TY - GEN
T1 - Wide-area streaming analytics
T2 - 4th Annual Symposium on Cloud Computing, SoCC 2013
AU - Heintz, Benjamin
AU - Chandra, Abhishek
AU - Sitaraman, Ramesh K.
PY - 2013
Y1 - 2013
N2 - To date, much research in data-intensive computing has focused on batch computation. Increasingly, however, it is necessary to derive knowledge from big data streams. As a motivating example, consider a content delivery network (CDN) such as Akamai [4], comprising thousands of servers in hundreds of globally distributed locations. Each of these servers produces a stream of log data, recording for example every user it serves, along with each video stream they access, when they play and pause streams, and more. Each server also records network- and system-level data such as TCP connection statistics. In aggregate, the servers produce billions of lines of log data from over a thousand locations daily.
AB - To date, much research in data-intensive computing has focused on batch computation. Increasingly, however, it is necessary to derive knowledge from big data streams. As a motivating example, consider a content delivery network (CDN) such as Akamai [4], comprising thousands of servers in hundreds of globally distributed locations. Each of these servers produces a stream of log data, recording for example every user it serves, along with each video stream they access, when they play and pause streams, and more. Each server also records network- and system-level data such as TCP connection statistics. In aggregate, the servers produce billions of lines of log data from over a thousand locations daily.
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U2 - 10.1145/2523616.2525963
DO - 10.1145/2523616.2525963
M3 - Conference contribution
AN - SCOPUS:84893273833
SN - 9781450324281
T3 - Proceedings of the 4th Annual Symposium on Cloud Computing, SoCC 2013
BT - Proceedings of the 4th Annual Symposium on Cloud Computing, SoCC 2013
PB - Association for Computing Machinery
Y2 - 1 October 2013 through 3 October 2013
ER -