Kalyan Boggavarapu Lehigh University A Protocol-Independent Technique for Eliminating Redundant Network Traffic Neil T.Spring and David Wetherall Computer Science and Engineering, University of Washington Presented by Kalyan Boggavarapu, Lehigh University 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Introduction 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Aim To eliminate redundant transfers Currently highest cacheabitly obtained is 45% by squid. ( to improve that) Origins of uncached content Dynamically generated or personalized Mirrored on a different server Named by a different URL Delivered using a new or unsupported protocol Updated static content Access counted for advertising revenue 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Algorithm 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Rabin Fingerprint Rabin fingerprint for a sequence of bytes t1,t2,t3,…tß ß = Length of the string M = 260 P = 1048583 Make a table of ti x ß values for fast access (256 entries) RF/FP are calculated for consecutive bytes of length of ß Eg: 1,2,3,4,5,6 bytes,where ß=3 1,2,3 2,3,4 3,4,5 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Algorithm Hold recent packets in the cache Maintain a FP index of the first bytes of the packets Check for each incoming packets Follow LRU for cache replacement 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Architecture 11/16/2018 Kalyan Boggavarapu Lehigh University

Shared Cache Architecture Not implemented Inconsistency is detected by New FPs Protocol independent Web Caching + Delta transmission Bandwidth constrained link Packets = tokens + encoded 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Implementation 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Parameters Selection Selected values are β=64 bytes γ = 5; Throughput is inversly proportional to β, γ; Cache Allocation = 40% for Fp Index, 60% cache 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Amount of Redundancy Incoming is redundancy can be captured by increasing the cache size Outgoing data redundancy has limit 11/16/2018 Kalyan Boggavarapu Lehigh University

Locality of Redundancy Same server, lot of redundancy, therefore we can apply end-end solutions Name based proxy caching would fail to capture this amount of redundancy 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Traffic Analysis 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Link Utilization Less utilization => more Byte savings (cache is divided among less number of users) More utilization => less Byte savings ( can be improved by increasing the cache size) 11/16/2018 Kalyan Boggavarapu Lehigh University

Redundant incoming traffic High 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Summary Best 11/16/2018 Kalyan Boggavarapu Lehigh University To eliminate requests Long Term repitition

Kalyan Boggavarapu Lehigh University Related Work 11/16/2018 Kalyan Boggavarapu Lehigh University

Duplicate Suppression Similar to this method β is larger or equal to the packet size Calculate similarity between larger regions Achieved suppression is less Delta Encoding Send the differences in the page 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Conclusion Redundancy suppression > Duplicate Suppression Redundancy suppression > Delta encoding Protocol independent Useful to suppress 60% of the incoming traffic and 30% of the outgoing traffic 11/16/2018 Kalyan Boggavarapu Lehigh University

Kalyan Boggavarapu Lehigh University Questions ! 11/16/2018 Kalyan Boggavarapu Lehigh University