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Nielson-like rating function for multicast updates

IP.com Disclosure Number: IPCOM000031300D
Original Publication Date: 2004-Sep-21
Included in the Prior Art Database: 2004-Sep-21
Document File: 1 page(s) / 22K

Publishing Venue

IBM

Abstract

Currently, software downloads are performed in a dedicated, client to server retrieval methodology. Software can be distributed via a multicast channel allowing for mulitiple simultaneous clients to receive. The frequency of software downloads today is transmitted at a pre-determined frequency which may not be optimal. For example the update being transmitted may be an update that is not needed by the majority of the users. This can be achieved by monitoring the frequency of clients retrieving software downloads and by scheduling the frequency of the software transmission accordingly. In addition add a weighting function that provides more bandwidth to newer updates, and allows them to trail off as more clients download the update.

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Nielson-like rating function for multicast updates

     Currently, software downloads are performed in a dedicated, client to server retrieval methodology. Software can be distributed via a multicast channel allowing for mulitiple simultaneous clients to receive. The frequency of software downloads today is transmitted at a pre-determined frequency which may not be optimal. For example the update being transmitted may be an update that is not needed by the majority of the users.

     This can be achieved by monitoring the frequency of clients retrieving software downloads and by scheduling the frequency of the software transmission accordingly. In addition add a weighting function that provides more bandwidth to newer updates, and allows them to trail off as more clients download the update.

     As software is multicast, the transmitting server will send out a query periodically to determine the number of simultaneous clients that are receiving the data. A counter of the number of simultaneous clients/update will be maintained on the server over a period of time. Based on data collected for each software transmitted, a numerical value will be calculated providing histogram-like data showing client demand for updates. Future transmission schedules will be based on the frequency of active clients. Using this data allows our method to collect all the histrograms for each of the updates, and average the download times, to construct a peak demand schedule for the available band wid...