Skip to main content

Self-managed Computer Systems: Foundations and Examples

  • Conference paper
  • First Online:
Enterprise Information Systems (ICEIS 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 378))

Included in the following conference series:

Abstract

The traditional approach to managing complex computer systems is to use a cadre of skilled IT professionals who use monitoring tools in order to detect when problems arise. They are then able to use their skills and experience to determine what actions should be taken to solve the problems. This approach is no longer viable for highly complex, networked computer information systems that have numerous configuration knobs, and operate in environments that vary with time at a very high rate. In this case, one cannot expect that design-time configurations will make the system operate optimally at run-time. For that reason, complex systems need to manage themselves using controllers that make the systems self-configuring, self-optimizing, self-healing, and self-protecting. This paper provides a formalism to describe self-managed systems and discusses concrete examples that illustrate how these properties are enforced by controllers in a variety of domains including cloud computing, fog/cloud computing, Internet datacenters, distributed software systems, and secure database systems.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

References

  1. Albassam, E., Porter, J., Gomaa, H., Menascé, D.A.: DARE: a distributed adaptation and failure recovery framework for software systems. In: 2017 IEEE International Conference on Autonomic Computing (ICAC), pp. 203–208 (2017)

    Google Scholar 

  2. Aldhalaan, A., Menascé, D.A.: Autonomic allocation of communicating virtual machines in hierarchical cloud data centers. In: 2014 International Conference on Cloud and Autonomic Computing, pp. 161–171 (2014)

    Google Scholar 

  3. Alomari, F.B., Menascé, D.A.: Self-protecting and self-optimizing database systems: implementation and experimental evaluation. In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, CAC 2013, pp. 18:1–18:10, New York, NY, USA. ACM (2013)

    Google Scholar 

  4. Arnaboldi, M., Brondolin, R., Santambrogio, M.D.: Hyppo: hybrid performance-aware power-capping orchestrator. In: 2018 IEEE International Conference on Autonomic Computing (ICAC), pp. 71–80 (2018)

    Google Scholar 

  5. Bajunaid, N., Menascé, D.A.: Efficient modeling and optimizing of checkpointing in concurrent component-based software systems. J. Syst. Softw. 139, 1–13 (2018)

    Article  Google Scholar 

  6. Bennani, M., Menascé, D.: Resource allocation for autonomic data centers using analytic performance models. In: Proceedings of International Conference on Automatic Computing, ICAC 2005, pp. 229–240, Washington, DC, USA. IEEE Computer Society (2005)

    Google Scholar 

  7. Bennani, M.N., Menasce, D.A.: Assessing the robustness of self-managing computer systems under highly variable workloads. In: International Conference on Autonomic Computing, 2004, Proceedings, pp. 62–69 (2004)

    Google Scholar 

  8. Connell, W., Menasce, D.A., Albanese, M.: Performance modeling of moving target defenses with reconfiguration limits. IEEE Trans. Dependable Secure Comput. (2018). https://doi.org/10.1109/TDSC.2018.2882825

  9. Esfahani, N., Yuan, E., Canavera, K.R., Malek, S.: Inferring software component interaction dependencies for adaptation support. ACM Trans. Auton. Adapt. Syst. 10(4), 26:1–26:32 (2016)

    Article  Google Scholar 

  10. Ewing, J., Menascé, D.A.: Business-oriented autonomic load balancing for multitiered web sites. In: Proceedings of the International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS. IEEE (2009)

    Google Scholar 

  11. Ewing, J.M., Menascé, D.A.: A meta-controller method for improving run-time self-architecting in SOA systems. In: Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering, ICPE 2014, pp. 173–184. ACM, New York (2014)

    Google Scholar 

  12. Horn, G., Rozanska, M.: Affine scalarization of two-dimensional utility using the Pareto front. In: 2019 IEEE International Conference on Autonomic Computing (ICAC), pp. 147–156 (2019)

    Google Scholar 

  13. Imes, C., Zhang, H., Zhao, K., Hoffmann, H.: Copper: soft real-time application performance using hardware power capping. In: 2019 IEEE International Conference on Autonomic Computing (ICAC), pp. 31–41 (2019)

    Google Scholar 

  14. Intel. Enhanced Intel speedstep technology for the Intel Pentium M processor (2004)

    Google Scholar 

  15. Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Comput. 36(1), 41–50 (2003)

    Article  Google Scholar 

  16. Krzywda, J., Ali-Eldin, A., Wadbro, E., Ostberg, P., Elmroth, E.: Alpaca: aaplication performance aware server power capping. In: 2018 IEEE International Conference on Autonomic Computing (ICAC), pp. 41–50 (2018)

    Google Scholar 

  17. Menascé, D.: Security performance. IEEE Internet Comput. 7(3), 84–87 (2003)

    Article  Google Scholar 

  18. Menascé, D., Gomaa, H., Malek, S., Sousa, J.: SASSY: a framework for self-architecting service-oriented systems. IEEE Softw. 28, 78–85 (2011)

    Article  Google Scholar 

  19. Menasce, D.A.: TPC-W: a benchmark for e-commerce. IEEE Internet Comput. 6(3), 83–87 (2002)

    Article  Google Scholar 

  20. Menascé, D.A.: Modeling the tradeoffs between system performance and CPU power consumption. In: Proceedings of the International Conference on Computer Measurement Group, CMG (2015)

    Google Scholar 

  21. Menascé, D.A.: Taming complexity with self-managed systems. In: 21st International Conference on Enterprise Information Systems (ICEIS), vol. 1, pp. 5–13 (2019)

    Google Scholar 

  22. Menascé, D.A., Almeida, V.A.F., Dowdy, L.W.: Performance by Design: Computer Capacity Planning by Example. Prentice Hall, Upper Saddle River (2004)

    Google Scholar 

  23. Menascé, D.A., Krishnamoorthy, M., Brodsky, A.: Autonomic smart manufacturing. J. Decis. Syst. 24(2), 206–224 (2015)

    Article  Google Scholar 

  24. Miettinen, K.: Nonlinear Multiobjective Optimization. Springer, New York (1999). https://doi.org/10.1007/978-1-4615-5563-6

    Book  MATH  Google Scholar 

  25. Pfannemueller, M., Krupitzer, C., Weckesser, M., Becker, C.: A dynamic software product line approach for adaptation planning in autonomic computing systems. In: 2017 IEEE International Conference on Autonomic Computing (ICAC), pp. 247–254 (2017)

    Google Scholar 

  26. Schmitt, N., Iffländer, L., Bauer, A., Kounev, S.: Online power consumption estimation for functions in cloud applications. In: 2019 IEEE International Conference on Autonomic Computing (ICAC), pp. 63–72 (2019)

    Google Scholar 

  27. Sopitkamol, M., Menascé, D.A.: A method for evaluating the impact of software configuration parameters on e-commerce sites. In: Proceedings of the 5th International Workshop on Software and Performance, WOSP 2005, pp. 53–64. ACM, New York (2005)

    Google Scholar 

  28. Tadakamalla, U., Menascé, D.: FogQN: an analytic model for fog/cloud computing. In: Proceedings of the 1st Workshop on Managed Fog-to-Cloud (mF2C), pp. 307–313 (2018)

    Google Scholar 

  29. Tadakamalla, U., Menascé, D.: Autonomic resource management using analytic models for fog/cloud computing. In: IEEE International Conference on Fog Computing (ICFC 2019), pp. 69–79 (2019a)

    Google Scholar 

  30. Tadakamalla, U., Menascé, D.: Characterization of IoT Workloads, pp. 1–15 (2019b)

    Google Scholar 

  31. Tadakamalla, V., Menascé, D.A.: Model-driven elasticity control for multi-server queues under traffic surges in cloud environments. In: 2018 International Conference on Autonomic Computing (ICAC), pp. 157–162. IEEE (2018)

    Google Scholar 

  32. Tantawi, A.N., Steinder, M.: Autonomic cloud placement of mixed workload: an adaptive bin packing algorithm. In: 2019 IEEE International Conference on Autonomic Computing (ICAC), pp. 187–193 (2019)

    Google Scholar 

  33. Tesfatsion, S.K., Wadbro, E., Tordsson, J.: Perfgreen: performance and energy aware resource provisioning for heterogeneous clouds. In: 2018 IEEE International Conference on. Autonomic Computing (ICAC), pp. 81–90 (2018)

    Google Scholar 

  34. von Kistowski, J., Deffner, M., Kounev, S.: Run-time prediction of power consumption for component deployments. In: 2018 IEEE International Conference on Autonomic Computing (ICAC), pp. 151–156 (2018)

    Google Scholar 

  35. Weyns, D., Malek, S., Andersson, J.: Forms: unifying reference model for formal specification of distributed self-adaptive systems. ACM Trans. Auton. Adapt. Syst. 7(1), 8:1–8:61 (2012)

    Article  Google Scholar 

  36. Yuan, E., Esfahani, N., Malek, S.: A systematic survey of self-protecting software systems. ACM Trans. Auton. Adapt. Syst. 8(4), 17:1–17:41 (2014)

    Article  Google Scholar 

  37. Zangeneh, V., Shajari, M.: A cost-sensitive move selection strategy for moving target defense. Comput. Secur. 75, 72–91 (2018)

    Article  Google Scholar 

  38. Zoghi, P., Shtern, M., Litoiu, M., Ghanbari, H.: Designing adaptive applications deployed on cloud environments. ACM Trans. Auton. Adapt. Syst. 10(4), 25:1–25:26 (2016)

    Article  Google Scholar 

  39. Zuefle, M., Bauer, A., Lesch, V., Krupitzer, C., Herbst, N., Kounev, S., Curtef, V.: Autonomic forecasting method selection: examination and ways ahead. In: 2019 IEEE International Conference on Autonomic Computing (ICAC), pp. 167–176 (2019)

    Google Scholar 

Download references

Acknowledgements

I would like to thank my former Ph.D. students whose work is referenced here: Arwa Aldhalaan, Firas Alomari, Noor Bajunaid, Mohamed Bennani, Warren Connell, John Ewing, Mohan Krishnamoorthy, Uma Tadakamlla, and Venkat Tadakamalla.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daniel A. Menascé .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Menascé, D.A. (2020). Self-managed Computer Systems: Foundations and Examples. In: Filipe, J., Śmiałek, M., Brodsky, A., Hammoudi, S. (eds) Enterprise Information Systems. ICEIS 2019. Lecture Notes in Business Information Processing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-030-40783-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-40783-4_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-40782-7

  • Online ISBN: 978-3-030-40783-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics