In: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. IEEE, pp 159–168

  19. Masdari M, Nabavi SS, Ahmadi V (2016) An overview of virtual machine placement schemes in cloud computing. J Netw Comput Appl 66:106–127

    Google Scholar 

  20. Lopez-Pires F, Baran B (2015) Virtual machine placement literature review. arXiv preprint arXiv:1506.01509

  21. Podvratnik A, Spatzier T, Teich T (2016) Optimizing virtual machines placement in cloud computing environments, November 15 2016. US Patent 9,495,215

  22. Satpathy A, Addya SK, Turuk AK, Majhi B, Sahoo G (2018) Crow search based virtual machine placement strategy in cloud data centers with live migration. Comput Electr Eng 69:334–350

    Google Scholar 

  23. Ghobaei-Arani M, Rahmanian AA, Shamsi M, Rasouli-Kenari A (2018) A learning-based approach for virtual machine placement in cloud data centers. Int J Commun Syst 31(8):e3537

    Google Scholar 

  24. Ilkhechi AR, Korpeoglu I, Ulusoy Ö (2015) Network-aware virtual machine placement in cloud data centers with multiple traffic-intensive components. Comput Netw 91:508–527

    Google Scholar 

  25. Ferdaus MH, Murshed M, Calheiros RN, Buyya R (2015) Network-aware virtual machine placement and migration in cloud data centers. In: Emerging research in cloud distributed computing systems. IGI Global, pp 42–91

  26. Zhang W, Han S, He H, Chen H (2017) Network-aware virtual machine migration in an overcommitted cloud. Future Gener Comput Syst 76:428–442

    Google Scholar 

  27. Larumbe F, Sansò B (2017) Elastic, on-line and network aware virtual machine placement within a data center. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). IEEE, pp 28–36

  28. Hamdi K, Kefi M (2016) Network-aware virtual machine placement in cloud data centers: an overview. In: 2016 International Conference on Industrial Informatics and Computer Systems (CIICS). IEEE, pp 1–6

  29. Tao C, Xiaofeng G, Chen G (2016) Optimized virtual machine placement with traffic-aware balancing in data center networks. Scientific Program. https://doi.org/10.1155/2016/3101658

    Article  Google Scholar 

  30. Cui Y, Yang Z, Xiao S, Wang X, Yan S (2017) Traffic-aware virtual machine migration in topology-adaptive dcn. IEEE/ACM Trans Netw (TON) 25(6):3427–3440

    Google Scholar 

  31. Luo J, Song W, Yin L (2018a) Reliable virtual machine placement based on multi-objective optimization with traffic-aware algorithm in industrial cloud. IEEE Access 6:23043–23052

    Google Scholar 

  32. Luo G, Qian Z, Dong M, Ota K, Sanglu L (2018b) Improving performance by network-aware virtual machine clustering and consolidation. J Supercomput 74(11):5846–5864

    Google Scholar 

  33. Goyal KK, Jain V, Verma P (2018) An analysis on virtual machine migration issues and challenges in cloud computing. Int J Comput Appl 975:8887

    Google Scholar 

  34. Landi G, Capitani M, Kretsis A, Kokkinos P, Christodoulopoulos K, Varvarigos E (2018) Joint intra-and inter-datacenter network optimization and orchestration. In: Optical Fiber Communication Conference. Optical Society of America, pp Th2A–34

  35. Yamanaka N, Okamoto S, Hirono M, Imakiire Y, Muro W, Sato T, Oki E, Fumagalli A, Veeraraghavan M (2018) Application-triggered automatic distributed cloud/network resource coordination by optically networked inter/intra data center. J Opt Commun Netw 10(7):B15–B24

    Google Scholar 

  36. Vogel A, Griebler D, Schepke C, Fernandes LG (2017) An intra-cloud networking performance evaluation on cloudstack environment. In: 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). IEEE, pp 468–472

  37. Cisco Global Cloud Index (2016) Forecast and methodology 2016–2021. White Paper

  38. Yu C, Lumezanu C, Zhang Y, Singh V, Jiang G, Madhyastha HV (2013) Flowsense: monitoring network utilization with zero measurement cost. In: International Conference on Passive and Active Network Measurement. Springer, pp 31–41

  39. Van Adrichem NLM, Doerr C, Kuipers FA (2014) Opennetmon: network monitoring in openflow software-defined networks. In: 2014 IEEE Network Operations and Management Symposium (NOMS). IEEE, pp 1–8

  40. Chowdhury SR, Bari MF, Ahmed R, Boutaba R (2014) Payless: a low cost network monitoring framework for software defined networks. In: 2014 IEEE Network Operations and Management Symposium (NOMS). IEEE, pp 1–9

  41. Peng Y, Chen K, Wang G, Bai W, Ma Z, Gu L (2014) Hadoopwatch: a first step towards comprehensive traffic forecasting in cloud computing. In: IEEE INFOCOM 2014-IEEE Conference on Computer Communications. IEEE, pp 19–27

  42. LaCurts K, Mogul JC, Balakrishnan H, Turner Y (2014) Cicada: introducing predictive guarantees for cloud networks. In: 6th \(\{\)USENIX\(\}\) Workshop on Hot Topics in Cloud Computing (HotCloud 14)

  43. Zhang H, Chen L, Yi B, Chen K, Chowdhury M, Geng Y (2016) Coda: toward automatically identifying and scheduling coflows in the dark. In: Proceedings of the 2016 ACM SIGCOMM Conference. ACM, pp 160–173

  44. Popa L, Yalagandula P, Banerjee S, Mogul JC, Turner Y, Santos JR (2013) Elasticswitch: practical work-conserving bandwidth guarantees for cloud computing. In: ACM SIGCOMM Computer Communication Review, vol 43. ACM, pp 351–362

  45. Ballani H, Costa P, Karagiannis T, Rowstron A (2011) Towards predictable datacenter networks. In: ACM SIGCOMM Computer Communication Review, vol 41. ACM, pp 242–253

  46. Guo C, Lu G, Wang HJ, Yang S, Kong C, Sun P, Wu W, Zhang Y (2010) Secondnet: a data center network virtualization architecture with bandwidth guarantees. In: Proceedings of the 6th International Conference. ACM, p 15

  47. Terry L, Sivasankar R, Amin V, George V (2010) NetShare: virtualizing data center networks across services. Department of Computer Science and Engineering, University of California, San Diego

  48. Henrique R, Renato SJ, Yoshio T, Paolo S, Guedes Dorgival O (2011) Gatekeeper: supporting bandwidth guarantees for multi-tenant datacenter networks. WIOV 1(3):784–789

    Google Scholar 

  49. Alan S, Srikanth K, Greenberg Albert G, Changhoon K, Bikas S (2011) Sharing the data center network. NSDI 11:23–23

    Google Scholar 

  50. Popa L, Kumar G, Chowdhury M, Krishnamurthy A, Ratnasamy S, Stoica I (2012) Faircloud: sharing the network in cloud computing. ACM SIGCOMM Comput Commun Rev 42(4):187–198

    Google Scholar 

  51. Kumar A, Jain S, Naik U, Raghuraman A, Kasinadhuni N, Zermeno EC, Gunn CS, Ai J, Carlin B, Amarandei-Stavila M et al (2015) Bwe: flexible, hierarchical bandwidth allocation for wan distributed computing. ACM SIGCOMM Comput Commun Rev 45(4):1–14

    Google Scholar 

  52. Nagaraj K, Bharadia D, Mao H, Chinchali S, Alizadeh M, Katti S (2016) Numfabric: fast and flexible bandwidth allocation in datacenters. In: Proceedings of the 2016 ACM SIGCOMM Conference. ACM, pp 188–201

  53. Shen D, Luo J, Dong F, Zhang J (2016) Appbag: application-aware bandwidth allocation for virtual machines in cloud environment. In: 2016 45th International Conference on Parallel Processing (ICPP). IEEE, pp 21–30

  54. Dian S, Luo J, Fang D, Jiahui J, Junxue Z, Jun S (2019) Facilitating application-aware bandwidth allocation in the cloud with one-step-ahead traffic information. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2019.2922176

    Article  Google Scholar 

  55. Americas Headquarters (2007) Cisco data center infrastructure 2.5 design guide. Cisco Validated Design I

  56. Guo C, Wu H, Tan K, Shi L, Zhang Y, Lu S (2008) Dcell: a scalable and fault-tolerant network structure for data centers. In: ACM SIGCOMM Computer Communication Review, volume 38. ACM, pp 75–86

  57. Guo C, Guohan L, Li D, Haitao W, Zhang X, Shi Y, Tian C, Zhang Y, Songwu L (2009) Bcube: a high performance, server-centric network architecture for modular data centers. ACM SIGCOMM Comput Commun Rev 39(4):63–74

    Google Scholar 

  58. Vahdat A, Al-Fares M, Loukissas A (2013) Scalable commodity data center network architecture, July 9 2013. US Patent 8,483,096

  59. Niranjan Mysore R, Pamboris A, Farrington N, Huang N, Miri P, Radhakrishnan S, Subramanya V, Vahdat A (2009) Portland: a scalable fault-tolerant layer 2 data center network fabric. In: ACM SIGCOMM Computer Communication Review, vol 39. ACM, pp 39–50

  60. Greenberg A, Hamilton JR, Jain N, Kandula S, Kim C, Lahiri P, Maltz DA, Patel P, Sengupta S (2011) Vl2: a scalable and flexible data center network. Commun ACM 54(3):95–104

    Google Scholar 

  61. Wang T, Liu F, Guo J, Xu H (2016) Dynamic sdn controller assignment in data center networks: stable matching with transfers. In: Proceedings of the 35th IEEE International Conference on Computer Communications (INFOCOM 2016). IEEE, pp 1–9

  62. Gnu Linear Programming Kit (2017) https://www.gnu.org/software/glpk/. Accessed 10 Sept 2017

  63. Ersoz D, Yousif MS, Das CR (2007) Characterizing network traffic in a cluster-based, multi-tier data center. In: Null. IEEE, p 59

  64. Kandula S, Sengupta S, Greenberg A, Patel P, Chaiken R (2009) The nature of data center traffic: measurements & analysis. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference. ACM, pp 202–208

  65. Meng X, Pappas V, Zhang L (2010) Improving the scalability of data center networks with traffic-aware virtual machine placement. In: INFOCOM, 2010 Proceedings IEEE. IEEE, pp 1–9

  66. Wang G, Ng TE (2010) The impact of virtualization on network performance of amazon ec2 data center. In: 2010 Proceedings IEEE Infocom. IEEE, pp 1–9

  67. Chen Y, Ganapathi AS, Griffith R, Katz RH (2010) Analysis and lessons from a publicly available google cluster trace. EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2010-95. 94

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Acknowledgements

This research is an outcome of the R&D work supported by the Visvesvaraya Ph.D. Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by the Digital India Corporation, Ref. No. MLA/MUM/GA/10(37)C.

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  1. Computer Science and Engineering, University of Calcutta, Kolkata, India

    Kamalesh Karmakar, Rajib K. Das & Sunirmal Khatua

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Karmakar, K., Das, R.K. & Khatua, S. Bandwidth allocation for communicating virtual machines in cloud data centers. J Supercomput 76, 7268–7289 (2020). https://doi.org/10.1007/s11227-019-03128-6

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