Coding for Fast Content Download

We study the fundamental trade-off between storage and content download time. We show that the download time can be significantly reduced by dividing the content into chunks, encoding it to add redundancy and then distributing it across multiple disks. We determine the download time for two content access models - the fountain and fork-join models that involve simultaneous content access, and individual access from enqueued user requests respectively. For the fountain model we explicitly characterize the download time, while in the fork-join model we derive the upper and lower bounds. Our results show that coding reduces download time, through the diversity of distributing the data across more disks, even for the total storage used.Comment: 8 pages, 6 figures, conferenc

Scale-free download network for publications

The scale-free power-law behavior of the statistics of the download frequency of publications has been, for the first time, reported. The data of the download frequency of publications are taken from a well-constructed web page in the field of economic physics (http://www.unifr.ch/econophysics/). The Zipf-law analysis and the Tsallis entropy method were used to fit the download frequency. It was found that the power-law exponent of rank-ordered frequency distribution is $\gamma \sim 0.38 \pm 0.04$ which is consistent with the power-law exponent $\alpha \sim 3.37 \pm 0.45$ for the cumulated frequency distributions. Preferential attachment model of Barabasi and Albert network has been used to explain the download network.Comment: 3 pages, 2 figure

Download the PDF of the Full Issue

No abstract available