2026, Vol. 7, Issue 1, Part A

Cloud computing has revolutionized the way computational resources are provisioned and managed across various sectors. As cloud environments grow in scale, the need for efficient resource scheduling and allocation techniques has become more critical. Resource provisioning involves assigning cloud resources such as computing power, memory, and storage in response to users’ demands while ensuring minimal cost, maximum efficiency, and optimal performance. A key challenge in cloud resource provisioning is balancing supply and demand, as resource demands fluctuate dynamically. The efficient scheduling and allocation of resources is essential to achieve a balance between system performance and operational costs.

This paper explores various techniques employed in cloud resource provisioning, emphasizing strategies for efficient scheduling and allocation. It examines methods such as dynamic provisioning, load balancing, and resource prediction algorithms, which ensure that resources are allocated effectively in real-time. Additionally, the integration of machine learning and AI-based approaches in cloud systems has opened new avenues for predictive provisioning and proactive resource management. By analyzing these techniques, the paper aims to identify their strengths, limitations, and potential for further enhancement. Furthermore, it investigates the role of containerization and virtualization in resource allocation, which enables improved resource utilization and isolation.

The objective of this research is to provide an in-depth review of these techniques and present a framework for future advancements in cloud resource provisioning. The hypothesis proposed is that by optimizing resource scheduling and allocation, cloud environments can achieve not only cost-efficiency but also high system performance and reliability under diverse workloads.