Optimizing Cloud Storage Using Intelligent Caching Algorithms

Abstract

Cloud computing has become the new foundation of the current digital network, which provides the possibility to store data with scaling and on-demand access to huge amounts of data. Nevertheless, the fast development of the cloud-based applications has posed the significant threats in terms of latency, bandwidth usage as well as storage efficiency. Conventional cloud storage platforms are very much dependent on the concept of traditional or heuristic based caching whereby caching is not always adequate to adjust to the dynamism and heterogeneity of workloads. The given paper is a complete research on the optimization of cloud storage systems by using intelligent caching algorithms. Through machine learning, predictive analytics, and adaptive replacement technologies, intelligent caching will be used to enhance the speed and efficiency of data access, minimizing network congestions, and increasing the efficiency of a system as a whole. The suggested algorithm is a combination of workload-sensitive placement of caches, predicting access pattern, and managing the cache in real time to dynamically optimize storage throughput. The high performance of the cloud of isolated simulated cloud workloads has been extensively tested and results in high improvements in cache hit ratio, response time and bandwidth use as compared to traditional caching methods. The findings support the fact that smart caching schemes offer a solid and scalable remedy to optimization of next-generation cloud storage.