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A survey and taxonomy on virtual data center embedding

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Abstract

Data center network virtualization is being considered as a promising technology to provide a performance guarantee for cloud computing applications. One important problem in data center network virtualization technology is virtual data center (VDC) embedding, which handles the physical resource allocation to virtual nodes (virtual switches and virtual servers) and virtual links of a VDC. When node and link constraints (including CPU, memory, storage and network bandwidth) are both taken into account, the VDC embedding (VDCE) problem becomes NP-hard. The VDCE is so crucial that took wide consideration since it directly affects the execution, resource use and power consumption of data centers. To the best of our knowledge, there is no published work that precisely outlines open challenges connected with VDCE problem including all of its variants. On this point, this work tries to articulate this problem and bring research taxonomy for succinct classification of existing works. Moreover, we summarize the possible techniques already presented in the literature and we establish a classification based on a taxonomy study. At the end, we examine the limitations of existing solutions and identify the related open challenges.

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References

  1. Alboaneen DA, Tianfield H, Zhang Y (2016) Metaheuristic approaches to virtual machine placement in cloud computing: a review. In: 15th International Symposium on Parallel and Distributed Computing (ISPDC). IEEE, pp 214–221

  2. Amokrane A, Langar R, Zhani MF, Boutaba R, Pujolle G (2015) Greenslater: on satisfying green slas in distributed clouds. IEEE Trans Netw Serv Manag 12(3):363–376

    Article  Google Scholar 

  3. Amokrane A, Zhani MF, Langar R, Boutaba R, Pujolle G (2013) Greenhead: virtual data center embedding across distributed infrastructures. IEEE Trans Cloud Comput 1(1):36–49

    Article  Google Scholar 

  4. Ashraf A, Porres I (2018) Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system. Int J Parallel Emerg Distrib Syst 33(1):103–120

    Article  Google Scholar 

  5. Ballani H, Costa P, Karagiannis T, Rowstron A (2011) Towards predictable datacenter networks. In: ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications ( SIGCOMM), vol 41. ACM, pp 242–253

  6. Bari MF, Boutaba R, Esteves R, Granville LZ, Podlesny M, Rabbani MG, Zhang Q, Zhani MF (2013) Data center network virtualization: a survey. IEEE Commun Surv Tutor 15(2):909–928

    Article  Google Scholar 

  7. Beck MT, Fischer A, de Meer H, Botero JF, Hesselbach X (2013) A distributed, parallel, and generic virtual network embedding framework. In: 12th IEEE International Conference on Communications (ICC). IEEE, pp 3471–3475

  8. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gen Comput Syst 28(5):755–768

    Article  Google Scholar 

  9. Cao X, Popescu I, Chen G, Guo H, Yoshikane N, Tsuritani T, Wu J, Morita I (2017) Optimal and dynamic virtual datacenter provisioning over metro-embedded datacenters with holistic SDN orchestration. Opt Switch Netw 24:1–11

    Article  Google Scholar 

  10. Carnes T, Shmoys D (2008) Primal-dual schema for capacitated covering problems. In: International Conference on Integer Programming and Combinatorial Optimization. Springer, pp 288–302

  11. Chen G, Guo H, Zhang D, Zhu Y, Wang C, Yu H, Wang Y, Wang J, Wu J, Cao X et al (2015) First demonstration of holistically-organized metro-embedded cloud platform with all-optical interconnections for virtual datacenter provisioning. In: 2015 Opto-Electronics and Communications Conference (OECC). IEEE, pp 1–3

  12. Chowdhury NMK, Boutaba R (2010) A survey of network virtualization. Comput Netw 54(5):862–876

    Article  Google Scholar 

  13. Correa ES, Fletscher LA, Botero JF (2015) Virtual data center embedding: a survey. IEEE Latin Am Trans 13(5):1661–1670

    Article  Google Scholar 

  14. Costa MC, Létocart L, Roupin F (2005) Minimal multicut and maximal integer multiflow: a survey. Eur J Oper Res 162(1):55–69

    Article  MathSciNet  Google Scholar 

  15. Dally WJ, Towles BP (2004) Principles and practices of interconnection networks. Elsevier, Amsterdam

    Google Scholar 

  16. Dorigo M (1992) Optimization, learning and natural algorithms. PhD Thesis, Politecnico di Milano

  17. Esposito F, Di Paola D, Matta I (2016) On distributed virtual network embedding with guarantees. IEEE/ACM Trans Netw 24(1):569–582

    Article  Google Scholar 

  18. Fischer A, Botero JF, Beck MT, De Meer H, Hesselbach X (2013) Virtual network embedding: a survey. IEEE Commun Surv Tutor 15(4):1888–1906

    Article  Google Scholar 

  19. Fox A, Griffith R, Joseph A, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I (2009) Above the clouds: a Berkeley view of cloud computing. Dept. Electrical Eng. and Comput. Sciences, University of California, Berkeley, Rep. UCB/EECS 28:13

  20. Galante G, de Bona LCE (2012) A survey on cloud computing elasticity. In: 5th IEEE International Conference on Utility and Cloud Computing (UCC). IEEE, pp 263–270

  21. Ghomi EJ, Rahmani AM, Qader NN (2017) Load-balancing algorithms in cloud computing: a survey. J Netw Comput Appl 88:50–71

    Article  Google Scholar 

  22. Gilesh M (2016) Towards a complete framework for virtual data center embedding. arXiv preprint arXiv:1611.06309

  23. Gilesh M, Kumar S, Jacob L (2018) Bounding the cost of virtual machine migrations for resource allocation in cloud data centers. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing. ACM, pp 201–206

  24. Gilesh M, Satheesh S, Kumar S, Jacob L (2018) Selecting suitable virtual machine migrations for optimal provisioning of virtual data centers. ACM SIGAPP Appl Comput Rev 18(2):22–32

    Article  Google Scholar 

  25. Greenberg A, Hamilton JR, Jain N, Kandula S, Kim C, Lahiri P, Maltz DA, Patel P, Sengupta S (2009) Vl2: a scalable and flexible data center network. In: ACM SIGCOMM Computer Communication Review, vol 39. ACM, pp 51–62

  26. Guo B, Shen Y, Shao Z (2009) The redefinition and some discussion of green computing. Chin J Comput 32(12):2311–2319

    Google Scholar 

  27. Guo C, Lu G, Li D, Wu H, Zhang X, Shi Y, Tian C, Zhang Y, Lu S (2009) Bcube: a high performance, server-centric network architecture for modular data centers. ACM SIGCOMM Comput Commun Rev 39(4):63–74

    Article  Google Scholar 

  28. 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: 6th International Conference on emerging Networking EXperiments and Technologies (CoNEXT). ACM, p 15

  29. 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, vol 38. ACM, pp 75–86

  30. Han Y, Li J, Chung JY, Yoo JH, Hong JWK (2015) Save: energy-aware virtual data center embedding and traffic engineering using SDN. In: 1st IEEE Conference on Network Softwarization (NetSoft). IEEE, pp 1–9

  31. Herbst NR, Kounev S, Reussner RH (2013) Elasticity in cloud computing: what it is, and what it is not. In: 10th International Conference on Autonomic Computing (ICAC), vol 13, pp 23–27

  32. Holland JH et al (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge

    Book  Google Scholar 

  33. ILOG I (2008) Ilog cplex: high-performance software for mathematical programming and optimization. http://www.ilog.com/products/cplex

  34. Infrastructure, C.D.C.: 2.5 design guide (2010)

  35. Jennings B, Stadler R (2015) Resource management in clouds: survey and research challenges. J Netw Syst Manag 23(3):567–619

    Article  Google Scholar 

  36. Jing SY, Ali S, She K, Zhong Y (2013) State-of-the-art research study for green cloud computing. J Supercomput 65(1):445–468

    Article  Google Scholar 

  37. Kim S, Eom H, Yeom HY (2013) Virtual machine consolidation based on interference modeling. J Supercomput 66(3):1489–1506

    Article  Google Scholar 

  38. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  Google Scholar 

  39. Kocoloski B, Lange J (2015) Xemem: efficient shared memory for composed applications on multi-os/r exascale systems. In: 24th International Symposium on High-Performance Parallel and Distributed Computing. ACM, pp 89–100

  40. Kocoloski B, Lange J (2016) Lightweight memory management for high performance applications in consolidated environments. IEEE Trans Parallel Distrib Syst 27(2):468–480

    Article  Google Scholar 

  41. Kocoloski B, Zhou Y, Childers B, Lange J (2015) Implications of memory interference for composed HPC applications. In: International Symposium on Memory Systems. ACM, pp 95–97

  42. Lau W, Jha S (2004) Failure-oriented path restoration algorithm for survivable networks. IEEE Trans Netw Serv Manag 1(1):11–20

    Article  Google Scholar 

  43. Leiserson CE (1985) Fat-trees: universal networks for hardware-efficient supercomputing. IEEE Trans Comput 100(10):892–901

    Article  Google Scholar 

  44. Li Q, Zhou M (2011) The survey and future evolution of green computing. In: IEEE/ACM International Conference on Green Computing and Communications. IEEE Computer Society, pp 230–233

  45. Liu F, Liu Z et al (2012) Multi-objective optimization for initial virtual machine placement in cloud data center. J Inf Comput Sci 9(16):5029–5038

    Google Scholar 

  46. Lo HY, Liao W (2017) Calm: survivable virtual data center allocation in cloud networks. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2017.2777979

    Article  Google Scholar 

  47. Lodi A, Martello S, Monaci M (2002) Two-dimensional packing problems: a survey. Eur J Oper Res 141(2):241–252

    Article  MathSciNet  Google Scholar 

  48. Ma J (2017) Resource management framework for virtual data center embedding based on software defined networking. Comput Electric Eng 60:76–89

    Article  Google Scholar 

  49. Martello S, Toth P (1990) Knapsack problems: algorithms and computer implementations. Wiley, New York

    MATH  Google Scholar 

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

    Article  Google Scholar 

  51. Medina A, Lakhina A, Matta I, Byers J (2001) Brite: an approach to universal topology generation. In: 9th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS). IEEE, pp 346–353

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

  53. Mishra M, Das A, Kulkarni P, Sahoo A (2012) Dynamic resource management using virtual machine migrations. IEEE Commun Mag 50(9):34–40

    Article  Google Scholar 

  54. Nam TM, Thanh NH, Hieu HT, Manh NT, Van Huynh N, Tuan HD (2017) Joint network embedding and server consolidation for energy-efficient dynamic data center virtualization. Comput Netw 125:76–89

    Article  Google Scholar 

  55. Quinn P, Nadeau T (2015) Problem statement for service function chaining. Tech. rep

  56. Rabbani MG, Pereira Esteves R, Podlesny M, Simon G, Zambenedetti Granville L, Boutaba R (2013) On tackling virtual data center embedding problem. In: IFIP/IEEE International Symposium on Integrated Network Management. IEEE, pp 177–184

  57. Rabbani MG, Zhani MF, Boutaba R (2014) On achieving high survivability in virtualized data centers. IEICE Trans Commun 97(1):10–18

    Article  Google Scholar 

  58. Reiss C, Tumanov A, Ganger GR, Katz RH, Kozuch MA (2012) Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In: 3rd ACM Symposium on Cloud Computing. ACM, p 7

  59. Saino L, Cocora C, Pavlou G (2013) A toolchain for simplifying network simulation setup. SimuTools 13:82–91

    Google Scholar 

  60. Schneider F, Egawa T, Schaller S, Hayano SI, Schöller M, Zdarsky F (2014) Standardizations of SDN and its practical implementation. NEC Technical Journal, Special Issue on SDN and Its Impact on Advanced ICT Systems 8.2

  61. Sivaranjani B, Vijayakumar P (2015) A technical survey on various VDC request embedding techniques in virtual data center. In: 2015 National Conference on Parallel Computing Technologies (PARCOMPTECH), pp 1–6

  62. Stage A, Setzer T (2009) Network-aware migration control and scheduling of differentiated virtual machine workloads. In: Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing. IEEE Computer Society, pp 9–14

  63. Sun G, Bu S, Anand V, Chang V, Liao D (2016) Reliable virtual data center embedding across multiple data centers. In: 1st Conference on Internet of Things and Big Data, pp 1059–1064

  64. Sun G, Liao D, Bu S, Yu H, Sun Z, Chang V (2017) The efficient framework and algorithm for provisioning evolving VDC in federated data centers. Future Gen Comput Syst 73:79–89

    Article  Google Scholar 

  65. Szeto W, Iraqi Y, Boutaba R (2003) A multi-commodity flow based approach to virtual network resource allocation. In: IEEE Global Telecommunications Conference (GLOBECOM), vol 6. IEEE, pp 3004–3008

  66. Verma A, Dasgupta G, Nayak TK, De P, Kothari R (2009) Server workload analysis for power minimization using consolidation. In: Conference on USENIX Annual Technical Conference. USENIX Association, pp 28–28

  67. Wen X, Han Y, Yu B, Chen X, Xu Z (2016) Towards reliable virtual data center embedding in software defined networking. In: IEEE Military Communications Conference (MILCOM). IEEE, pp 1059–1064

  68. Xie D, Ding N, Hu YC, Kompella R (2012) The only constant is change: incorporating time-varying network reservations in data centers. ACM SIGCOMM Comput Commun Rev 42(4):199–210

    Article  Google Scholar 

  69. Yan F, Lee TT, Hu W (2017) Congestion-aware embedding of heterogeneous bandwidth virtual data centers with hose model abstraction. IEEE/ACM Trans Netw (TON) 25(2):806–819

    Article  Google Scholar 

  70. Yang Y, Chang X, Liu J, Li L (2017) Towards robust green virtual cloud data center provisioning. IEEE Trans Cloud Comput 5(2):168–181

    Article  Google Scholar 

  71. Zegura EW, Calvert KL, Bhattacharjee S (1996) How to model an internetwork. In: Proceedings of IEEE Conference on Computer Communications (INFOCOM), vol 2, pp 594–602

  72. Zhang Q, Zhani MF, Jabri M, Boutaba R (2014) Venice: Reliable virtual data center embedding in clouds. In: IEEE Conference on Computer Communications (INFOCOM). IEEE, pp 289–297

  73. Zhang Z, Su S, Lin Y, Cheng X, Shuang K, Xu P (2015) Adaptive multi-objective artificial immune system based virtual network embedding. J Netw Comput Appl 53:140–155

    Article  Google Scholar 

  74. Zhani MF, Zhang Q, Simon G, Boutaba R (2013) VDC planner: dynamic migration-aware virtual data center embedding for clouds. In: IFIP/IEEE International Symposium on Integrated Network Management (IM). IEEE, pp 18–25

  75. Zheng Q, Shin KG (1998) Fault-tolerant real-time communication in distributed computing systems. IEEE Trans Parallel Distrib Syst 9(5):470–480

    Article  Google Scholar 

  76. Zou J, Yan F, Lee TT, Hu W (2014) A perturbation algorithm for embedding virtual data centers in multipath networks. In: IEEE Global Communications Conference (GLOBECOM). IEEE, pp 2240–2245

  77. Zuo C, Yu H, Anand V (2014) Reliability-aware virtual data center embedding. In: 6th International Workshop on Reliable Networks Design and Modeling (RNDM). IEEE, pp 151–157

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Acknowledgements

We dedicate this research work for the memory of our deceased co-author Prof. Maher Ben Jemaa. We thank the editor and the eighteen anonymous referees who have provided valuable comments on an earlier version of this paper. We would also like to show our gratitude to Mrs Jabeen Nazeer Hussain (Faculty member at the College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University) for the English proofreading of the last version of this paper.

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Correspondence to Mahdi Khemakhem.

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Hbaieb, A., Khemakhem, M. & Ben Jemaa, M. A survey and taxonomy on virtual data center embedding. J Supercomput 75, 6324–6360 (2019). https://doi.org/10.1007/s11227-019-02854-1

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