A COMPREHENSIVE SURVEY ON AI-DRIVEN RESOURCE ALLOCATION AND COST OPTIMIZATION TECHNIQUES IN SAAS PLATFORMS

Abstract

This has been possible through the popularity of Software-as-a-Service (SaaS) platforms in changing the way software is made, offered, and used in a cloud setting. As these platforms ramp up, it is very challenging to manage workloads, satisfy varying user needs and respond to high-service-level agreements (SLAs) on costs and resources. The usual inefficient and inflexible management of resources via the traditional static administration techniques tends to result in underutilization, overprovisioning, and augmented operations costs. This survey gives a review of the Artificial Intelligence (AI)-based approaches to optimizing the allocation of resources and reduction of costs within SaaS ecosystems. It emphasizes the application of machine learning, deep learning, and reinforcement learning to automate workload prediction, intelligent scheduling and online resource scaling. Moreover, it analyzes cost prediction models on AI, SLA-wise optimization plans, and use of cloud-native and third-party tools in assisting financial efficiency. Architectural structures of SaaS platforms, as well as critical resource measurements, including CPU, memory, storage and bandwidth, are discussed in the paper. Current issues such as the interpretability of the models, the complexity of integration, data privacy are discussed, and recommendations regarding the future research directions, including explainable AI, federated learning, and multi-objective optimization, are given. The work intends to assist researchers and practitioners to develop intelligent, adaptive, and cost-effective SaaS systems which strike a balance between performance and economical sustainability.