Pu and Singaravelu presented Fine-Grain Mixing, an adaptive compression system which aimed to maximize CPU and network utilization simultaneously by splitting a network stream into a mixture of compressed and uncompressed blocks. Blocks were compressed opportunistically in a send buffer, they compressed as many blocks as they could without becoming a bottleneck. They successfully utilized all available CPU and network bandwidth even on high speed connections. In addition, they noted much greater throughput than previous adaptive compression systems. Here, we take a different view of FG-Mixing than was taken by Pu and Singaravelu and give another explanation for its high performance: that fine-grain mixing of compressed and uncompressed blocks enables off-the-shelf compressors to scale down their degree of compression linearly with decreasing CPU usage. Exploring the scaling behavior in-depth allows us to make a variety of improvements to fine-grain mixed compression: better compression ratios for a given level of CPU consumption, a wider range of data reduction and CPU cost options, and parallelized compression to take advantage of multi-core CPUs. We make full compatibility with the ubiquitous deflate decompress or (as used in many network protocols directly, or as the back-end of the gzip and Zip formats) a primary goal, rather than using a special, incompatible protocol as in the original implementation of FG-Mixing. Moreover, we show that the benefits of fine-grain mixing are retained by our compatible version.

Model building is identified as the most important part of the analysis and design process for software systems. A set of primitives to support this process is presented, along with a formal language, MSG.84, for recording the results of analysis and design. The semantics of the notation is defined in terms of the actor formalism, which is based on a message passing paradigm. The automatic derivation of a graphical form of the specification for user review is discussed. Potentials for computer-aided design based on MSG.84 are indicated.