ICETETI-2024 | View PDF
Paper Id:ICETETI-MSM119
doi: 10.71141/30485037/ICETETI-MSM119

Evaluating Cloud Scalability Post-Migration for Mainframe Workloads: A Comparative Analysis

Liam Miller, Muhammadu Sathik Raja

Citation:
Liam Miller, Muhammadu Sathik Raja, "Evaluating Cloud Scalability Post-Migration for Mainframe Workloads: A Comparative Analysis" International Journal of Multidisciplinary on Science and Management, pp. 160-165, 2024.

Abstract:
Cloud scalability has become a critical concern for enterprises transitioning from legacy mainframe systems to modern cloud architectures. This paper provides an in-depth analysis of the scalability challenges and performance improvements associated with post-migration scenarios for mainframe workloads. A comparative study is conducted across multiple cloud platforms, evaluating parameters such as elasticity, latency, cost efficiency, and fault tolerance. The research further explores the impact of containerization, microservices, and auto-scaling mechanisms in optimizing cloud scalability. The findings indicate that while cloud migration offers significant benefits, certain mainframe workloads exhibit unique constraints requiring specialized solutions. The study concludes with recommendations for ensuring optimal scalability while preserving data integrity and operational efficiency.

Keywords: Cloud Scalability, Mainframe Migration, Performance Optimization, Cloud Elasticity, AutoScaling, Containerization, Microservices, Workload Analysis, Cost Efficiency, Cloud Computing.

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