Operationalizing Batch Workloads in the Cloud with Case Studies
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Informative Article | Engineering Science | India | Volume 9 Issue 7, July 2020 | Popularity: 4.5 / 10


     

Operationalizing Batch Workloads in the Cloud with Case Studies

Ramakrishna Manchana


Abstract: The rapid adoption of cloud computing has transformed the landscape of batch processing, offering unprecedented scalability, flexibility, and cost-efficiency. However, simply migrating existing batch workloads to the cloud (the "lift-and-shift" approach) often fails to fully leverage the cloud's potential. This paper explores strategies and best practices for operationalizing batch workloads in the cloud, going beyond mere migration to achieve true cloud-native optimization. We delve into key considerations such as orchestration, data management, monitoring, error handling, security, and cost optimization. Through a comparative analysis of leading cloud platforms (AWS, Azure, and GCP) and real-world use cases, we provide a comprehensive guide for organizations seeking to unlock the full potential of batch processing in the cloud-native era.


Keywords: Cloud-Native, Batch Processing, AWS, Azure, GCP, Orchestration, Data Management, Monitoring, Error Handling, Security, Cost Optimization


Edition: Volume 9 Issue 7, July 2020


Pages: 2031 - 2041



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Ramakrishna Manchana, "Operationalizing Batch Workloads in the Cloud with Case Studies", International Journal of Science and Research (IJSR), Volume 9 Issue 7, July 2020, pp. 2031-2041, https://www.ijsr.net/getabstract.php?paperid=SR24820052154, DOI: https://www.doi.org/10.21275/SR24820052154

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