Authors : Shankar Dheeraj Konidena

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/48xnp57y

Scribd : https://tinyurl.com/yckbef2b

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT092

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The current paper studies the impact of Autoscaling on application performance in Cloud computing environments. Cloud computing is one of the most encouraging innovations due to its vast applications. Predictive autoscaling is an advanced technique that aims to address the challenges in the autoscaling trends for large-scale systems. To account for the Quality of Service to the customer, features like load balancing according to the workload demand, understanding resource allocation and utilization, and dynamic decision-making are vital to any cloud computing application. This paper interprets these challenges and reviews a meta-reinforcement learning approach for predictive autoscaling in cloud environments. A novel RL-based predictive autoscaling approach on a popular large-scale digital payment platform system, Alipay, is compared with the existing models such as Autopilot and FIRM. The aim is to conduct a detailed analysis of performance metrics before and after autoscaling actions, aiming to identify optimal scaling strategies that minimize response time and maximize resource utilization without over- provisioning.

Keywords : Predictive Autoscaling, Application Performance, Cloud Applications, Machine Learning, Reinforcement Learning.

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