Advertisement

An Artificial Immune Ecosystem Model for Hybrid Cloud Supervision

  • Fabio GuigouEmail author
  • Pierre Parrend
  • Pierre Collet
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

In this paper, we propose a new approach to the performance supervision of complex and heterogeneous infrastructures found in hybrid cloud networks, which typically consist of hundreds or thousands of interconnected servers and networking devices. This hardware and the quality of the interconnections are monitored by sampling specific metrics (such as bandwidth usage, CPU time and packet loss) using probes, and raising alarms in case of an anomaly. We study an Artificial Immune Ecosystem model derived from the Artificial Immune Systems (AIS) algorithms to perform distributed analysis of the data collected throughout the network by these probes. In particular, we use the low variability of the measured data to derive statistical approaches to outlier detection, instead of the traditional stochastic antibody generation and selection method. The failure modes and baseline behaviour of the metrics being monitored (such as bandwidth usage, CPU time and packet loss) are recorded in a distributed learning process and increase the system’s ability to react quickly to suspicious events. By matching the data with only a small number of failure signatures, we reduce the overall computations required to operate the system with respect to traditional AIS, therefore allowing its deployment on low-end monitoring servers or virtual machines. We demonstrate that a very small computational overhead allows the supervision engine to react much faster than the monitoring solutions currently in use.

Notes

Acknowledgements

The work presented here has been funded by IPLine SAS, by the French ANRT in the frame of CIFRE contract 2015/0079 and by the French Banque Publique d’Investissement (BPI) under program FUI-AAP-19 in the frame of the HuMa project.

References

  1. 1.
    Aickelin U, Cayzer S (2008) The danger theory and its application to artificial immune systems. arXiv preprint arXiv:0801.3549Google Scholar
  2. 2.
    Aickelin U, Dasgupta D, Gu F (2014) Artificial immune systems. In: Search methodologies. Springer, Berlin, pp 187–211CrossRefGoogle Scholar
  3. 3.
    Calenbuhr V, Bersini H, Stewart J, Varela FJ (1995) Natural tolerance in a simple immune network. J Theor Biol 177(3):199–213CrossRefGoogle Scholar
  4. 4.
    Dasgupta D, Forrest S (1995) Novelty detection in time series data using ideas from immunology. In: Proceedings of the international conference on intelligent systemsGoogle Scholar
  5. 5.
    Dasgupta D, Majumdar N, Nino F (2007) Artificial immune systems: a bibliography. Computer Science Division, University of Memphis, Technical reportGoogle Scholar
  6. 6.
    Dillon T, Wu C, Chang E (2010) Cloud computing: issues and challenges. In: 2010 24th IEEE international conference on advanced information networking and applications (AINA). IEEE, New York, pp 27–33CrossRefGoogle Scholar
  7. 7.
    Forrest S, Perelson AS, Allen L, Cherukuri R (1994) Self-nonself discrimination in a computer. In: IEEE symposium on security and privacy. Oakland, pp 202–212Google Scholar
  8. 8.
    Forrest S, Hofmeyr SA, Somayaji A (1997) Computer immunology. Commun ACM 40(10): 88–96CrossRefGoogle Scholar
  9. 9.
    Haidar AA (2011) An adaptive document classifier inspired by t-cell cross-regulation in the immune system. Ph.D. thesis, CiteseerGoogle Scholar
  10. 10.
    Haidar AA, Six A, Ganascia J-G, Thomas-Vaslin V (2013) The artificial immune systems domain: identifying progress and main contributors using publication and co-authorship analyses. Adv Artif Life, ECAL 12:1206–1217Google Scholar
  11. 11.
    Hofmeyr SA, Forrest S (1999) An immunological model of distributed detection and its application to computer security. The University of New MexicoGoogle Scholar
  12. 12.
    Hosseinpour F, Amoli PV, Farahnakian F, Plosila J, Hämäläinen T (2014) Artificial immune system based intrusion detection: innate immunity using an unsupervised learning approach. Int J Digit Content Technol Appl 8(5):1Google Scholar
  13. 13.
    Jeswani D, Korde N, Patil D, Natu M, Augustine J (2010) Probe station selection algorithms for fault management in computer networks. In: 2010 second international conference on communication systems and networks (COMSNETS), pp 1–9, Jan 2010Google Scholar
  14. 14.
    Lafferty KJ, AJ Cunningham (1975) A new analysis of allogeneic interactions. Immunol Cell Biol 53(1):27–42Google Scholar
  15. 15.
    Matzinger P (1994) Tolerance, danger, and the extended family. Annu Rev Immunol 12(1): 991–1045CrossRefGoogle Scholar
  16. 16.
    Molina-Jimenez C, Shrivastava S, Crowcroft J, Gevros P (2004) On the monitoring of contractual service level agreements. In: First IEEE international workshop on electronic contracting, 2004. Proceedings, July 2004, pp 1–8Google Scholar
  17. 17.
    Moreno-Vozmediano R, Montero RS, Llorente IM (2012) IaaS cloud architecture: from virtualized datacenters to federated cloud infrastructures. Computer 45(12):65–72CrossRefGoogle Scholar
  18. 18.
    Natu M, Sethi AS (2006) Active probing approach for fault localization in computer networks. In: 2006 4th IEEE/IFIP Workshop on end-to-end monitoring techniques and services, April 2006, pp 25–33Google Scholar
  19. 19.
    Natu M, Sethi AS (2008) Application of adaptive probing for fault diagnosis in computer networks. In: Network operations and management symposium, April 2008. NOMS 2008. IEEE, New York, pp 1055–1060Google Scholar
  20. 20.
    Sahai A, Machiraju V, Sayal M, van Moorsel A, Casati F (2002) Automated SLA monitoring for web services. In: Feridun M, Kropf P, Babin G (eds) Management technologies for e-commerce and e-business applications. Lecture notes in computer science, vol 2506. Springer Berlin, pp 28–41CrossRefGoogle Scholar
  21. 21.
    Salvador S, Chan P (2005) Learning states and rules for detecting anomalies in time series. Appl Intell 23(3):241–255CrossRefGoogle Scholar
  22. 22.
    Silverstein AM (2005) Paul Ehrlich, archives and the history of immunology. Nat Immunol 6(7):639–639CrossRefGoogle Scholar
  23. 23.
    Sotomayor B, Montero RS, Llorente IM, Foster I (2009) Virtual infrastructure management in private and hybrid clouds. IEEE Internet Comput 13(5):14–22CrossRefGoogle Scholar
  24. 24.
    Stibor T, Mohr P, Timmis J, Eckert C (2005) Is negative selection appropriate for anomaly detection? In: Proceedings of the 7th annual conference on Genetic and evolutionary computation. ACM, New York, pp 321–328Google Scholar
  25. 25.
    Thomas-Vaslin V (2014) A complex immunological idiotypic network for maintenance of tolerance. Front Immunol 5:369CrossRefGoogle Scholar
  26. 26.
    Timmis J, Neal M, Hunt J (2000) An artificial immune system for data analysis. Biosystems 55(1):143–150CrossRefGoogle Scholar
  27. 27.
    Vaquero LM, Rodero-Merino L, Morán D (2011) Locking the sky: a survey on IaaS cloud security. Computing 91(1):93–118CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Fabio Guigou
    • 1
    • 2
    • 3
    Email author
  • Pierre Parrend
    • 1
    • 3
    • 4
  • Pierre Collet
    • 1
    • 3
  1. 1.ICube LaboratoryUniversité de StrasbourgStrasbourgFrance
  2. 2.IPLineCaluire-et-CuireFrance
  3. 3.Complex System Digital Campus (UNESCO Unitwin)ParisFrance
  4. 4.ECAM Strasbourg-EuropeSchiltigheimFrance

Personalised recommendations