Analysis of Cloud Resource Optimization for Parallel Workloads

Cloud computing has revolutionized IT infrastructure, offering scalability, flexibility, and cost efficiency. However, managing parallel workloads in diverse cloud environments poses challenges. Optimizing cloud resources in parallel computing is crucial for achieving optimal performance and resource utilization. This analysis begins by examining the current landscape of cloud computing, highlighting the challenges associated with managing parallel workloads across disparate cloud environments. It then delves into the intricacies of dynamic resource allocation, examining existing strategies and their limitations in handling complex workload scenarios. Various cloud resource optimization mechanisms and their combinations, specifically tailored for parallel workloads are examined. A comprehensive theoretical analysis is conducted and a comparative evaluation, exploring the principles, functionalities, and synergies of these mechanisms are performed based on some predefined metrics. The insights from this analysis serve as a cornerstone for crafting efficient cloud resource optimization strategies tailored to parallel computing environments. Addressing workload management and resource allocation challenges can lead to enhanced performance and scalability. These findings lay the groundwork for further research in optimizing cloud resources for evolving parallel computing demands.