An Infrastructure Cost Optimised Algorithm for Partitioning of Microservices
<|reference_start|>An Infrastructure Cost Optimised Algorithm for Partitioning of Microservices: The evolution and advances made in the field of Cloud engineering influence the constant changes in software application development cycle and practices. Software architecture has evolved along with other domains and capabilities of software engineering. As migrating applications into the cloud is universally adopted by the software industry, microservices have proven to be the most suitable and widely accepted architecture pattern for applications deployed on distributed cloud. Their efficacy is enabled by both technical benefits like reliability, fault isolation, scalability and productivity benefits like ease of asset maintenance and clear ownership boundaries which in turn lead to fewer interdependencies and shorter development cycles thereby resulting in faster time to market. Though microservices have been established as an architecture pattern over the last decade, many organizations fail to optimize the architecture design to maximize efficiency. In some cases, the complexity of migrating an existing application into the microservices architecture becomes overwhelmingly complex and expensive. Additionally, automation and tool support for this problem are still at an early stage as there isn't a single well-acknowledged pattern or tool which could support the decomposition. This paper discusses a few impactful previous research and survey efforts to identify the lack of infrastructure cost optimization as a parameter in any of the approaches present. This paper proposes an Infrastructure-optimised predictive algorithm for partitioning monolithic software into microservices. It also summarizes the scope for future research opportunities within the area of microservices architecture and distributed cloud networks.<|reference_end|>
@article{pendyala2024an,
    title={An Infrastructure Cost Optimised Algorithm for Partitioning of
  Microservices},
    author={Kalyani V N S Pendyala and Rajkumar Buyya},
    journal={Proceedings of the 6th Asia Conference on Machine Learning and
  Computing (ACMLC 2024, ISBN: 979-8-4007-1001-8, ACM Press, USA), Bangkok,
  Thailand, July 26-28, 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06570},
    primaryClass={cs.SE}
}