Deep Learning and Optimization Algorithms for Intelligent Cloud Resource Allocation: A Survey

With the arrival of the cloud computing, modern IT infrastructure has been transformed by the ability to provision resources on demand and scalable over the internet. Nevertheless, due to the dynamic and heterogeneous nature of user demands, such resource utilization, energy consumption, and service quality are difficult to achieve efficiently. The focus of this paper is to provide comprehensive survey of artificial intelligence (AI) based approaches, namely machine learning (ML) and deep learning (DL) models for predictive resource scaling in cloud environment. It studies combining Neural networks, Ensemble models and Metaheuristic optimization for forecast of Resource Demand and increase the accuracy of allocation. Challenges addressed are minimizing energy usage, lowering operational cost and meeting service level agreements. This survey also presents recent results for feature selection, hybrid optimization and performance tuning strategies appropriate for cloud systems. The insights from this work are meant to assist the further research in intelligent, adaptive, and sustainable cloud resource management.