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Content Provider | IEEE Xplore Digital Library |
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Author | Romoser, B. Fares, R. Janovics, P. Xiaojun Ruan Xiao Qin Ziliang Zong |
Copyright Year | 2012 |
Description | Author affiliation: Computer Science Department, Texas State University (Romoser, B.; Fares, R.; Janovics, P.; Ziliang Zong) || Department of Computer Science and Software Engineering, Auburn University (Xiao Qin) || Department of Computer Science, West Chester University of Pennsylvania (Xiaojun Ruan) |
Abstract | Online content distribution systems, which store incredibly large amounts of information and provide service to large numbers of users, are becoming increasingly commonplace. To fulfill the wide range of requests sent by different users, these systems must ensure efficient handling of massive amount of data. To achieve this goal, the in-depth analysis and comprehensive understanding of user behaviors are critical. However, analyzing the behaviors of worldwide users with different needs is a very challenging task. This is especially true when historical user behaviors evolve over time or may be affected by unpredictable events. In this paper, we present a number of workload characterization techniques applied to one of the world's largest online satellite image distribution systems operated by the U.S. Geological Survey (USGS) and NASA. |
Starting Page | 368 |
Ending Page | 375 |
File Size | 707082 |
Page Count | 8 |
File Format | |
ISBN | 9781467348812 |
ISSN | 10972641 |
e-ISBN | 9781467348836 |
DOI | 10.1109/PCCC.2012.6407779 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-12-01 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Satellites Earth Remote sensing Prefetching Google Educational institutions NASA geovisualization clustering content distribution system geospatial data |
Content Type | Text |
Resource Type | Article |
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