A Network Science-Based Approach for an Optimal Microservice Governance

Gihan Saranga Siriwardhana; Nishitha De Silva; Liyanage Sanjaya Jayasinghe; Lakshitha Vithanage

A Network Science-Based Approach for an Optimal Microservice Governance

Gihan Saranga Siriwardhana * §

Contact

Nishitha De Silva

Contact

Liyanage Sanjaya Jayasinghe

Contact

Lakshitha Vithanage

Contact

§ Sri Lanka Institute of Information Technology

A Network Science-Based Approach for an Optimal Microservice Governance

Article Fingerprint

ReserarchID

CST138M5

A Network Science-Based Approach for an Optimal Microservice Governance Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

With the introduction of microservice architecture for the development of software applications, a new breed of tools, platforms, and development technologies emerged that enabled developers and system administrators to monitor, orchestrate and deploy their containerized microservice applications more effectively and efficiently. Among these vast arrays of technologies, Kubernetes has become one such prominent technology widely popular due to its ability to deploy and orchestrate containerized microservices. Nevertheless, a common issue faced in such orchestration technologies is the employment of vast arrays of disjoint monitoring solutions that fail to portray a holistic perspective on the state of microservice deployments, which in turn, inhibit the creation of more optimized deployment policies. In response to this issue, this publication proposes the use of a network science-based approach to the creation of a microservice governance model that incorporates the use of dependency analysis, load prediction, centrality analysis, and resilience evaluation to effectively construct a more holistic perspective on a given microservice deployment.

References

12 Cites in Article

Reference Format

Pooyan Jamshidi,Claus Pahl,Nabor Mendonca,James Lewis,Stefan Tilkov (2018). Microservices: The Journey So Far and Challenges Ahead.
Sean Whitesell,Rob Richardson (2020). Healthy Microservices.
Philippe Martin (2020). Kubernetes API Introduction.
Philippe Martin (2020). Kubernetes API Introduction.
Pablo Acuña (2020). Kubernetes.
R Heinrich (2017). Performance engineering for microservices: Research challenges & directions.
Maria Fazio,Antonio Celesti,Rajiv Ranjan,Chang Liu,Lydia Chen,Massimo Villari (2016). Open Issues in Scheduling Microservices in the Cloud.
Dongmin Kim,Hanif Muhammad,Eunsam Kim,Sumi Helal,Choonhwa Lee (2019). TOSCA-Based and Federation-Aware Cloud Orchestration for Kubernetes Container Platform.
Anshul Jindal,Vladimir Podolskiy,Michael Gerndt (2019). Performance Modeling for Cloud Microservice Applications.
Víctor Medel,Omer Rana,José Bañares,Unai Arronategui (2016). Modelling performance & resource management in kubernetes.
Thomas Düllmann,André Van Hoorn (2017). Model-driven Generation of Microservice Architectures for Benchmarking Performance and Resilience Engineering Approaches.
(2020). Fig. 3. The shape of the logistic function: a — the standard; b — the transformed. x — the argument of the logistic function; x(t) — the argument of the transformed logistic function; σ — the output variable of the logistic function; h(t) — the output variable of the transformed logistic function..

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Gihan Saranga Siriwardhana, Nishitha De Silva, Liyanage Sanjaya Jayasinghe, Lakshitha Vithanage. 2026. "A Network Science-Based Approach for an Optimal Microservice Governance". Global Journal of Computer Science and Technology - B: Cloud & Distributed GJCST-B Volume 22 (GJCST Volume 22 Issue B1).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 22 Issue B1

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

C.2.0

Submission ReceivedOctober 1, 2020
Peer Review Double Blind
Handling Editor
Accepted October 29, 2020
Published November 9, 2020

Version of record

v1.2

Issue date

November 21, 2022

Language

English

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Views: 941

Total Downloads: 59

All Trends