English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recently, the existence of considerable amount of redundancy in the Internettraffic has stimulated the deployment of several redundancy eliminationtechniques within the network. These techniques are often based on eitherpacket-level Redundancy Elimination (RE) or Content-Centric Networking (CCN).However, these techniques cannot exploit sub-packet redundancies. Further,other alternative techniques such as the end-to-end universal compressionsolutions would not perform well either over the Internet traffic, as suchtechniques require infinite length traffic to effectively remove redundancy.This paper proposes a memory-assisted universal compression technique thatholds a significant promise for reducing the amount of traffic in the networks.The proposed work is based on the observation that if a source is to becompressed and sent over a network, the associated universal code entails asubstantial overhead in transmission due to finite length traffic. However,intermediate nodes can learn the source statistics and this can be used toreduce the cost of describing the source statistics, reducing the transmissionoverhead for such traffics. We present two algorithms (statistical anddictionary-based) for the memory-assisted universal lossless compression ofinformation sources. These schemes are universal in the sense that they do notrequire any prior knowledge of the traffic's statistical distribution. Wedemonstrate the effectiveness of both algorithms and characterize thememorization gain using the real Internet traces. Furthermore, we apply thesecompression schemes to Internet-like power-law graphs and solve the routingproblem for compressed flows.

Memory-Assisted Universal Compression of Network Flows