Resource Allocation Strategies for Scalable Big Data Processing in Cloud Environments

Abstract

This study investigates how cloud computing systems share resources like processing power and storage to manage big data tasks. The aim is to identify the most effective methods for resource allocation that are both powerful and cost-saving. By thoroughly reviewing existing research papers, industry reports, and case studies, we will compare different strategies for distributing resources in cloud environments. Our focus is finding efficient approaches to processing large amounts of data and being mindful of budget constraints. After evaluating these methods, we will recommend the one that offers the best balance between performance and affordability. This study is theoretical, relying on analysis of published findings rather than conducting new experiments. The outcome will give cloud service providers and researchers more precise insights into resource allocation methods. This understanding could lead to improved management of cloud resources and potentially inspire new, more efficient strategies in the future.