No abstract

Every second, millions of data are being generated due to the use of emerging technologies. It is very challenging to store and handle such a large amount of data. Data deduplication is a solution for this problem. It is a new technique that eliminates duplicate data and stores only a single copy of data, reducing storage utilization and the cost of maintaining redundant data. Content-defined chunking (CDC) has been playing an important role in data deduplication systems due to its ability to detect high redundancy. In this paper, we focused on deduplication system optimization by tuning relevant factors in CDC to identify chunk cut-points and introduce an efficient fingerprint using a new hash function. We proposed a novel bytes frequency-based chunking (BFBC) algorithm and a new low-cost hashing function. To evaluate the efficiency of the proposed system, extensive experiments were done using two different datasets. In all experiments, the proposed system persistently outperformed the common CDC algorithms, achieving a better storage gain ratio and enhancing both chunking and hashing throughput. Practically, our experiments show that BFBC is 10 times faster than basic sliding window (BSW) and approximately three times faster than two thresholds two divisors (TTTD). The proposed triple hash function algorithm is five times faster than SHA1 and MD5 and achieves a better deduplication elimination ratio (DER) than other CDC algorithms. The symmetry of our work is based on the balance between the proposed system performance parameters and its reflection on the system efficiency compared to other deduplication systems.

Due to the quick increase in digital data, especially in mobile usage and social media, data deduplication has become a vital and cost-effective approach for removing redundant data segments, reducing the pressure imposed by enormous volumes of data that must be kept. As part of the data deduplication process, fingerprints are employed to represent and identify identical data blocks. However, when the amount of data increases, the number of fingerprints grows as well, and due to the restricted memory size, the speed of data deduplication suffers dramatically. Various deduplication solutions show a bottleneck in the form of matching lookups and chunk fingerprint calculations, for which we pay in the form of storage and processors needed for storing hashes. Utilizing a fast hash algorithm to improve the fingerprint lookup performance is an appealing challenge. Thus, this study is focused on enhancing the deduplication system by suggesting a novel and effective mathematical bounded linear hashing algorithm that decreases the hashing time by more than two times compared to MD5 and SHA-1 and reduces the size of the hash index table by 50%. Due to the enormous number of chunk hash values, looking up and comparing hash values takes longer for large datasets; this work offers a hierarchal fingerprint lookup strategy to minimize the hash judgement comparison time by up to 78%. Our suggested system reduces the high latency imposed by deduplication procedures, primarily the hashing and matching phases. The symmetry of our work is based on the balance between the proposed hashing algorithm performance and its reflection on the system efficiency, as well as evaluating the approximate symmetries of the hashing and lookup phases compared to the other deduplication systems.