iNetLab: A Model-Driven Development and Performance Engineering Environment for Autonomic Network Applications

  • Hiroshi Wada
  • Chonho Lee
  • Junichi Suzuki
  • Tetsuo Otani


A key software engineering challenge in autonomic computing is the complexity of administrating operational policies of applications. In order to address this challenge, this chapter proposes and evaluates a new development environment, called iNetLab, which is designed to improve the productivity of designing, maintaining, and tuning operational policies in autonomic network applications. iNetLab consists of (1) a set of visual modeling languages specialized to define operational policies in network applications, (2) a set of supporting facilities for those modeling languages, and (3) tools estimates the performance of a network application with its operational policy under development. The proposed visual modeling languages and their supporting facilities can simplify and semi-automate the process to design and maintain operational policies by allowing application administrators (i.e., non-programmers) to graphically deal with operational policies in an intuitive manner. The proposed performance estimation tools leverage the performance history of each network application (i.e., pairs of an operational policy and a performance result obtained in the past) and approximate the application’s performance without deploying and running it actually. This simplifies the process to tune operational policies against desirable performance requirements and contributes to shorten the time to develop autonomic applications.


Agent Behavior Operational Policy Autonomic Computing Eclipse Modeling Framework Behavior Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, D., Lee, K.W., Lobo, J.: Policy-based management of networked computing systems. IEEE Communications Magazine 43(10), 69–75 (2005)CrossRefGoogle Scholar
  2. 2.
    Bahati, R.M., Bauer, M.A., Vieira, E.M.: Policy-driven autonomic management of multi-component systems. In: IBM International Conference on Computer Science and Software Engineering, pp. 137–151. Ontario, Canada (2007)Google Scholar
  3. 3.
    Berek, C.: Somatic hypermutation and b-cell receptor selection as regulators of the immune response. Transfusion Medicine and Hemotherapy 32(6), 333–338 (2005)CrossRefGoogle Scholar
  4. 4.
    Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology 2, 113–120 (1972)Google Scholar
  5. 5.
    Borgatti, S.P.: Centrality and network flow. Social Networks 27(1), 55–71 (2005)CrossRefGoogle Scholar
  6. 6.
    Chase, J., Anderson, D., Thakar, P., Vahdat, A., Doyle, R.: Managing energy and server resources in hosting centers. In: ACM Symposium on Operating Systems Principles, pp. 103–116. Banff, Canada (2001)Google Scholar
  7. 7.
    Dubus, J., Merle, P.: Applying OMG D&C specification and ECA rules for autonomous distributed component-based systems. In: ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, Workshop on Models@Runtime, pp. 242–251. Genova, Italy (2006)Google Scholar
  8. 8.
    Eclipse Foundation: Eclipse graphical modeling framework project.
  9. 9.
    Eclipse Foundation: Eclipse modeling framework project.
  10. 10.
    Hucka, M., Finney, A., Bornstein, B., Keating, S., Shapiro, B., Matthews, J., Kovitz, B., Schilstra, M., Funahashi, A., Doyle, J., Kitano, H.: Evolving a lingua franca and associated software infrastructure for computational systems biology: The systems biology markup language (sbml) project. IEE Systems Biology 406, 41–53 (2004)CrossRefGoogle Scholar
  11. 11.
    Jerne, N.K.: Idiotypic networks and other preconceived ideas. Immunological Review 79, 5–24 (1984)CrossRefGoogle Scholar
  12. 12.
    Jouault, F., B{\’e}zivin, J.: KM3: A DSL for metamodel specification. In: IFIP International Conference on Formal Methods for Open Object-Based Distributed Systems, pp. 171–185. Bologna, Italy (2006)Google Scholar
  13. 13.
    Kasinger, H., Bauer, B.: Towards a model-driven software engineering methodology for organic computing systems. In: IASTED International Conference on Computational Intelligence, pp. 141–146. Alberta, Canada (2005)Google Scholar
  14. 14.
    Kephart, J.O.: Research challenges of autonomic computings. In: ACM/IEEE International Conference on Software Engineering, pp. 15–22. St. Louis, MO, USA (2005)Google Scholar
  15. 15.
    Kephart, J.O., Walsh, W.E.: An artificial intelligence perspective on autonomic computing policies. In: IEEE International Workshop on Policies for Distributed Systems and Networks, pp. 3–12. Yorktown Heights, NY, USA (2004)Google Scholar
  16. 16.
    Kolpakov, F.: Biouml – framework for visual modeling and simulation biological systems. In: International Conference Bioinformatics of Genome Regulation and Structure. Novosibirsk, Russia (2002)Google Scholar
  17. 17.
    Lee, C., Wada, H., Suzuki, J.: Towards a biologically-inspired architecture for self-regulatory and evolvable network applications. In: Advances in Biologically Inspired Information Systems, pp. 21–45. Springer (2007)Google Scholar
  18. 18.
    Lupu, E.C., Sloman, M.: Conflicts in policy-based distributed systems management. IEEE Transactions on Software Engineering 25(6), 852–869 (1999)CrossRefGoogle Scholar
  19. 19. openarchitectureware.
  20. 20.
    Peña, J., Hinchey, M., Sterritt, R., Cortés, A., Resinas, M.: A model-driven architecture approach for modeling, specifying and deploying policies in autonomous and autonomic systems. In: IEEE International Symposium on Dependable Autonomic and Secure Computing, pp. 19–30. Indianapolis, IN, USA (2006)Google Scholar
  21. 21.
    Rohr, M., Boskovic, M., Giesecke, S., Hasselbring, W.: Model-driven development of self-managing software systems. In: ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, Workshop on Models@Runtime, pp. 115–116. Genova, Italy (2006)Google Scholar
  22. 22.
    Scott, J., Ideker, T., Karp, R.M., Sharan, R.: Efficient algorithms for detecting signaling pathways in protein interaction networks. Journal of Computational Biology 13(2), 133–144 (2006)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Trencansky, I., Cervenka, R., Greenwood, D.: Applying a UML-based agent modeling language to the autonomic computing domain. In: ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications, onward! track, pp. 521–529. Portland, OR, USA (2006)Google Scholar
  24. 24.
    Wada, H., Lee, C., Suzuki, J., Otani, T.: A model-driven development environment for biologically-inspired autonomic network applications. In: IEEE International Workshop on Modelling Autonomic Communications Environments, pp. 25–41 (2007)Google Scholar
  25. 25.
    White, J., Schmidt, D., Gokhale, A.: Simplifying autonomic enterprise java bean applications via model-driven engineering and simulation. Journal of Software and System Modeling, Springer 7(1), 3–23 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  • Hiroshi Wada
    • 1
  • Chonho Lee
    • 1
  • Junichi Suzuki
    • 1
  • Tetsuo Otani
    • 2
  1. 1.University of MassachusettsBostonUSA
  2. 2.Central Research Institute of Electric Power IndustryUSA

Personalised recommendations