Comprehensive Review on Optimizing Resource Allocation in Cloud Computing for Cost Efficiency

Abstract

The cloud computing model has become very popular among organizations as they search for the ability to access computing resources on demand and pay-as-you-go with flexible and cost-efficient solutions. Fortunately, cloud environments are highly dynamic, but allocating resources efficiently can be difficult. Workloads may vary over time, and resources must be deployed to meet different performance targets while keeping within the stated budgetary allocations. This implies the tradeoff optimization of cost performance using predictive modeling, automation, and learning from diverse optimization methods. Thus, several solutions are given, such as criteria rules, reinforcement learning, metaheuristic, mathematical programming, and game theory. The aim is to choose the best resource mix that is economically viable and satisfies the threshold performance. There are plenty of benefits for the correct allocation, including less over-provisioning, better performance of applications, more efficient infrastructure use, and evidence-driven planning. The progress made is substantial. However, there still are significant difficulties around benchmarking strategies, uncertainties, coordination, business objectives, designs, and implementations that are robust and scalable. Further study is crucial to retrieve the economic profit of the cloud’s elasticity fully. This article reviews the most recent research on the optimal allocation of resources on cloud computing platforms and cost efficiency.