Abstract
One desired aspect of a self-adapting microservices architecture is the ability to continuously monitor the operational environment, detect and observe anomalous behaviour as well as implement a reasonable policy for self-scaling, self-healing, and self-tuning the computational resources in order to dynamically respond to a sudden change in its operational environment. Often the behaviour of a microservices architecture continuously changes over time and the identification of both normal and abnormal behaviours of running services becomes a challenging task. This paper proposes a self-healing Microservice architecture that continuously monitors the operational environment, detects and observes anomalous behaviours, and provides a reasonable adaptation policy using a multi-dimensional utility-based model. This model preserves the cluster state and prevents multiple actions to taking place at the same time. It also guarantees that the executed adaptation action fits the current execution context and achieves the adaptation goals. The results show the ability of this model to dynamically scale the architecture horizontally or vertically in response to the context changes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
References
Ahmad, S., Lavin, A., Purdy, S., Agha, Z.: Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262(Suppl. C), 134–147 (2017)
Anderson, D., Frivold, T., Valdes, A.: Next-generation intrusion detection expert system (NIDES): a summary. SRI International, Computer Science Laboratory Menio Park, CA (1995)
Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 1153–1176 (2016)
Cheng, B., de Lemos, R., Giese, H., Inverardi, P., Magee, J., Malek, R.M., Müller, H., Park, S., Shaw, M., Tichy, M.: Software engineering for self-adaptive systems: a research road map (draft version). In: Dagstuhl Seminar Proceedings 08031 (2008)
Cheng, S.W., Garlan, D., Schmerl, B.: Evaluating the effectiveness of the rainbow self-adaptive system. In: ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2009, pp. 132–141. IEEE (2009)
Cheung-Foo-Wo, D., Tigli, J.Y., Lavirotte, S., Riveill, M.: Self-adaptation of event-driven component-oriented middleware using aspects of assembly. In: Proceedings of the 5th International Workshop on Middleware for Pervasive and Ad-Hoc Computing: Held at the ACM/IFIP/USENIX 8th International Middleware Conference, pp. 31–36 (2007)
Craig, J.W.: A new, simple and exact result for calculating the probability of error for two-dimensional signal constellations. In: Conference Record, Military Communications in a Changing World, Military Communications Conference, MILCOM 1991, pp. 571–575. IEEE (1991)
De Lemos, R., Giese, H., Müller, H.A., Shaw, M., Andersson, J., Litoiu, M., Schmerl, B., Tamura, G., Villegas, N.M., Vogel, T., et al.: Software engineering for self-adaptive systems: a second research roadmap. In: Software Engineering for Self-Adaptive Systems II, pp. 1–32. Springer (2013)
Fishburn, P.C., Kochenberger, G.A.: Two-piece von Neumann-Morgenstern utility functions. Decis. Sci. 10(4), 503–518 (1979)
Golmah, V.: An efficient hybrid intrusion detection system based on C5.0 and SVM. Int. J. Database Theory Appl. 7(2), 59–70 (2014)
Haq, N.F., Onik, A.R., Hridoy, M.A.K., Rafni, M., Shah, F.M., Farid, D.M.: Application of machine learning approaches in intrusion detection system: a survey. IJARAI-Int. J. Adv. Res. Artif. Intell. 4(3), 9–18 (2015)
Hawkins, J., Blakeslee, S.: On Intelligence. Macmillan, London (2007)
Hirschfeld, R., Costanza, P., Nierstrasz, O.M.: Context-oriented programming. J. Object Technol. 7(3), 125–151 (2008)
Horn, P.: Autonomic computing: IBM’s perspective on the state of information technology. Technical report (2001)
Kakousis, K., Paspallis, N., Papadopoulos, G.A.: Optimizing the utility function-based self-adaptive behavior of context-aware systems using user feedback. In: OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”, pp. 657–674. Springer (2008)
Kohavi, R., Provost, F.: Confusion matrix. Mach. Learn. 30(2–3), 271–274 (1998)
Lavin, A., Ahmad, S.: Evaluating real-time anomaly detection algorithms–the Numenta anomaly benchmark. In: 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA, pp. 38–44. IEEE (2015)
Mikalsen, M., Paspallis, N., Floch, J., Stav, E., Papadopoulos, G.A., Chimaris, A.: Distributed context management in a mobility and adaptation enabling middleware (MADAM). In: Proceedings of the 2006 ACM Symposium on Applied Computing, pp. 733–734. ACM (2006)
Mishra, A., Nadkarni, K., Patcha, A.: Intrusion detection in wireless ad hoc networks. IEEE Wirel. Commun. 11(1), 48–60 (2004)
Ongaro, D., Ousterhout, J.K.: In search of an understandable consensus algorithm. In: USENIX Annual Technical Conference, pp. 305–319 (2014)
Phua, C., Lee, V., Smith, K., Gayler, R.: A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119 (2010)
Roesch, M., et al.: Snort: lightweight intrusion detection for networks. In: LISA, vol. 99, pp. 229–238 (1999)
Salehie, M., Tahvildari, L.: Self-adaptive software: landscape and research challenges. Trans. Auton. Adapt. Syst. (TAAS) 4(2), 14 (2009)
Sama, M., Rosenblum, D.S., Wang, Z., Elbaum, S.: Model-based fault detection in context-aware adaptive applications. In: Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering, pp. 261–271. ACM (2008)
Sterritt, R., Bustard, D.: Towards an autonomic computing environment. In: Proceedings of the 14th International Workshop on Database and Expert Systems Applications, pp. 694–698. IEEE (2003)
Strang, T., Linnhoff-Popien, C.: A context modeling survey. In: Workshop on Advanced Context Modelling, Reasoning and Management, UbiComp, vol. 4, pp. 34–41 (2004)
Stubbs, J., Moreira, W., Dooley, R.: Distributed systems of microservices using docker and serfnode. In: 2015 7th International Workshop on Science Gateways, IWSG, pp. 34–39. IEEE (2015)
Wei, W., Fan, X., Song, H., Fan, X., Yang, J.: Imperfect information dynamic stackelberg game based resource allocation using hidden Markov for cloud computing. IEEE Trans. Serv. Comput. 11(1), 78–89 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Magableh, B., Almiani, M. (2020). A Self Healing Microservices Architecture: A Case Study in Docker Swarm Cluster. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_71
Download citation
DOI: https://doi.org/10.1007/978-3-030-15032-7_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15031-0
Online ISBN: 978-3-030-15032-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)