Containerized Event-Driven Microservice Architecture

Main Article Content

Siti Zulaikha Mohd Zuki
https://orcid.org/0009-0008-2874-1165
Radziah Mohamad
https://orcid.org/0000-0003-4075-9604
Nor Azizah Saadon

Abstract

Microservice architecture offers many advantages, especially for business applications, due to its flexibility, expandability, and loosely coupled structure for ease of maintenance. However, there are several disadvantages that stem from the features of microservices, such as the fact that microservices are independent in nature can hinder meaningful communication and make data synchronization more challenging. This paper addresses the issues by proposing a containerized microservices in an asynchronous event-driven architecture. This architecture encloses microservices in containers and implements an event manager to keep track of all the events in an event log to reduce errors in the application. Experiment results show a decline in response time compared to two other benchmark architectures, as well as a lessening in error rate.

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1.
Containerized Event-Driven Microservice Architecture. Baghdad Sci.J [Internet]. 2024 Feb. 25 [cited 2024 Nov. 19];21(2(SI):0584. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9729
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article

How to Cite

1.
Containerized Event-Driven Microservice Architecture. Baghdad Sci.J [Internet]. 2024 Feb. 25 [cited 2024 Nov. 19];21(2(SI):0584. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9729

References

Blinowski G, Ojdowska A, Przybylek A. Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation. IEEE Access. 2022 Feb;10:20357–74. https://doi.org/10.1109/ACCESS.2022.3152803

Zhang S, Zhang M, Ni L, Liu P. A Multi-Level Self-Adaptation Approach For Microservice Systems. ICCCBDA. 2019;498–502. https://doi.org/10.1109/ICCCBDA.2019.8725647

He H, Su L, Ye K. GraphGRU: A Graph Neural Network Model for Resource Prediction in Microservice Cluster. ICPADS. 2023;499–506. https://doi.org/10.1109/ICPADS56603.2022.00071

Liu H, Zhang W, Zhang X, Cao Z, Tian R. Context-Aware and QoS Prediction-based Cross-Domain Microservice Instance Discovery. ICSESS . 2022;30–4. https://doi.org/10.1109/ICSESS54813.2022.9930241

Wan F, Wu X, Zhang Q. Chain-Oriented Load Balancing in Microservice System. WCCCT. 2020;10–4. https://doi.org/10.1109/WCCCT49810.2020.9169996

Yu X, Wu W, Wang Y. Dependable Workflow Scheduling for Microservice QoS Based on Deep Q-Network. ICWS. 2022;240–5. https://doi.org/10.1109/ICWS55610.2022.00045

Hossen MR, Islam MA, Ahmed K. Practical Efficient Microservice Autoscaling with QoS Assurance. HPDC. 2022;240–52. https://doi.org/10.1145/3502181.3531460

Gan Y, Liang M, Dev S, Lo D, Delimitrou C. Sage: Practical and scalable ML-driven performance debugging in microservices. ASPLOS. 2021;135–51. https://doi.org/10.1145/3445814.3446700

Chen J, Liu F, Jiang J, Zhong G, Xu D, Tan Z, et al. TraceGra: A trace-based anomaly detection for microservice using graph deep learning. Comput Commun. Elsevier B.V. 2023 Apr 15;204:109–17. https://doi.org/10.1016/j.comcom.2023.03.028

Abed MM, Younis MF. Developing load balancing for IoT - Cloud computing based on advanced firefly and weighted round robin algorithms. Baghdad Sci J. 2019;16(1):130–9. https://doi.org/10.21123/bsj.2019.16.1.0130

Kumar S, Kumar N. Conceptual service level agreement mechanism to minimize the SLA violation with SLA negotiation process in cloud computing environment. Baghdad Sci J. 2021 Jun 1;18:1020–9. https://doi.org/10.21123/bsj.2021.18.2(Suppl.).1020

Vohra N, Kerthyayana Manuaba IB. Implementation of REST API vs GraphQL in Microservice Architecture. ICIMTech . 2022;45–50. https://doi.org/10.1109/ICIMTech55957.2022.9915098

Lan Y, Fang L, Zhang M, Su J, Yang Z, Li H. Service dependency mining method based on service call chain analysis. ICSS. 2021;84–9. https://doi.org/10.1109/ICSS53362.2021.00021

Rahmatulloh A, Nugraha F, Gunawan R, Darmawan I. Event-Driven Architecture to Improve Performance and Scalability in Microservices-Based Systems. ICADEIS. 2022. https://doi.org/10.1109/ICADEIS56544.2022.10037390

Singh A, Singh V, Aggarwal A, Aggarwal S. Event Driven Architecture for Message Streaming data driven Microservices systems residing in distributed version control system.ICISTSD. 2022;308–12. https://doi.org/10.1109/ICISTSD55159.2022.10010390

Surantha N, Utomo OK, Lionel EM, Gozali ID, Isa SM. Intelligent Sleep Monitoring System Based on Microservices and Event-Driven Architecture. IEEE Access. 2022;10:42055–66. https://doi.org10.1109/ACCESS.2022.3167637

Mulahuwaish A, Korbel S, Qolomany B. Improving datacenter utilization through containerized service-based architecture. J Cloud Comput; 2022 Dec 1;11(1). https://doi.org/10.1186/s13677-022-00319-0

Matani A, Naji HR, Motallebi H. A Fault-Tolerant Workflow Scheduling Algorithm for Grid with Near-Optimal Redundancy. J Grid Comput. 2020 Sep 1;18(3):377–94. https://doi.org/10.1007/s10723-020-09522-2

Zhou J, Sun J, Zhang M, Ma Y. Dependable Scheduling for Real-Time Workflows on Cyber-Physical Cloud Systems. IEEE Trans Industr Inform. 2021 Nov 1;17(11):7820–9. https://doi.org/10.1109/TII.2020.3011506

Madi T, Esteves-Verissimo P. A Fault and Intrusion Tolerance Framework for Containerized Environments: A Specification-Based Error Detection Approach. SRMC. 2022;1–8. https://doi.org/10.1109/SRMC57347.2022.00005

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