EPAS: A Sampling Based Similarity Identification Algorithm for the Cloud

The explosive growth of data brings new challenges to the data storage and management in cloud environment. These data usually have to be processed in a timely fashion in the cloud. Thus, any increased latency may cause a massive loss to the enterprises. Similarity detection plays a very important role in data management. Many typical algorithms such as Shingle, Simhash, Traits and Traditional Sampling Algorithm (TSA) are extensively used. The Shingle, Simhash and Traits algorithms read entire source file to calculate the corresponding similarity characteristic value, thus requiring lots of CPU cycles and memory space and incurring tremendous disk accesses. In addition, the overhead increases with the growth of data set volume and results in a long delay. Instead of reading entire file, TSA samples some data blocks to calculate the fingerprints as similarity characteristics value. The overhead of TSA is fixed and negligible. However, a slight modification of source files will trigger the bit positions of file content shifting. Therefore, a failure of similarity identification is inevitable due to the slight modifications. This paper proposes an Enhanced Position-Aware Sampling algorithm (EPAS) to identify file similarity for the cloud by modulo file length. EPAS concurrently samples data blocks from the head and the tail of the modulated file to avoid the position shift incurred by the modifications. Meanwhile, an improved metric is proposed to measure the similarity between different files and make the possible detection probability close to the actual probability. Furthermore, this paper describes a query algorithm to reduce the time overhead of similarity detection. Our experimental results demonstrate that the EPAS significantly outperforms the existing well known algorithms in terms of time overhead, CPU and memory occupation. Moreover, EPAS makes a more preferable tradeoff between precision and recall than that of other similarity detection algorithms. Therefore, it is an effective approach of similarity identification for the cloud.