Cloud computing has become a crucial platform for processing data and executing computationally intensive applications on a pay-per-use basis. Resource allocation is the mechanism by which cloud providers provide resources to users based on their adaptable needs. Service Level Agreement (SLA) between service providers and customers has grown more critical as data continues to grow exponentially. This process of resource distribution gets increasingly difficult as a result of limited resources and rising customer demands. In light of the uniqueness of the models and approaches, the primary objective of resource allocation is to minimize the related overhead costs. The thematic taxonomy of resource allocation dimensions is examined, along with the articles that fall within each category. Focusing on resource and request validation, we propose the Multi-Agent-based Dynamic Resource Allocation (MADRA) strategy, a multistage framework utilizing the QoS-based Resource Allocation (QRA) algorithm, and the Artificial Immune System Directed Acyclic Graph (AIS-DAG) model for optimal resource allocation use to improve QoS and scalability.

Cloud computing, AIS-DAG, Resource allocation, Resource scheduling, QoS