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

The existence of considerable amount of redundancy in the Internet traffic atthe packet level has stimulated the deployment of packet-level redundancyelimination techniques within the network by enabling network nodes to memorizedata packets. Redundancy elimination results in traffic reduction which in turnimproves the efficiency of network links. In this paper, the concept of networkcompression is introduced that aspires to exploit the statistical correlationbeyond removing large duplicate strings from the flow to better suppressredundancy.  In the first part of the paper, we introduce "memory-assisted compression",which utilizes the memorized content within the network to learn the statisticsof the information source generating the packets which can then be used towardreducing the length of codewords describing the packets emitted by the source.Using simulations on data gathered from real network traces, we show thatmemory-assisted compression can result in significant traffic reduction.  In the second part of the paper, we study the scaling of the averagenetwork-wide benefits of memory-assisted compression. We discuss routing andmemory placement problems in network for the reduction of overall traffic. Wederive a closed-form expression for the scaling of the gain in Erdos-Renyirandom network graphs, where obtain a threshold value for the number ofmemories deployed in a random graph beyond which network-wide benefits start toshine. Finally, the network-wide benefits are studied on Internet-likescale-free networks. We show that non-vanishing network compression gain isobtained even when only a tiny fraction of the total number of nodes in thenetwork are memory-enabled.

Packet-Level Network Compression: Realization and Scaling of the Network-Wide Benefits