Publication

Abstract : Computing paradigms are changing at a faster pace leading to highly complex and dynamic environments, thereby demanding resource management acumen. Pivoting in these paradigms, auto scaling ensures optimal resource allocation, sufficient to accommodate variable demands. However, the dynamic nature of such settings poses challenges in maintaining reliability and efficiency in auto-scaling methodologies. This paper proposal solves this challenge through a dependable anomaly detection system intended for auto-scaling in cloud computing frameworks. This work tries to design, develop, and validate the framework of anomaly detection for resource consumption and workload patterns using a MAPE-K loop. The envisioned framework is meant to use machine learning techniques and methodologies of data analysis to track in real time system parameters for the early identification of potential scalability issues. In terms of scores for all metrics, it has performed better with the MAPE-K loop, and the R2 score for all metrics came out to be 1.