ISSN 2996-8223
International Journal of Computer Science and Technology | Vol. 11, No. 4, April 2009 | pp. 25–32
DOI: 10.46882/2009/IJCST/000013
Article Type: Original Research
Title: A Novel Data Compression Algorithm for Cloud Data Repositories
Names of Authors: Li Wei¹, David Chang²
Authors’ Affiliations: ¹School of Software Engineering, Tsinghua University, Beijing, China; ²Department of Computer Science, Stanford University, Stanford, USA
Abstract: Exponential growth in corporate data generation requires efficient storage techniques to optimize bandwidth and minimize cloud hosting infrastructure costs. This article introduces the Bit-Level Adaptive Compression (BLAC) algorithm, a lossless data compression technique tailored specifically for large-scale structured cloud repositories. BLAC dynamically switches encoding strategies between modified Huffman coding and run-length encoding based on the real-time entropy calculation of data blocks. We benchmarked the algorithm against standard GZIP and LZW formats using text, XML, and numeric databases. Results indicate that BLAC achieves a compression ratio improvement of 1.45:1 over traditional LZW while maintaining comparable CPU execution overhead. The average throughput reached 450 MB/s during decompression cycles on standard hardware configurations. This balanced efficiency drastically lowers data transmission latencies across cloud environments, proving highly beneficial for multi-tenant data centers handling petabyte-scale transactions.
Keywords: Data Compression, Lossless Encoding, Cloud Storage, Huffman Coding, Entropy Analysis, Throughput Optimization
Manuscript Timeline: Received: November 1, 2008; Revised: December 15, 2008; Accepted: January 18, 2009; Published: February 18, 2009
Citation: Wei, L., & Chang, D. (2009). A Novel Data Compression Algorithm for Cloud Data Repositories. International Journal of Computer Science and Technology, 11(4), 25–32.
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