Skip to main content
SpringerLink
Log in
Book cover

International Conference on Simulation Tools and Techniques

SIMUtools 2020: Simulation Tools and Techniques pp 513–532Cite as

A Simulation-Based Optimization Framework for Online Adaptation of Networks

  • Conference paper
  • First Online:

  • 824 Accesses

Abstract

Today’s data centers face continuous changes, including deployed services, growing complexity, and increasing performance requirements. Customers expect not only round-the-clock availability of the hosted services but also high responsiveness. Besides optimizing software architectures and deployments, networks have to be adapted to handle the changing and volatile demands. Approaches from self-adaptive systems can optimize data center networks to continuously meet Service Level Agreements (SLAs) between data center operators and customers. However, existing approaches focus only on specific objectives like topology design, power optimization, or traffic engineering.

In this paper, we present an extensible framework that analyzes networks using different types of simulation and adapts them subject to multiple objectives using various adaptation techniques. Analyzing each suggested adaptation ensures that the network continuously meets the performance requirements and SLAs. We evaluate our framework w.r.t. finding Pareto-optimal solutions considering a multi-dimensional cost model, and scalability on a typical data center network. The evaluation shows that our approach detects the bottlenecks and the violated SLAs correctly, outputs valid and cost-optimal adaptations, and keeps the runtime for the adaptation process constant even with increasing network size and an increasing number of alternative configurations.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://gitlab2.informatik.uni-wuerzburg.de/descartes/dni-adaptation.

  2. 2.

    https://gitlab2.informatik.uni-wuerzburg.de/descartes/dni-adaptation.

References

  1. Ahn, J.H., Binkert, N., Davis, A., McLaren, M., Schreiber, R.S.: HyperX: Topology, Routing, and Packaging of Efficient Large-Scale Networks. In: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, pp. 1–11 (November 2009)

    Google Scholar 

  2. Arcaini, P., Riccobene, E., Scandurra, P.: Modeling and analyzing MAPE-K feedback loops for self-adaptation. In: 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 13–23. IEEE (2015)

    Google Scholar 

  3. Bari, M.F., et al.: Data center network virtualization: a survey. IEEE Commun. Surv. Tutorials 15(2), 909–928 (2013)

    Article  Google Scholar 

  4. Bause, F.: Queueing Petri Nets-A formalism for the combined qualitative and quantitative analysis of systems. In: Proceedings of 5th international workshop on Petri nets and performance models, pp. 14–23. IEEE (1993)

    Google Scholar 

  5. Benson, T., Anand, A., Akella, A., Zhang, M.: The Case for Fine-Grained Traffic Engineering in Data Centers. In: INM/WREN (2010)

    Google Scholar 

  6. Benson, T., Anand, A., Akella, A., Zhang, M.: MicroTE: fine grained traffic engineering for data centers. In: 7th CoNEXT. ACM (2011)

    Google Scholar 

  7. Buchanan, J.M.: The relevance of pareto optimality. J. Conflict Resolut. 6(4), 341–354 (1962)

    Article  Google Scholar 

  8. Bültmann, D., Mühleisen, M., Klagges, K., Schinnenburg, M.: OpenWNS-open Wireless Network Simulator. In: European Wireless Conference, EW. IEEE (2009)

    Google Scholar 

  9. Farooq Butt, N., Chowdhury, M., Boutaba, R.: Topology-awareness and Reoptimization Mechanism for Virtual Network Embedding. In: Crovella, M., Feeney, L.M., Rubenstein, D., Raghavan, S.V. (eds.) NETWORKING 2010. LNCS, vol. 6091, pp. 27–39. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12963-6_3

    Chapter  Google Scholar 

  10. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23-50 (2011)

    Google Scholar 

  11. Campanile, L., Gribaudo, M., Iacono, M., Marulli, F., Mastroianni, M.: Computer network simulation with ns-3: a systematic literature review. Electronics 9(2), 272 (2020)

    Article  Google Scholar 

  12. Chen, M., Miao, Y., Humar, I.: OPNET IoT Simulation. Springer Nature, Singapore (2019)

    Google Scholar 

  13. Coello, C.A.C., Lamont, G.B., Van Veldhuizen, D.A., et al.: Evolutionary Algorithms for Solving Multi-objective Problems, vol. 5. Springer, New York (2007)

    Google Scholar 

  14. Curtis, A.R., Carpenter, T., Elsheikh, M., López-Ortiz, A., Keshav, S.: Rewire: An Optimization-based Framework for Unstructured Data Center Network Design. In: INFOCOM. IEEE (2012)

    Google Scholar 

  15. Datta, S., Das, S.: Multiobjective support vector machines: handling class imbalance with pareto optimality. IEEE Trans. Neural Netw. Learn. Syst. 30(5), 1602–1608 (2018)

    Article  MathSciNet  Google Scholar 

  16. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  17. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)

    Article  MathSciNet  Google Scholar 

  18. Dorigo, M., Di Caro, G.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 2, pp. 1470–1477. IEEE (1999)

    Google Scholar 

  19. Durillo, J.J., Nebro, A.J., Alba, E.: The jMetal framework for multi-objective optimization: Design and architecture. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)

    Google Scholar 

  20. Fang, W., Liang, X., Li, S., Chiaraviglio, L., Xiong, N.: VMPlanner: optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers. Comput. Netw. 57(1), 179–196 (2013)

    Article  Google Scholar 

  21. Fredericks, E.M., Gerostathopoulos, I., Krupitzer, C., Vogel, T.: Planning as optimization: dynamically discovering optimal configurations for runtime situations. In: Proceedings of the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019, IEEE (June 2019)

    Google Scholar 

  22. Garlan, D., Cheng, S.W., Huang, A.C., Schmerl, B., Steenkiste, P.: Rainbow: architecture-based self-adaptation with reusable infrastructure. Computer 37(10), 46–54 (2004)

    Article  Google Scholar 

  23. Herrnleben, S.: Model-Based Network Analysis and Optimization. Master Thesis, University of Wuerzburg (2017)

    Google Scholar 

  24. Herrnleben, S., Rygielski, P., Grohmann, J., Eismann, S., Hossfeld, T., Kounev, S.: Model-based performance predictions for SDN-based networks: a case study. In: Proceedings of the 20th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems. MMB 2020 (March 2020)

    Google Scholar 

  25. Jarschel, M., Zinner, T., Hoßfeld, T., Tran-Gia, P., Kellerer, W.: Interfaces, attributes, and use cases: a compass for SDN. IEEE Commun. Mag. 52(6), 210–217 (2014)

    Article  Google Scholar 

  26. Jiang, J.W., Lan, T., Ha, S., Chen, M., Chiang, M.: Joint VM placement and routing for data center traffic engineering. In: INFOCOM, vol. 12 (2012)

    Google Scholar 

  27. Kim, M., Hiroyasu, T., Miki, M., Watanabe, S.: SPEA2+: improving the performance of the strength pareto evolutionary algorithm 2. In: Yao, X. (ed.) PPSN 2004. LNCS, vol. 3242, pp. 742–751. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_75

    Chapter  Google Scholar 

  28. Kounev, S.: Performance modeling and evaluation of distributed component-based systems using queueing petri nets. IEEE Trans. Software Eng. 32(7), 486–502 (2006)

    Article  Google Scholar 

  29. Kounev, S., Buchmann, A.: SimQPN–a tool and methodology for analyzing queueing Petri net models by means of simulation. Perform. Eval. 63(4–5), 364–394 (2006)

    Article  Google Scholar 

  30. Lalanda, P., McCann, J.A., Diaconescu, A.: Autonomic Computing. Springer, New York (2013)

    Google Scholar 

  31. Lawler, E.L., Wood, D.E.: Branch-and-bound methods: a survey. Oper. Res. 14(4), 699–719 (1966)

    Article  MathSciNet  Google Scholar 

  32. Lukasiewycz, M., Glaß, M., Reimann, F., Teich, J.: Opt4J - a modular framework for meta-heuristic optimization. In: Proceedings of the Genetic and Evolutionary Computing Conference (GECCO 2011), pp. 1723–1730. Dublin, Ireland (2011)

    Google Scholar 

  33. Luke, S.: Ecj then and now. In: GECCO (Companion), pp. 1223–1230 (2017)

    Google Scholar 

  34. Martí, R., Laguna, M., Glover, F.: Principles of scatter search. Eur. J. Oper. Res. 169(2), 359–372 (2006)

    Article  MathSciNet  Google Scholar 

  35. Nguyen-Ngoc, A., Lange, S., Geissler, S., Zinner, T., Tran-Gia, P.: Estimating the flow rule installation time of SDN switches when facing control plane delay. In: 19th International GI/ITG MMB Conference. Erlangen (2 2018)

    Google Scholar 

  36. Pawar, C.S., Wagh, R.: A review of resource allocation policies in cloud computing. World J. Sci. Technol. 2(3), 165–167 (2012)

    Google Scholar 

  37. Przybylski, A., Gandibleux, X.: Multi-objective branch and bound. Eur. J. Oper. Res. 260(3), 856–872 (2017)

    Article  MathSciNet  Google Scholar 

  38. Qiu, T., Li, B., Qu, W., Ahmed, E., Wang, X.: TOSG: A topology optimization scheme with global small world for industrial heterogeneous Internet of Things. IEEE Trans. Industr. Inf. 15(6), 3174–3184 (2018)

    Article  Google Scholar 

  39. Qu, L., Assi, C., Shaban, K.: Delay-aware scheduling and resource optimization with network function virtualization. IEEE Trans. Commun. 64(9), 3746–3758 (2016)

    Article  Google Scholar 

  40. Reyes-Sierra, M., Coello, C.C., et al.: Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int. J. Comput. Intell. Res. 2(3), 287–308 (2006)

    MathSciNet  Google Scholar 

  41. Riley, G.F.: Simulation of large scale networks II: large-scale network simulations with GTNetS. In: Proceedings of the 35th Conference on Winter Simulation: Driving Innovation. Winter Simulation Conference (2003)

    Google Scholar 

  42. Rygielski, P., Seliuchenko, M., Kounev, S.: Modeling and prediction of software-defined networks performance using queueing petri nets. In: Proceedings of the Ninth International Conference on Simulation Tools and Techniques (SIMUTools 2016) (August 2016). http://dl.acm.org/citation.cfm?id=3021426.3021437

  43. Sachs, K., Kounev, S., Buchmann, A.: Performance modeling and analysis of message-oriented event-driven systems. Soft. Syst. Model. 12(4), 705–729 (2013)

    Article  Google Scholar 

  44. Sofi, I.B., Gupta, A., Jha, R.K.: Power and energy optimization with reduced complexity in different deployment scenarios of massive MIMO network. Int. J. Commun. Syst. 32(6), e3907 (2019)

    Article  Google Scholar 

  45. Sommer, J., Scharf, J.: IKR Simulation Library. In: Wehrle, K., Güneş, M., Gross, J. (eds.) Modeling and Tools for Network Simulation. Springer, Berlin https://doi.org/10.1007/978-3-642-12331-3_4 (2010)

  46. Tajiki, M.M., Salsano, S., Chiaraviglio, L., Shojafar, M., Akbari, B.: Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining. In: IEEE TNSM (2018)

    Google Scholar 

  47. Tso, F.P., Pezaros, D.P.: Improving data center network utilization using near-optimal traffic engineering. IEEE Trans. Parallel Distrib. Syst. 24(6), 1139–1148 (2013)

    Article  Google Scholar 

  48. Varga, A.: A practical introduction to the OMNeT++ simulation framework. In: Virdis, A., Kirsche, M. (eds.) Recent Advances in Network Simulation. Varga, A.: A practical introduction to the OMNeT++ simulation framework. In: Recent Advances in Network Simulation, pp. 3–51. Springer (2019), pp. 3–51. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12842-5_1

    Chapter  Google Scholar 

  49. Varga, A., Hornig, R.: An Overview of the OMNeT++ Simulation Environment. In: SIMUtools 2008. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ICST, Brussels, Belgium, Belgium (2008). http://dl.acm.org/citation.cfm?id=1416222.1416290

  50. Wang, L., Zhang, F., Vasilakos, A.V., Hou, C., Liu, Z.: Joint virtual machine assignment and traffic engineering for green data center networks. SIGMETRICS Perform. Eval. Rev. 41(3), 107–112 (2014)

    Article  Google Scholar 

  51. Züfle, M., et al.: Autonomic Forecasting Method Selection: Examination and Ways Ahead. In: Proceedings of the International Conference on Autonomic Computing (ICAC), pp. 167–176 (2019)

    Google Scholar 

Download references

Acknowledgements

This work was funded by the German Research Foundation (DFG) under grant No. (KO 3445/18-1). Special thanks to our student Pascal Fries, who assisted us with the implementation and evaluation of the alternative route adaptation tactic.

Author information

Authors and Affiliations

  1. University of Würzburg, Würzburg, Germany

    Stefan Herrnleben, Johannes Grohmann, Veronika Lesch, Christian Krupitzer & Samuel Kounev

  2. D4L data4life gGmbH, Potsdam, Germany

    Piotr Rygielski

Authors
  1. Stefan Herrnleben
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Johannes Grohmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Piotr Rygielski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Veronika Lesch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christian Krupitzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Samuel Kounev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefan Herrnleben .

Editor information

Editors and Affiliations

  1. Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics and Aeronautic, UESTC, Chengdu, China

    Dingde Jiang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Herrnleben, S., Grohmann, J., Rygielski, P., Lesch, V., Krupitzer, C., Kounev, S. (2021). A Simulation-Based Optimization Framework for Online Adaptation of Networks. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72792-5_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72791-8

  • Online ISBN: 978-3-030-72792-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation