DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models

Samir, Areeg; Pahl, Claus

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DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models

Samir, Areeg; Pahl, Claus

2019 • In Samir, Areeg (Ed.) the 7th International Conference on Future Internet of Things and Cloud

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Disciplines :

Computer science

Author, co-author :

Samir, Areeg ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)

Pahl, Claus

Language :

English

Title :

DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models

Publication date :

2019

Event name :

DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models

Event date :

2019

Audience :

International

Main work title :

the 7th International Conference on Future Internet of Things and Cloud

Author, co-author :

Samir, Areeg

Publisher :

IEEE Computer Society, Istanbul, Turkey, Unknown/unspecified

Peer reviewed :

Peer reviewed

Available on ORBilu :

since 24 January 2020

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Scopus citations^®
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Bibliography

C. Pahl, P. Jamshidi, O. Zimmermann, "Architectural principles for cloud software," ACM Trans on Internet Technology, 2018.
T. F. Düllmann, "Performance anomaly detection in microservice architectures under continuous change," Master, University of Stuttgart, 2017.
C. Pahl, A. Brogi, J. Soldani, P. Jamshidi, "Cloud container technologies: a state-of-The-art review," IEEE Transactions on Cloud Computing, 2017.
R. Heinrich, A. van Hoorn, H. Knoche, F. Li, L.E. Lwakatare, C. Pahl, S. Schulte, J. Wettinger, "Performance engineering for microservices: research challenges and directions," ACM, 2017.
S. Fine, Y. Singer, and N. Tishby, "The hierarchical hidden markov model: analysis and applications," Machine Learning, vol. 32, no. 1, pp. 41-62, 1998.
D. Taibi, V. Lenarduzzi, C. Pahl, "Architectural Patterns for Microservices: A Systematic Mapping Study," CLOSER, 2018.
M. Amaral, J. Polo, D. Carrera, I. Mohomed, M. Unuvar, and M. Steinder, "Performance evaluation of microservices architectures using containers," in Intl Symposium on Network Computing and Applications. 2016, pp. 27-34.
C. Pahl, "An ontology for software component matching," FASE Conference, 2003.
P. Jamshidi, C. Pahl, S. Chinenyeze, X. Liu, "Cloud migration patterns: a multi-cloud service architecture perspective," ICSOC 2014 Workshops, 2014.
Q. Du, T. Xie, and Y. He, "Anomaly detection and diagnosis for container-based microservices with performance," in Intl Conf on Algorithms and Architectures for Parallel Processing. 2018.
A. Samir and C. Pahl, "A Controller Architecture for Anomaly Detection, Root Cause Analysis and Self-Adaptation for Cluster Architectures,", Intl Conf Adaptive and Self-Adaptive Systems and Applications, 2019.
P. Jamshidi, C. Pahl, N.C. Mendonca, "Pattern-based multicloud architecture migration," Software: Practice &Experience, 2017.
J. Ohlsson, "Anomaly detection in microservice infrastructures," Master, School of Computer Science and Communication, 2018.
C. Sauvanaud, M. Kaaniche, K. Kanoun, K. Lazri, and G. Da Silva Silvestre, "Anomaly detection and diagnosis for cloud services: Practical experiments and lessons learned," Journal of Systems and Software, vol. 139, pp. 84-106, 2018.
D. von Leon, L. Miori, J. Sanin, N. E. Ioini, S. Helmer, and C. Pahl, "A performance exploration of architectural options for a middleware for decentralised lightweight edge cloud architectures," in Intl Conference on Internet of Things, Big Data and Security, 2018, pp. 73-84.
P. Jamshidi, A.M. Sharifloo, C. Pahl, A. Metzger, G. Estrada, "Self-learning cloud controllers: Fuzzy q-learning for knowledge evolution," ICCAC, 2015.
H. Sukhwani, "A survey of anomaly detection techniques and hidden markov model," International Journal of Computer Applications, vol. 93, no. 18, pp. 975-8887, 2014.
H. Arabnejad, C. Pahl, P. Jamshidi, G. Estrada, "A comparison of reinforcement learning techniques for fuzzy cloud autoscaling," Intl Symp Cluster, Cloud and Grid Computing, 2017.
P. Jamshidi, C. Pahl, N.C. Mendonca, "Managing uncertainty in autonomic cloud elasticity controllers," IEEE Cloud, 2016.
N. Ge, S. Nakajima, and M. Pantel, "Online diagnosis of accidental faults for real-time embedded systems using a hidden Markov model," Simulation, vol. 91, no. 19, pp. 851-868, 2016.
P. Jamshidi, A. Sharifloo, C. Pahl, H. Arabnejad, A. Metzger, G. Estrada, "Fuzzy self-learning controllers for elasticity management in dynamic cloud architectures," Intl Conf on Quality of Software Architectures, 2016.
R. Scolati, I. Fronza, N. El Ioini, A. Samir, C. Pahl, "A Containerized Big Data Streaming Architecture for Edge Cloud Computing on Clustered Single-Board Devices,", Closer, 2019.
G. Brogi, "Real-time detection of advanced persistent threats using information flow tracking and hidden markov," Doctoral dissertation, 2018.
P. Jamshidi, C. Pahl, N.C. Mendonca, J. Lewis, S. Tilkov, Microservices: The journey so far and challenges ahead, IEEE Software 35 (3), 24-35, 2018.
T. Taylor, "6 Container performance KPIs you should be tracking to ensure DevOps success," 2018.
A. Samir and C. Pahl, "Anomaly Detection and Analysis for Clustered Cloud Computing Reliability,", Intl Conf on Cloud Computing, GRIDs, and Virtualization, 2019.
A. Samir and C. Pahl, "Self-Adaptive Healing for Containerized Cluster Architectures with Hidden Markov Models,", Intl Conf Fog &Mobile Edge Comp, 2019.
T. Chis, "Sliding hidden markov model for evaluating discrete data," 2014.