Measurement and Analysis of a Streaming Media Workload
By Maureen Chesire, Alec Wolman, Geoffrey M. Voelker, Henry M. Levy. USENIX Symposium on Internet Technologies and Systems, 2001
310 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/8e3c8026e8ecf0899ad9b3273a96eb277e72fa3e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Gábor Szabó
- Kunwadee Sripanidkulchai
- Hui Zhang
- Balachander Krishnamurthy
- Craig E. Wills
- Bernardo A. Huberman
- J. Kurose
- Bruce MacDowell Maggs
Citations in the corpus
- On the use and performance of content distribution networks (2001, methodology): “Our approach differs from [13] by looking only for CDN-served objects versus such objects served by all servers.”
- Predicting the popularity of online content (2008, background): “Video characteristics and user access frequencies are studied together when streaming media workload is estimated [11, 7 , 13, 24].”
- The LCD interconnection of LRU caches and its analysis (2006, background): “The Zipf-like model is recognized as a good model for characterizing the popularity of various types of measured workloads, such as web objects [11] and multimedia clips [12].”
- Characteristics of YouTube network traffic at a campus network - Measurements, models, and implications (2009, methodology): “[9] apply a method very similar to our approach to measure streaming media workload in their university network.”
- Support vector machines for collaborative filtering (2006, methodology): “According to workload analysis of HPLabs Media server [7], where 79% of video files belong to a long video group (longer than 30 minutes), 77%-79% of media sessions last less than 10 minute long.”
- Understanding user behavior in large-scale video-on-demand systems (2006, methodology): “While most of the above works used media server logs, Mena [17], Chesire [8] and Guo [14] used different traffic tracing methods to trace the user accessing data in Internet streaming systems.”
- Network performance evaluation using frame size and quality traces of single-layer and two-layer video: A tutorial (2004, methodology): “For example, studies of streaming media servers, for example, [ 52 ], indicate that video popularity follows a Zipf distribution [53].”
- An analysis of live streaming workloads on the internet (2004, background): “More interesting is that small streams also have a diverse audience.”