Framework for Ensuring Runtime Stability of Control Loops in Multi-agent Networked Environments

  • Nikolay Tcholtchev
  • Ina Schieferdecker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8360)


The idea of autonomic computing, and accordingly autonomic networking, has drawn the attention of industry and academia during the past years. An autonomic behavior is widely understood as a control loop which is realized by an autonomic entity/agent that manages some resources, in order to improve the performance and regulate diverse operational aspects of the managed network or IT infrastructure. Self-management, realized through autonomic behaviors, is an appealing and dangerous vision at the same time. On one hand, it promises to reduce the need for human involvement in the network and system management processes. On the other hand, it bears a number of potential pitfalls that could be even dangerous to the network, the IT infrastructure, and the corresponding services. One of these pitfalls is constituted by the stability of the control loops, and correspondingly by the interference among multiple autonomic agents operating in parallel. In this paper, a novel approach to ensuring runtime synchronization and stability of multiple parallel autonomic control loops is presented. We formally model the problem of runtime action synchronization, propose different possible solutions, and provide a case study, as well as different performance measurements based on a prototype that implements our approach.


Multi-Agent Systems Autonomic Networks Stability Control Loops 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Autonomic Computing: An architectural blueprint for autonomic computing, IBM White Paper (2006)Google Scholar
  2. 2.
    GLPK, GNU Linear Programming Kit, (as of date March 18, 2012)
  3. 3.
    Chaparadza, R.: Requirements for a Generic Autonomic Network Architecture (GANA), suitable for Standardizable Autonomic Behavior Specifications for Diverse Networking Environments. IEC Annual Review of Communications 61 (December 2008)Google Scholar
  4. 4.
    Bai, J., University, V., Abdelwahed, S.: A Model Integrated Framework for Designing Self-managing Computing Systems. In: The Proceedings of FeBid 2008, Annapolis, Maryland, USA, June 6 (2008)Google Scholar
  5. 5.
    Debbabi, B., Diaconescu, A., Lalanda, P.: Controlling Self-Organising Software Applications with Archetypes. In: 2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems, pp. 69–78 (September 2012)Google Scholar
  6. 6.
    EC funded- FP7-EFIPSANS Project, (as of date March 18, 2013)
  7. 7.
    Thorncraft, S.R.: Evaluation of Open-Source LP Optimization Codes in Solving Electricity Spot Market Optimization Problems. In: 19th Mini-Euro Conference on Operation Research Models and Methods in the Energy Sector, Coimbra, Portugal, September 6-8 (2006)Google Scholar
  8. 8.
    Coin-OR, (as of date March 18, 2013)
  9. 9.
    Derbel, H., Agoulmine, N.: ANEMA: Autonomic network management architecture to support self-configuration and self-optimization in IP networks. Published in the Elsevier Journal of Computer Networks (November 2008)Google Scholar
  10. 10.
    Kephart, J.O., Das, R.: Achieving Self-Management via Utility Functions. IEEE Internet Computing 11(1), 40–48 (2007)CrossRefGoogle Scholar
  11. 11.
    Tesauro, G., Kephart, J.O.: Utility Functions in Autonomic Systems. In: Proceedings of the First International Conference on Autonomic Computing, May 17-18, pp. 70–77 (2004)Google Scholar
  12. 12.
    Lehtihet, E., Derbel, H., Agoulmine, N., Ghamri-Doudane, Y.M., van der Meer, S.: Initial approach toward self-configuration and self-optimization in IP networks. In: Dalmau Royo, J., Hasegawa, G. (eds.) MMNS 2005. LNCS, vol. 3754, pp. 371–382. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    CASCADAS project, (as of date March 18, 2012)
  14. 14.
    Strassner, J., Agoulmine, N., Lehtihet, E.: FOCALE: A Novel Autonomic Networking Architecture. In: Latin American Autonomic Computing Symposium (LAACS), Campo Grande, MS, Brazil (2006)Google Scholar
  15. 15.
    Famaey, J., Latre, S., Strassner, J., De Turck, F.: A hierarchical approach to autonomic network management. In: 2010 IEEE/IFIP Network Operations and Management Symposium Workshops, pp. 225–232 (2010)Google Scholar
  16. 16.
    Höfig, E., et al.: On Concepts for Autonomic Communication Elements. In: Proc. of 1st IEEE International Workshop on Modelling Autonomic Communication Environments (2006)Google Scholar
  17. 17.
    Prakash, A., et al.: “Formal Methods for Modeling, Refining and Verifying Autonomic Components of Computer Networks. Transactions on Computational Science 15, 1–48 (2012)CrossRefMathSciNetGoogle Scholar
  18. 18.
    Rodriguez-Fernández, C., et al.: Self-management capability requirements with SelfMML & INGENIAS to attain self-organising behaviours. In: Proceeding of the Second International Workshop on Self-organizing Architectures, SOAR 2010 (2010)Google Scholar
  19. 19.
    Vassev, E.: ASSL: Autonomic System Specification Language - A Framework for Specification and Code Generation of Autonomic Systems. LAP Lambert Academic Publishing, Germany (November 2009)Google Scholar
  20. 20.
    Vassev, E., Hinchey, M.: The ASSL approach to specifying self-managing embedded systems. Concurrency and Computation: Practice and Experience 24(16), 1860–1878 (2012)CrossRefGoogle Scholar
  21. 21.
    Vassev, E., Mokhov, S.A.: Developing Autonomic Properties for Distributed Pattern-Recognition Systems with ASSL - A Distributed MARF Case Study. Transactions on Computational Science 15, 130–157 (2012)CrossRefGoogle Scholar
  22. 22.
    NLOPT library, (as of date March 18, 2012)
  23. 23.
    Runarsson, T.P., Yao, X.: Search biases in constrained evolutionary optimization. IEEE Trans. on Systems, Man, and Cybernetics Part C: Applications and Reviews 35(2), 233–243 (2005)CrossRefGoogle Scholar
  24. 24.
    Powell, M.J.D.: Direct search algorithms for optimization calculations. Acta Numerica 7, 287–336 (1998)CrossRefGoogle Scholar
  25. 25.
    Birgin, E.G., Martínez, J.M.: Improving ultimate convergence of an augmented Lagrangian method. Optimization Methods and Software 23(2), 177–195 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  26. 26.
    Tcholtchev, N., Chaparadza, R., Prakash, A.: Addressing Stability of Control-Loops in the Context of the GANA Architecture: Synchronization of Actions and Policies. In: Spyropoulos, T., Hummel, K.A. (eds.) IWSOS 2009. LNCS, vol. 5918, pp. 262–268. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  27. 27.
    Kastrinogiannis, T., Tcholtchev, N., Prakash, A., Chaparadza, R., Kaldanis, V., Coskun, H., Papavassiliou, S.: Addressing Stability in Future Autonomic Networking. In: Pentikousis, K., Agüero, R., García-Arranz, M., Papavassiliou, S. (eds.) MONAMI 2010. LNICST, vol. 68, pp. 50–61. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  28. 28.
    Li, N., Chen, G., Zhao, M.: Autonomic Fault Management for Wireless Mesh Networks. Electronic Journal for E-Commence Tools and Applicatoins (eJETA) (January 2009)Google Scholar
  29. 29.
    The FCAPS management framework: ITU-T Rec. M. 3400Google Scholar
  30. 30.
    Fréville, A.: The multidimensional 0-1 knapsack problem: An overview. European Journal of Operational Research 155, 1–21 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  31. 31.
    Mak-Karé Gueye, S., de Palma, N., Rutten, E.: Coordinating energy-aware administration loops using discrete control. In: Proc. of the 8th International Conference on Autonomic and Autonomous Systems, ICAS 2012 (March 2012)Google Scholar
  32. 32.
    Gorski, J., Paquete, L., Pedrosa, F.: Greedy algorithms for a class of knapsack problems with binary weights. Computers & Operations Research 39, 498–511Google Scholar
  33. 33.
    Gueye, S.M.-K., Rutten, E., Tchana, A.: Discrete Control for the Coordination of Administration Loops. In: 2012 IEEE Fifth International Conference on Utility and Cloud Computing (UCC), November 5-8 (2012)Google Scholar
  34. 34.
    Gilmore, P.C., Gomory, R.E.: The theory and computation of knapsack functions. Operations Research 14, 1045–1075 (1966)CrossRefMathSciNetGoogle Scholar
  35. 35.
    EMF, (as of date March 18, 2012)
  36. 36.
    Hessel, A., Pettersson, P.: Model-Based Testing of a WAP Gateway: An Industrial Case-Study. In: Brim, L., Haverkort, B.R., Leucker, M., van de Pol, J. (eds.) FMICS 2006 and PDMC 2006. LNCS, vol. 4346, pp. 116–131. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  37. 37.
    Tcholtchev, N., et al.: Scalable Markov Chain Based Algorithm for Fault-Isolation in Autonomic Networks. In: 2010 IEEE Global Telecommunications Conference, GLOBECOM 2010, pp. 1–6 (2010)Google Scholar
  38. 38.
    Hellerstein, J.L., et al.: Feedback Control of Computing Systems. Wiley-IEEE Press (September 2004) ISBN: 978-0-471-26637-2Google Scholar
  39. 39.
    Schlittgen, R.: Einführung in die Statistik. 9. Auflage. Oldenbourg Wissenschaftsverlag, Oldenbourg (2000) ISBN 3-486-27446-5Google Scholar
  40. 40.
    Wang, F., et al.: A Route Flap Suppression Mechanism Based on Dynamic Timers in OSPF Network. In: Proceedings of ICYCS 2008, pp. 2154–2159 (2008)Google Scholar
  41. 41.
    Farnum, N.R.: Some results concerning the estimation of beta distribution parameters. J. Oper. Res. 38(3), 287–290 (198)Google Scholar
  42. 42.
    de Oliveira Jr., F.A., Sharrock, R., Ledoux, T.: Synchronization of multiple autonomic control loops: Application to cloud computing. In: Sirjani, M. (ed.) COORDINATION 2012. LNCS, vol. 7274, pp. 29–43. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  43. 43.
    Tcholtchev, N., Grajzer, M., Vidalenc, B.: Towards a Unified Architecture for Resilience, Survivability and Autonomic Fault-Management for Self-Managing Networks. In: Dan, A., Gittler, F., Toumani, F. (eds.) ICSOC/ServiceWave 2009. LNCS, vol. 6275, pp. 335–344. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  44. 44.
    Charalambous, T., Kalyvianaki, E.: A min-max framework for CPU resource provisioning in virtualized servers using H-infinity Filters. In: 2010 49th IEEE Conference on Decision and Control (CDC), December 15-17, pp. 3778–3783 (2010)Google Scholar
  45. 45.
    Kalyvianaki, E., Charalambous, T., Hand, S.: Resource Provisioning for Multi-Tier Virtualized Server Applications. Computer Measurement Group Journal (CMG Journal 2010) (126), 6–17 (2010)Google Scholar
  46. 46.
    Kalyvianaki, E., Charalambous, T., Hand, S.: Self-Adaptive and Self-Configured CPU Resource Provisioning for Virtualized Servers Using Kalman Filters. In: 6th Int. Conference on Autonomic Computing andCommunications, ICAC 2009 (2009)Google Scholar
  47. 47.
    ETSI AFI, (as of date March 18, 2012)
  48. 48.
    Frey, S., Diaconescu, A., Demeure, I.: Architectural Integration Patterns for Autonomic Management Systems. In: 9th IEEE International Conference and Workshops on the Engineering of Autonomic and Autonomous Systems (EASe 2012), Novi Sad, Serbia, April 11-13 (2012)Google Scholar
  49. 49.
    Agoulmine, N.: Autonomic Network Management Principles. Academic Press (December 2010) ISBN: 0123821908Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Nikolay Tcholtchev
    • 1
  • Ina Schieferdecker
    • 1
  1. 1.Fraunhofer FOKUS Institute for Open Communication SystemsBerlinGermany

Personalised recommendations