Optimizing Cloud Computing Resource Utilization Through Intelligent Allocation and Containerization Strategies

Abstract

Cloud computing has become a game-changer for organizations by replacing infrastructure costs with more modular pricing. On top of that, cloud computing increases scale and availability. Still, resource usage is crucial for any type of optimal performance in a cloud environment. This includes detailed approaches for optimizing cloud resource usage with several hypotheses that work together. Auto-scaling capabilities are used to add and remove resources as needed, in real-time. Reserved instances offer incentives for predictable workloads. Tiered storage separates data by access frequency to manage performance versus costs. Cloud-native, intrusive designs—based on microservices and containerization—allow applications to share operating system kernels at a much lower level of overhead than traditional virtualization provides. Sophisticated machine learning engines optimize resource allocation via predictive workload scheduling and intelligent optimization. Serverless computing and functions further automate resource consumption via dynamically-linked functions and services using the exact resources they need. Altogether, these inter-dependencies address the challenges of reducing costs, optimizing performance, and managing resources in today’s cloud environments of use. The productivity of these types of techniques and the improvements in operational efficiency and system performance where they have been implemented have been considerable, as have their impacts on security and compliance.