REDUNDANCY IN NETWORK TRAFFIC: FINDINGS AND IMPLICATIONS

Ashok Anand Ramachandran Ramjee Chitra Muthukrishnan Microsoft Research Lab, India Aditya Akella University of Wisconsin, Madison

2

Redundancy in network traffic Redundancy in network traffic

Popular objects, partial content matches, headers

Redundancy elimination (RE) for improving network efficiency Application layer object caching

Web proxy caches Recent protocol independent RE approaches

WAN optimizers, De-duplication, WAN Backups, etc.

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Protocol independent RE

Message granularity: packet or object chunk Different RE systems operate at different

granularity

WAN link

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RE applications Enterprise and data centers

Accelerate WAN performance

As a primitive in network architecture Packet Caches [Sigcomm 2008] Ditto [Mobicom 2008]

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ISP

Protocol independent RE in enterprises

Enterprises

Wan Opt

Wan Opt

Data centers

Globalized enterprise dilemma Centralized servers

Simple management Hit on performance

Distributed servers Direct request to closest servers Complex management

RE gives benefits of both worlds Deployed in network middle-boxes Accelerate WAN traffic while

keeping management simple

RE for accelerating WAN backup applications

6

ISP

Recent proposals for protocol independent RE

Enterprises

Web content

University

RE deployment on ISP access links to improve capacity

Reduce load on ISP access links Improve effective capacity

Packet caches [Sigcomm 2008] RE on all routers

Ditto [Mobicom 2008] Use RE on nodes in wireless

mesh networks to improve throughput

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Understanding protocol independent RE systems Currently little insight into these RE systems

How far are these RE techniques from optimal? Are there other better schemes? When is network RE most effective? Do end-to-end RE approaches offer performance close

to network RE? What fundamental redundancy patterns drive the

design and bound the effectiveness?

Important for effective design of current systems as well as future architectures e.g. Ditto, packet caches

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Large scale trace-driven study First comprehensive study

Traces from multiple vantage points Focus on packet level redundancy elimination

Performance comparison of different RE algorithms Average bandwidth savings Bandwidth savings in peak and 95th percentile utilization Impact on burstiness

Origins of redundancy Intra-user vs. Inter-user Different protocols

Patterns of redundancy Distribution of match lengths Hit distribution Temporal locality of matches

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Data sets Enterprise packet

traces (3 TB) with payload 11 enterprises

Small (10-50 IPs) Medium (50-100 IPs) Large (100+ IPs)

2 weeks Protocol composition

HTTP (20-55%) Spring et al. (64%)

File sharing (25-70%) Centralization of servers

UW Madison packet traces (1.6 TB) with payload 10000 IPs; trace collected

at campus border router Outgoing /24, web server

traffic 2 different periods of 2

days each Protocol composition

Incoming, HTTP 60% Outgoing, HTTP 36%

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Evaluation methodology Emulate memory-bound (500 MB - 4GB) WAN optimizer

Entire cache resides in DRAM (packet-level RE) Emulate only redundancy elimination

WAN optimizers do other optimizations also Deployment across both ends of access links

Enterprise to data center All traffic from University to one ISP

Replay packet trace

Compute bandwidth savings as (saved bytes/total bytes) Includes packet headers in total bytes Includes overhead of shim headers used for encoding

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Large scale trace-driven study Performance comparison of different RE

algorithms

Origins of redundancy

Patterns of redundancy Distribution of match lengths Hit distribution

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Redundancy elimination algorithms

Redundancy elimination algorithms

Redundancy suppression across

different packets(Use history)

Data compression only within packets

(No history)

MODP (Spring et al.)

MAXP (new algorithm)

GZIP and other variants

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MODP

Packet payloadWindo

wRabin fingerprinting

Value sampling: sample those fingerprints whose value is 0 mod p

Fingerprint table

Packet store

Payload-1

Payload-2

Spring et al. [Sigcomm 2000]

Compute fingerprints

Lookup fingerprints in Fingerprint table

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MAXP

MAXP

Choose fingerprints that are local maxima( or minima) for p bytes region

Similar to MODP Only selection criteria changes

MODP

Sample those fingerprints whose value is 0 mod p

No fingerprint to represent the shaded region

Gives uniform selection of fingerprints

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Optimal Approximate upper bound on optimal

Store every fingerprint in a bloom filter Identify fingerprint match if bloom filter

contains the fingerprint

Low false positive for bloom filter: 0.1%

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Comparison of MODP, MAXP and optimal

MAXP outperforms MODP by 5-10% in most cases Uniform sampling approach of MAXP MODP loses due to non uniform clustering of fingerprints

New RE algorithm which performs better than classical MODP

44040030

40

50

60

70MODP MAXP Optimal

Fingerprint sampling period(p)

Band

wid

th s

avin

gs(%

)

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Small Medium Large Univ/24 Univ-out0

10203040506070

GZIP (10 ms)->GZIPMAXP MAXP->(10ms)->GZIP

Band

wid

th

savi

ngs(

%)

Comparison of different RE algorithms

GZIP offers 3-15% benefit (10ms buffering) -> GZIP increases benefit up to 5%

MAXP significantly outperforms GZIP, offers 15-60% bandwidth savings MAXP -> (10 ms) -> GZIP further enhances benefit up to 8%

We can use combination of RE algorithms to enhance the bandwidth savings

-> means followed by

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Large scale trace-driven study Performance study of different RE

algorithms

Origins of redundancy

Patterns of redundancy Distribution of match lengths Match distribution

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Origins of redundancy

Enterprise Middlebox

Data Centers

Middlebox

Flow-1Flow-2

Flow-3

Flow-1Flow-2Flow-3

Different users accessing the same content, or same content being accessed repeatedly by same user?

Middle-box deployments can eliminate bytes shared across users How much sharing across users in practice?

INTER-USER: sharing across users(a) INTER-SRC(b)INTER-DEST(c) INTER-NODE

INTRA-USER: redundancy within same user (a) INTRA-FLOW (b) INTER-FLOW

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Study of composition of redundancy

90% savings is across destinations for Uout/24

For Uin/Uout, 30-40% savings is due to intra-user

For enterprises, 75-90% savings is due to intra-user

UOut

/24

UOut UIn

Larg

eM

ediu

mSm

all0

102030405060708090

100intersrc

internode

interdst

interflow

intraflow

Cont

ribut

ion

to S

avin

gs (%

)

Inter User

Intra User

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Implication: End-to-end RE as a promising alternative

Enterprise Middlebox

Data Centers

Middlebox

End-to-end RE as a compelling design choice Similar savings Deployment requires just software upgrade

Middle-boxes are expensive Middle-boxes may violate end-to-end semantics

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Large scale trace-driven study Performance study of different RE

algorithms

End-to-end RE versus network RE

Patterns of redundancy Distribution of match lengths Hit distribution

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Match length analysis Do most of the savings come from full

packet matches? Simple technique of indexing full packet will

be good

For partial packet matches, what should be the minimum window size?

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Match length analysis for enterprise

70% of the matches are less than 150 bytes and contribute 20% of savings 10% of the matches come from full matches and contribute 50% of savings Need to index small chunks of size <= 150 bytes for maximum benefit

<=150

150-300

300-450

450-600

600-750

750-900

900-1050

1050-1200

1200-1350

1350-1500

01020304050607080

Match length distribution Contribution to savings

Bins of different match lengths (in bytes)

Perc

enta

ge

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Hit distribution Contributors of redundancy

Few pieces of content repeated multiple times Small packet store would be sufficient

Many pieces of content repeated few times Large packet store

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Zipf-like distribution for chunk matches

Chunk ranking Unique chunk matches

sorted by their hit counts

Straight line shows the zip-fian distribution

Similar to web page access frequency How much popular

chunks contribute to savings?

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Savings due to hit distribution

80% of savings come from 20% of chunks

Need to index 80% of chunks for remaining 20% of savings

Diminishing return for cache size

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Savings vs. cache size

Small packet caches (250 MB) provide significant percentage of savings

Diminishing returns for increasing packet cache size after 250 MB

0 300 600 900 1200 150005

1015202530354045

Small Medium Large

Cache size (MB)

Savi

ngs

(%)

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Conclusion First comprehensive study of protocol

independent RE systems Key Results

15-60% savings using protocol independent RE A new RE algorithm, which performs 5-10% better

than Spring et al. approach Zip-fian distribution of chunk hits; small caches

are sufficient to extract most of the redundancy End-to-end RE solutions are promising

alternatives to memory-bound WAN optimizers for enterprises

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Thank you!

Questions ?

31 Backup slides

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Peak and 95th percentile savings

1 10 100 1000 10000 1000000

102030405060

Mean Median 95%tile Peak

Time (seconds)

Savi

ngs

(%)

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Effect on burstiness Wavelet based multi-resolution analysis

Energy plot higher energy means more burstiness

Compared with uniform compression Results

Enterprise No reduction in burstiness

Peak savings lower than average savings University

Reduction in burstiness Positive correlation of link utilization with redundancy

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Redundancy across protocols Large enterprise

University

Protocol Percentage Volume

Percentage redundancy

HTTP 16.8 29.5SMB 45.46 21.4LDAP 4.85 44.33Src code ctrl 17.96 50.32

Protocol Percentage Volume

Percentage redundancy

HTTP 58 12.49DNS 0.22 21.39RTSP 3.38 2FTP 0.04 16.93