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The nature of data center traffic: measurements & analysis
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Internet Measurement Conference archive
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference table of contents
Chicago, Illinois, USA
SESSION: Content distribution and mobility table of contents
Pages: 202-208  
Year of Publication: 2009
ISBN:978-1-60558-771-4
Authors
Srikanth Kandula  Microsoft Research, Redmond, WA, USA
Sudipta Sengupta  Microsoft Research, Redmond, WA, USA
Albert Greenberg  Microsoft Research, Redmond, WA, USA
Parveen Patel  Microsoft Research, Redmond, WA, USA
Ronnie Chaiken  Microsoft, Redmond, WA, USA
Sponsor
SIGCOMM: ACM Special Interest Group on Data Communication
Publisher
ACM  New York, NY, USA
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ABSTRACT

We explore the nature of traffic in data centers, designed to support the mining of massive data sets. We instrument the servers to collect socket-level logs, with negligible performance impact. In a 1500 server operational cluster, we thus amass roughly a petabyte of measurements over two months, from which we obtain and report detailed views of traffic and congestion conditions and patterns. We further consider whether traffic matrices in the cluster might be obtained instead via tomographic inference from coarser-grained counter data.


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
AmazonWeb Services. http://aws.amazon.com.
 
2
EventTracing forWindows. http://msdn.microso.com/en-us/library/ms751538.aspx.
 
3
Google app engine. http://code.google.com/appengine/.
 
4
Hadoop distributed filesystem. http://hadoop.apache.org.
 
5
Windows Azure. http://www.microso.com/azure/.
 
6
7
 
8
 
9
10
 
11
Cisco Guard DDoS Mitigation Appliance. http://www.cisco.com/en/US/products/ps5888/.
 
12
Cisco Nexus 7000 Series Switches. http://www.cisco.com/en/US/products/ps9402/.
13
 
14
15
16
17
18
19
20
 
21
L. Huang, X. Nguyen, M. Garofalakis, J. Hellerstein, M. Jordan, M. Joseph, and N. Taft. Communication-Efficient Online Detection of Network-Wide Anomalies. In INFOCOM, 2007.
 
22
IETF Working Group IP Flow Information Export (ipfix). http://www.ietf.org/html.charters/ipfix-charter.html.
23
24
 
25
M. Kodialam, T. V. Lakshman, and S. Sengupta. Efficient and Robust Routing of Highly Variable Traffic. In HotNets, 2004.
 
26
27
 
28
 
29
IETF Packet Sampling (ActiveWG). http://tools.ietf.org/wg/psamp/.
 
30
S. Kandula and D. Katabi and S. Sinha and A. Berger. Dynamic Load Balancing Without Packet Reordering. In CCR, 2006.
 
31
sFlow.org. Making the network visible. http://www.sflow.org.
32
33
 
34
35
 
36
R. Zhang-Shen and N. McKeown. Designing a Predictable Internet Backbone Network. In HotNets, 2004.

Collaborative Colleagues:
Srikanth Kandula: colleagues
Sudipta Sengupta: colleagues
Albert Greenberg: colleagues
Parveen Patel: colleagues
Ronnie Chaiken: colleagues