Designing Novel Algorithms for Optimizing Data Analytics and Storage in High-Performance Cloud Environments

Abstract

The fast rise of data-intensive applications has put a great burden on the cloud infrastructures, and the efficient algorithms needed are those ones that would optimize both the performance of the storage and the data analytics. The paper describes the design and implementation of new algorithms that are intended to enhance the speed, scalability, and power efficiency of high-performance cloud systems. The suggested models combine adaptive machine learning systems and heuristic optimization tools to improve the process of data processing, eliminate redundancy, and balance the allocation of work among the various cloud nodes. The algorithms show enhanced throughput, reduced latency and optimal storage use relative to the traditional ones through simulation and benchmarking. The study presents the importance of using smart algorithm design to reduce computational overhead without affecting reliability and cost-effectiveness. The results are relevant to the future development of cloud-based data management systems and form a basis of incorporating AI-based resource optimization into a high-performance computing infrastructures in the future.