The article provides an overview of cloud computing workloads. Despite the fast-paced advancements in cloud technology, there has been limited focus on analyzing and describing these workloads. However, gaining a deep understanding of the properties and behaviors of these workloads is crucial for effectively deploying cloud technologies and achieving desired service levels. The parallel and distributed systems field has general principles that can be applied to cloud workloads . Cloud workloads have unique characteristics that require careful consideration from both researchers and practitioners. This document emphasizes these distinctive features and discusses the primary issues associated with deploying cloud workloads. Furthermore, this document highlights the areas that require doing so, we aim to provide valuable insights that will enable organizations to optimize their use of cloud computing and ensure they are fully leveraging the potential of this rapidly evolving technology. Also, it discusses Cloud Environments such as AWS, GCP, and Azure. In this study, we will analyze how well cloud computing services perform when used for scientific computing workloads. Our research aims to address the challenges posed by scientific computing workloads and evaluate the suitability of existing cloud computing platforms for these workloads. Through this analysis, we hope to shed light on the potential benefits and limitations of cloud computing for scientific computing and provide insights into how these platforms can be optimized to serve the scientific communities' needs better.