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
5/28/2016
Kalyan Boggavarapu
Lehigh University
Introduction
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Kalyan Boggavarapu
Lehigh University
Aim
 To eliminate redundant transfers
 Currently highest cacheabitly obtained is
45% by squid. ( to improve that)
 Origins of uncached content
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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
Kalyan Boggavarapu
Lehigh University
Algorithm
5/28/2016
Kalyan Boggavarapu
Lehigh University
Rabin Fingerprint
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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
5/28/2016
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
5/28/2016
Kalyan Boggavarapu
Lehigh University
Architecture
5/28/2016
Kalyan Boggavarapu
Lehigh University
Shared Cache Architecture
 Not implemented
 Inconsistency is
detected by New
FPs
 Protocol
independent
 Web Caching +
Delta transmission
5/28/2016
Packets =
tokens +
encoded
Kalyan Boggavarapu
Lehigh University
Bandwidth
constrained
link
Implementation
5/28/2016
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%
5/28/2016
Kalyan Boggavarapu
cache
Lehigh University
Amount of
Redundancy
Incoming is redundancy
can be captured by
increasing the cache
size
Outgoing data
redundancy has limit
5/28/2016
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
5/28/2016
Kalyan Boggavarapu
Lehigh University
Traffic Analysis
5/28/2016
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)
5/28/2016
Kalyan Boggavarapu
Lehigh University
Redundant incoming traffic
5/28/2016
Kalyan Boggavarapu
Lehigh University
High
Summary
Best
5/28/2016
To eliminate requests
Kalyan Boggavarapu
Lehigh University
Long Term repitition
Related Work
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5/28/2016
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
5/28/2016
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
5/28/2016
Kalyan Boggavarapu
Lehigh University
Questions !
5/28/2016
Kalyan Boggavarapu
Lehigh University