An Extensive Analysis on Examining Several Data Deduplication Techniques in Cloud Computing

As computer technologies and internet applications are developing at a fast rate, the volume of data is also increasing dramatically. It becomes necessary to store this huge amount of data in the cloud. Following the outbreak of COVID-19 (Coronavirus Disease 2019), it has been observed that offices started working from home and educational institutes began offering online education. As everything becomes online, the demand for storing online data grows. Cloud computing technology has existed long before COVID-19, but it has grown in popularity as a result of the pandemic. Sometimes the same data is being stored multiple times on the cloud by different users, which consumes more storage space, but the storage memory is limited. As a consequence, some storage optimization technique is required, which gives birth to a technique named Deduplication. It is a technique in which duplicated or redundant data is removed to save storage space. This paper presents an extensive analysis of several data deduplication techniques used in cloud computing. The goal is to study the existing techniques of deduplication and then to determine the tradeoff in terms of performance metrices. The graphical and tabular comparison between various existing deduplication techniques is done using parameters like efficiency, throughput, memory consumption, deduplication rate, and computation time. This paper aims to identify the appropriate technique to be used based on the user’s requirements.