Abstract: Real-time reduction of CPU overhead for data compression is performed by a processor device in a computing environment. Non-compressing heuristics are applied on a randomly selected data sample from data sequences for determining whether to compress the data sequences. A compression potential is calculated based on the non-compressing heuristics. The compression potential is compared to a threshold value. The data sequences are either compressed if the compress threshold is matched, compressed using Huffman coding if Huffman coding threshold is matched, or stored without compression.

Abstract: Identification of data candidates for data processing is performed in real time by a processor device in a computing environment. Data candidates are sampled for performing a classification-based compression upon the data candidates. A heuristic is computed on a randomly selected data sample from the data candidate for determining if the data candidate may benefit from the classification-based compression. A decision is provided for approving the classification-based compression on the data candidates according to the heuristic.

Abstract: For real-time classification of data into data compression domains, a decision is made for which of the data compression domains write operations should be forwarded by reading randomly selected data of the write operations for computing a set of classifying heuristics thereby creating a fingerprint for each of the write operations. The write operations having a similar fingerprint are compressed together in a similar compression stream.

Abstract: Machines, systems and methods for compression ratio estimation are provided. The method comprises selecting a plurality of sample points in a data stream to compress sample data selected at said sample points according to a first compression method; and computing an average compression ratio contribution over the selected plurality of sample points, wherein compression ratio contribution of a sample point is determined based on the first compression method and data in locality of the sample point, and wherein the locality for the sample point is defined by locations in the data stream, such that said locations potentially affect the sample point's contribution to the compression ratio.

Abstract: Systems and methods for estimating data reduction ratio for a data set is provided. The method comprises selecting a plurality of m elements from a data set comprising a plurality of N elements; associating an identifier hi for each of the plurality of m elements; associating an identifier he for each of the plurality of elements in the data set; tracking number of times an element i appears in a base set that includes the plurality of m elements selected from the data set; calculating a value counti that indicates the number of times an identifier he matches an identifier hi; and estimating data reduction ratio for the plurality of N elements in the data set, based on number of m number elements selected from the data set and the value counti.

Abstract: Systems and methods for estimating data reduction ratio for a data set is provided. The method comprises selecting a plurality of m elements from a data set comprising a plurality of N elements; associating an identifier hi for each of the plurality of m elements; associating an identifier he for each of the plurality of elements in the data set; tracking number of times an element i appears in a base set that includes the plurality of m elements selected from the data set; calculating a value counti that indicates the number of times an identifier he matches an identifier hi; and estimating data reduction ratio for the plurality of N elements in the data set, based on number of m number elements selected from the data set and the value counti.