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Redundancy in network traffic: findings and implications
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Joint International Conference on Measurement and Modeling of Computer Systems archive
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems table of contents
Seattle, WA, USA
SESSION: Traffic analysis table of contents
Pages 37-48  
Year of Publication: 2009
ISBN:978-1-60558-511-6
Authors
Ashok Anand  University of Wisconsin, Madison, WI, USA
Chitra Muthukrishnan  University of Wisconsin, Madison, WI, USA
Aditya Akella  University of Wisconsin, Madison, WI, USA
Ramachandran Ramjee  Microsoft Research, Bangalore, India
Sponsors
ACM: Association for Computing Machinery
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
Publisher
ACM  New York, NY, USA
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ABSTRACT

A large amount of popular content is transferred repeatedly across network links in the Internet. In recent years, protocol-independent redundancy elimination, which can remove duplicate strings from within arbitrary network flows, has emerged as a powerful technique to improve the efficiency of network links in the face of repeated data. Many vendors offer such redundancy elimination middleboxes to improve the effective bandwidth of enterprise, data center and ISP links alike.

In this paper, we conduct a large scale trace-driven study of protocol independent redundancy elimination mechanisms, driven by several terabytes of packet payload traces collected at 12 distinct network locations, including the access link of a large US-based university and of 11 enterprise networks of different sizes. Based on extensive analysis, we present a number of findings on the benefits and fundamental design issues in redundancy elimination systems. Two of our key findings are (1) A new redundancy elimination algorithm based on Winnowing that outperforms the widely-used Rabin fingerprint-based algorithm by 5-10% on most traces and by as much as 35% in some traces. (2) A surprising finding that 75-90% of middlebox's bandwidth savings in our enterprise traces is due to redundant byte-strings from within each client's traffic, implying that pushing redundancy elimination capability to the end hosts, i.e. an end-to-end redundancy elimination solution, could obtain most of the middlebox's bandwidth savings.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Citrix, application delivery infrastructure. http://www.citrix.com/.
 
2
Computerworld -- WAN optimization continues growth. www.computerworld.com.au/index.php/id;1174462047;fp;16;fpid;0/.
 
3
F5 Networks: WAN Delivery Products. http://www.f5.com/.
 
4
Netequalizer Bandwidth Shaper. http://www.netequalizer.com/.
 
5
Packeteer WAN optimization solutions. http://www.packeteer.com/.
 
6
PeerApp: P2P and Media Caching. http://www.peerapp.com.
 
7
Peribit Networks (Acquired by Juniper in 2005): WAN Optimization Solution. http://www.juniper.net/.
 
8
Riverbed Networks: WAN Optimization. http://www.riverbed.com/solutions/optimize/.
 
9
WAN optimization revenues grow 16% -- IT Facts. www.itfacts.biz/wan-optimization--market-to-grow-16/1205/.
 
10
WAN Optimization: Wikipedia entry. http://en.wikipedia.org/wiki/WAN_Optimization.
 
11
P. Abry and D. Veitch.Wavelet analysis of long-range dependent traffic. IEEE Transactions on Information Theory, 44(1):2--15, Jan 1998.
12
 
13
N. Bjorner, A. Blass, and Y. Gurevich. Content-Dependent Chunking for Differential Compression, the Local Maximum Approach. Technical Report 109, Microsoft Research, July 2007.
 
14
L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker. Web caching and zipf-like distributions: Evidence and implications. In IEEE Infocom, 1999.
 
15
M. Burrows and D. J.Wheeler. A block-sorting lossless data compression algorithm. Technical report, Digital SRC Research Report, 1994.
16
17
18
 
19
X. Li, D. Salyers, and A. Striegel. Improving packet caching scalability through the concept of an explicit end of data marker. In HotWeb, 2006.
 
20
21
 
22
H. Pucha, D. G. Andersen, and M. Kaminsky. Exploiting similarity for multi-source downloads using file handprints. In Proc. 4th USENIX NSDI, Cambridge, MA, Apr. 2007.
 
23
M. Rabin. Fingerprinting by random polynomials. Technical report, Harvard University, 1981. Technical Report, TR-15-81.
 
24
RouteScience Technologies, Inc. Routescience PathControl. http://www.routescience.com/products.
25
26
 
27
Squid Web Proxy Cache. http://www.squid--cache.org/.
28
 
29
 
30
J. Ziv and A. Lempel. A universal algorithm for sequential data compression. Information Theory, IEEE Transactions on, 23(3):337--343, 1977.


Collaborative Colleagues:
Ashok Anand: colleagues
Chitra Muthukrishnan: colleagues
Aditya Akella: colleagues
Ramachandran Ramjee: colleagues