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Evolutionary high-dimensional QoS optimization for safety-critical utility communication networks

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Abstract

This paper proposes and evaluates an evolutionary multiobjective optimization algorithm, called EVOLT, which heuristically optimizes quality of service (QoS) parameters in communication networks. EVOLT uses a population of individuals, each of which represents a set of QoS parameters, and evolves the individuals via genetic operators such as crossover, mutation and selection for satisfying given QoS requirements. For evaluating EVOLT in real-world settings that have high-dimensional parameter and optimization objective spaces, this paper focuses on QoS optimization in safety-critical communication networks for electric power utilities. Simulation results show that EVOLT outperforms a well-known existing evolutionary algorithm for multiobjective optimization and efficiently obtains quality QoS parameters with acceptable computational costs. Moreover, EVOLT visualizes obtained QoS parameters in a self-organizing map in order to aid network administrators to intuitively understand the QoS parameters and the tradeoffs among optimization objectives.

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Notes

  1. In the research field of multiobjective optimization, an objective space is considered high-dimensional when it has more than three objectives (Ishibuchi et al. 2008).

  2. http://jns.sourceforge.net/.

  3. http://www.isi.edu/nsnam/ns/.

  4. SOM is an unsupervised classifier that classifies high-dimensional data in a low-dimensional space (Kohonen 1998).

References

  • Abdel-Magid YL, Abido MA (2003) Optimal multiobjective design of robust power system stabilizers using GA. IEEE Trans Power Syst 18(3):1125–1132

    Article  Google Scholar 

  • Abdelzaher TF, Shin KG (1998) End-host architecture for QoS-adaptive communication. In: Proceedings of IEEE symposium on real-time technology and applications, pp 121–130

  • Andersson G, Donalek P, Farmer R, Hatziargyriou N, Kamwa I, Kundur P, Martins N, Paserba J, Pourbeik P, Sanchez-Gasca J et al (2005) Causes of the 2003 major grid blackouts in North America and Europe, and recommended means to improve system dynamic performance. IEEE Trans Power Syst 20(4):1922–1928

    Article  Google Scholar 

  • Barolli L, Koyama A, Shiratori N (2003) A QoS routing method for ad-hoc networks based on genetic algorithm. In: Proceedings of international workshop on database and expert systems applications, pp 175–179

  • Beaty HW (1998) Electric power distribution systems: a nontechnical guide. PennWell Books, Tulsa

    Google Scholar 

  • Champrasert P, Suzuki J, Otani T (2009) Constraint-based evolutionary QoS adaptation for power utility communication networks. In: Proceedings of IEEE international conference on tools with artificial intelligence, pp 395–403

  • Chen S, Nahrstedt K (1998) An overview of quality of service routing for next-generation high-speed networks: problems and solutions. IEEE Netw 12(6):64–79

    Article  Google Scholar 

  • Clune J, Goings S, Punch B, Goodman E (2005) Investigations in meta-GAs: panaceas or pipe dreams? In: Proceedings of ACM conference on genetic and evolutionary computation, pp 235–241

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York

    MATH  Google Scholar 

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

    Article  Google Scholar 

  • Diao Y, Hellerstein JL, Parekh S (2002) Optimizing quality of service using fuzzy control. In: Proceedings of IFIP/IEEE international workshop on distributed systems: operations and management: management technologies for E-commerce and E-business applications, pp 42–53

  • Diao Y, Gandhi N, Hellerstein JL, Parekh S, Tilbury DM (2002) Using MIMO feedback control to enforce policies for interrelated metrics with application to the Apache web server. In: Proceedings of IEEE/IFIP network operations and management symposium, pp 219–234

  • Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin

    MATH  Google Scholar 

  • Eiben AE, Michalewicz Z, Schoenauer M, Smith J (2007) Parameter control in evolutionary algorithms. In: Lobo FG, Lima CF, Michalewicz Z (eds) Studies in computational intelligence, vol 54. Springer, Berlin, pp 19–46

  • Finch JW, Besmi MR (1995) Genetic algorithms applied to a power system stabilizer. In: Proceedings of IEEE international conference on genetic algorithms in engineering systems: innovations and applications, pp 100–105

  • Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, Reading

    MATH  Google Scholar 

  • Haghighat AT, Faez K, Dehghan M, Mowlaei A, Ghahremani Y (2003) GA-based heuristic algorithms for QoS based multicast routing. Knowl Based Syst 16(5–6):305–312

    Article  Google Scholar 

  • Harik GR, Lobo FG (1999) A parameter-less genetic algorithm. In: Proceedings of ACM conference on genetic and evolutionary computation, pp 258–265

  • Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge

    Google Scholar 

  • IEEE Computer Society (2004) 802.1D: IEEE standard for local and metropolitan area networks: media access control (MAC) Bridges. IEEE Computer Society, New York

  • Ishibuchi H, Tsukamoto N, Hitotsuyanagi Y, Nojima Y (2008) Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems. In: Proceedings of ACM conference on genetic and evolutionary computation, pp 649–656

  • Khare V, Yao X, Deb K (2003) Performance scaling of multi-objective evolutionary algorithms. In: Proceedings of international conference on evolutionary multi-criterion optimization, pp 376–390

  • Kohonen T (1998) The self-organizing map. Neurocomputing 21(1–3):1–6

    Article  MATH  Google Scholar 

  • Koyama A, Beralli L, Matsumoto K, Apduhan BO (2004) A GA-based multi-purpose optimization algorithms for QoS routing. In: Proceedings of IEEE international conference on advanced information networking and applications, pp 23–28

  • Lansberry JE, Wozniak L (1994) Adaptive hydrogenerator governor tuning with a genetic algorithm. IEEE Trans Energy Convers 9(1):179–185

    Article  Google Scholar 

  • Liu Z, Squillante MS, Wolf JL (2001) On maximizing service level agreement profits. In: Proceedings of ACM conference on electronic commerce, pp 213–223

  • Lobo FG, Goldberg DE (2004) The parameter-less genetic algorithm in practice. Inf Sci Int J 167(1–4):217–232

    MATH  Google Scholar 

  • Lobo FG, Lima CF, Michalewicz Z (2007) Parameter setting in evolutionary algorithms. Springer, Berlin

    Book  MATH  Google Scholar 

  • Lu C, Stankovic JA, Abdelzaher TF, Tao G, Son SH, Marley M (2000) Performance specifications and metrics for adaptive real-time systems. In: Proceedings of IEEE real-time systems symposium, pp 13–23

  • Menasce DA, Barbara D, Dodge R (2001) Preserving QoS of E-commerce sites through self-tuning: a performance model approach. In: Proceedings of ACM conference on electronic commerce, pp 224–234

  • Meunier H, Talbi EG, Reininger P (2000) A multiobjective genetic algorithm for radio network optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 317–324

  • Montana D, Hussain T, Saxena T (2002) Adaptive reconfiguration of data networks using genetic algorithms. In: Proceedings of ACM conference on genetic and evolutionary computation, pp 1141–1149

  • Northcote-Green J, Wilson R (2006) Control and automation of electrical power distribution systems. CRC Press, Boca Raton

    Google Scholar 

  • Orda A (1999) Routing with end-to-end QoS guarantees in broadband networks. IEEE/ACM Trans Netw 7(3):365–374

    Article  Google Scholar 

  • Purshouse RC, Fleming PJ (2003) Evolutionary many-objective optimization: an exploratory analysis. In: Proceedings of IEEE congress on evolutionary computation, pp 2066–2073

  • Ramírez-Rosado IJ, Bernal-Agustín JL (1998) Genetic algorithms applied to the design of large power distribution systems. IEEE Trans Power Syst 13(2):696–703

    Article  Google Scholar 

  • Riedl A (2002) A hybrid genetic algorithm for routing optimization in IP networks utilizing bandwidth and delay metrics. In: Proceedings of IEEE workshop on IP operations and management, pp 166–170

  • Roy A, Das SK (2004) QM2RP: a QoS-based mobile multicast routing protocol using multi-objective genetic algorithm. Wirel Netw 10(3):271–286

    Article  Google Scholar 

  • Shahidehpour M, Wang Y (2003) Communication and control in electric power systems: applications of parallel and distributed processing. Wiley-IEEE, New York

    Google Scholar 

  • Sun B, Li L (2004) Optimizing on multiple constrained QoS multicast routing algorithms based on GA. J Syst Eng Electron 15(4):677–683

    Google Scholar 

  • Wang Z, Crowcroft J (1996) Quality-of-service routing for supporting multimedia applications. IEEE J Sel Areas Commun 14(7):1228–1234

    Article  Google Scholar 

  • Xiang F, Junzhou L, Jieyi W, Guanqun G (1999) QoS routing based on genetic algorithm. Comput Commun 22(15):1392–1399

    Article  Google Scholar 

  • Ye T, Kalyanaraman S (2003) A recursive random search algorithm for large-scale network parameter configuration. ACM SIGMETRICS Perform Eval Rev 31(1):196–205

    Article  Google Scholar 

  • Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of Massachusetts, Boston, Boston, MA, USA

    Paskorn Champrasert & Junichi Suzuki

  2. Central Research Institute of Electric Power Industry, Tokyo, Japan

    Tetsuo Otani

Authors
  1. Paskorn Champrasert
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  2. Junichi Suzuki
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  3. Tetsuo Otani
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Correspondence to Junichi Suzuki.

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Champrasert, P., Suzuki, J. & Otani, T. Evolutionary high-dimensional QoS optimization for safety-critical utility communication networks. Nat Comput 10, 1431–1458 (2011). https://doi.org/10.1007/s11047-011-9252-2

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