Novel Read Algorithms for Improving the Performance of Big Data Storage Systems

Cloud computing systems provide scalable infrastructure to store and process Big Data generated by various organizations. Distributed file system (DFS) is used as the main storage component in a cloud computing system for storing and accessing Big Data. Improving the performance of read operations in the DFS is one of the important research issues as more frequently the users perform read operations on the DFS and less frequently the write operations. In this paper, we have developed two novel read algorithms for improving the performance of the read operations of the DFS by considering the presence of the client-side caches, global cache and speculative processing. The main advantages of our algorithms are (i) Reduction in read access time (ii) Write operations do not perform caching and do not require the execution of cache invalidation or synchronization protocols. Our performance evaluation results indicate that the proposed algorithms perform better than the algorithm which does not use caching and speculative processing.