Best Practices for Running Workloads in Public Cloud Environments

Author:

MART JOSEPH1ORCID,Oyetoro Amos2,Amah Ugochukwu3

Affiliation:

1. Joseph Mart obtained his bachelor’s degree in electrical and electronic engineering from the University of Benin, Benin City, Nigeria. He has four years of technical experience across various IT domains. He obtained his master’s degree in computer science in 2022 from Austin Peay State University, Clarksville, Tennessee State. Joseph has multiple certifications with giant IT industries such as Amazon Web Services, Cisco, IBM, CompTIA, Juniper Networks, and Harshi corps Inc. He is an active member of multiple scientific organizations such as IEEE, the National Society of Professional Engineers, the Nonprofit Technology Enterprise Network, and the National Society of Black Engineers. His research interest includes Artificial Intelligence and Machine learning, Cloud Computing, and Cybersecurity.

2. Amos Oyetoro obtained his MSc in Computer Science from Austin Peay State University, Clarksville, Tennessee, United States. He had his bachelor’s degree in computer engineering from Nigeria in 2012. Over the past eight years, Amos has held various positions in the Information Technology industry, from System development to system design and implementation. He has a strong background in Cloud computing and cyber security, Application and system architecture, database design, web application development, and system analysis. He possesses an active member of multiple scientific organizations such as IEEE, the National Society of Professional Engineers, The Nonprofit Technology Enterprise Network, and the National Society of Black Engineers. His primary interest includes Cloud computing, Network and security, Data Analysis and performance, machine learning, and Information Technologies

3. Ugochukwu Amah received his bachelor’s degree in Electronics and Computer Engineering from Nnamdi Azikiwe University, Awka, Anambra State, Nigeria, in 2015 and his master’s degree in computer science from Lamar University, Beaumont, Texas in 2022. He worked for four years in the information technology industry as Cloud Engineer and currently works with one of the top IT companies. His research interest includes cyber security, cloud computing, machine learning, and artificial intelligence. Joshua is active in different scientific societies, such as Member of the National Society of Professional Engineers, The Nonprofit Technology Enterprise Network, and the National Society of Black Engineers.

Abstract

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.

Publisher

ScienceOpen

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