Intelligent Cost Optimization Strategies for Multi-Tenant SaaS Platforms Using Machine Learning

Abstract

The paper is an intelligent cost-optimization strategy with machine learning and how it can be applied to a multi-tenant Software as a Service (SaaS) platform optimization. It suggests a comprehensive design to cost-efficient infrastructure optimization in the cloud, which allows sharing of resources among a number of tenants. This paper will be an attempt to conduct predictive workload analysis with the help of machine learning algorithms that will help discover the patterns of tenant behaviour and resource usage. This allows to make decisions ahead of time about the allocation of resources, minimize waste and optimize efficiency in an information-driven fashion. The platform relies on historical data and usage patterns to perform predictive scaling and scale resources dynamically in order to meet demand in the most cost-effective manner. Another feature discussed in the paper is automated rightsizing that can help to maintain cloud resources according to the changing demands of individual tenants and provide the best performance without excessive provisioning. The platform incorporates FinOps principles to manage costs and focus on financial goals and provides information about cloud economics and how expenses are distributed among tenants. The machine learning models are trained to recognize abnormal utilization and also to optimize the provisioning of resources and this makes it cost effective when the workload is varied. The architecture will ensure SaaS platforms have the capacity to offer high-quality services that are scalable at low costs that cater to the needs of the tenants at high levels and ensure that the performance of the platforms are up to the business goals.