PERFORMANCE ANALYSIS & OPTIMIZING CLOUD STORAGE USING A DYNAMIC WORKLOAD ASSESSMENT - Safeena Ansari; S. Veenadhari (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Cloud storage has become a fundamental component of modern computing, offering scalable and cost-effective solutions for data management. However, optimizing cloud storage performance while handling dynamic workloads remains a significant challenge. This paper explores Dynamic Workload Assessment and Performance Analysis as a strategy to enhance cloud storage efficiency. We analyze workload variations, including read/write operations, latency, and storage utilization patterns, to develop adaptive optimization techniques. Machine learning algorithms and predictive analytics are leveraged to anticipate workload fluctuations and allocate resources dynamically. Additionally, we evaluate various storage optimization strategies such as caching, duplication, compression, and tiered storage management to enhance performance and reduce costs. Experimental results demonstrate that dynamic workload-aware optimizations significantly improve cloud storage responsiveness, throughput, and resource utilization. The study concludes with key recommendations for designing intelligent, self-optimizing cloud storage systems that ensure scalability, efficiency, and cost-effectiveness in dynamic computing environments.