INSTRUMENTATION-BASED MONITORING TECHNIQUES SURVEY ON HOST, PLATFORM, AND SERVICE LEVEL IN MICROSERVICE ARCHITECTURE

Achmadaniar Anindya Rhosady, Fuad Dary Rosyadi, Lucas Susanto

Abstract


Microservice is an application architecture that separates one big application into smaller ones. The architecture simplifies development, deployment, and management process. However, the architecture is quite complex thus the monitoring process becomes much more challenging. Classifications for the instrumentations that are used in the monitoring process is needed to achieve better practicality for the administrators. We surveyed the monitoring technique classification method in microservice architecture. The method is divided into three levels. They are host level, platform level, and service level. In this paper, we present the latest instruments that are being used in the monitoring process in each level. Correlation between the goals, needs, and stakeholder is also presented.


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S. K. Sownya, P. Deepika and J. Naren, "Layers of Cloud – IaaS, PaaS and SaaS: A Survey," pp. 1-5, 2014.

S. Haselbock and R. Weinreich, "Decision guidance models for microservice monitoring," in Proc. IEEE ICSAW, Gothenburg, Sweden, 2017, pp. 54-61.

S. Haselbock, R. Weinreich and G. Buchgeher, "An Expert Interview Study on Areas of Microservice Design," in Proc. IEEE 11th SOCA, Paris, France, 2018, pp. 137-144.

J. Soldani, D. A. Tamburri and W. J. Van Den Heuvel, "The pains and gains of microservices: A Systematic grey literature review," Journal of Systems and Software, vol. 146, pp. 215-232, 2018.

M. Yang and M. Huang, "A microservices-based OpenStack monitoring tool," in Proc. IEEE 10th ICSESS, Beijing, China, 2019, pp. 706-709.

J. Thalheim, A. Rodrigues, I. E. Akkus, P. Bhatotia, R. Chen, B. Viswanath, and L. Jiao, "Sieve: Actionable insights from monitored metrics in distributed systems," in Proc. 18 IFIP, 2017, pp. 14-27.

E. Fadda, P. Plebani, and M. Vitali, "Optimizing Monitorability of Multi-cloud Applications," CAiSE, 2016.

C. Canali and R. Lancellotti, "An adaptive technique to model virtual machine behavior for scalable cloud monitoring," in Proc. ISCC, Funchal, Portugal, 2014.

A. Noor, D. N. Jha, K. Mitra, P. P. Jayaraman, A. Souza, R. Ranjan, and S. Dustdar, "A framework for monitoring micro service-oriented cloud applications in heterogeneous virtualization environments," in Proc. IEEE 12th CLOUD, Milan, Italy, 2019, pp. 156-163.

M. J. Kargar and A. Hanifizade, "Automation of regression test in microservice architecture," in Proc. 4th ICWR, Tehran, Iran, 2018, pp. 133-137.

F. Pina, J. Correia, R. Filipe, F. Araujo, and J. Cardroom, "Nonintrusive monitoring of microservice-based systems," in Proc. IEEE 17th NCA, Cambridge, MA, USA, 2018.

M. Cinque, R. D. Corte, and A. Pecchia, "Advancing monitoring in microservices systems," in Proc. IEEE ISSREW, Berlin, Germany, 2019, pp. 122-123.

P. Gkikopoulos, "Data distribution and exploitation in a global microservice artifact observatory," in Proc. IEEE World Cong. On Serv., Milan, Italy, 2019, pp. 319-322.

G. Toffetti, S. Brunner, M. Blochlinger, F. Dudouet, and A. Edmonds, "An architecture for self-managing microservices," in Proc. Int. Work. On Aut. Inc. Man. in Cl., 2015, pp. 19-24.




DOI: http://dx.doi.org/10.12962/j24068535.v19i2.a994

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