Root Cause Localization in Microservices with Explain Ability

Project Code :TCMAPY1053

Objective

The objective of this project is to develop a comprehensive and effective approach for root cause localization in microservices-based applications while emphasizing the integration of explain ability. This involves researching advanced techniques and tools to swiftly and accurately pinpoint the sources of system disruptions and performance issues. The primary goal is to enhance the reliability and maintainability of microservices architectures by providing clear and understandable explanations of diagnostic findings. Real-world applications and case studies will validate the proposed approach, ultimately enabling more efficient debugging, reducing downtime, and improving the overall robustness of micro services-based software systems.

Abstract

The increasing adoption of microservices architecture has led to improved scalability and agility in modern software systems. However, this architecture also introduces new challenges in diagnosing and resolving issues that can disrupt the system's functionality. In this context, root cause localization is a critical task for maintaining the reliability of microservices-based applications. This paper presents a comprehensive approach to root cause localization in microservices environments, emphasizing the importance of explainability in the debugging process. We discuss various techniques and tools that aid in pinpointing the root causes of issues within a microservices ecosystem while also providing transparent explanations of the diagnostic results. By integrating explainability into the root cause localization process, we enhance the understanding of system failures and facilitate quicker resolution, ultimately improving the reliability and maintainability of microservices-based applications.

Keywords: ML evaluation, ML techniques etc.,.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.