This paper presents a traffic characterization study of the popular video sharing service, YouTube. Over a three month period we observed almost 25 million transactions between users on an edge network and YouTube, including more than 600,000 video downloads. We also monitored the globally popular videos over this period of time.In the paper we examine usage patterns, file properties, popularity and referencing characteristics, and transfer behaviors of YouTube, and compare them to traditional Web and media streaming workload characteristics. We conclude the paper with a discussion of the implications of the observed characteristics. For example, we find that as with the traditional Web, caching could improve the end user experience, reduce network bandwidth consumption, and reduce the load on YouTube's core server infrastructure. Unlike traditional Web caching, Web 2.0 provides additional metadata that should be exploited to improve the effectiveness of strategies like caching.

This article presents a detailed workload characterization study of the 1998 World Cup W e b site. Measurements from this site were collected over a three-month period. During this time the site received l .35 billion re uests, making this the largest throu h comparison with existing characterization studies, we are able to determ i n e l o w W e b server workloads are evolving. W e find that improvements in the caching architecture of the World Wide W e b are changing the workloads of W e b servers, but major im rovements to that architecture are still necessary. In particular, we uncover evilence that a better consistency mechanism is required for World Wide W e b caches.

Fear of increasing prices and concern about climate change are motivating residential power conservation efforts. We investigate the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes. Specifically, we consider variants of the factorial hidden Markov model. Our results indicate that a conditional factorial hidden semi-Markov model, which integrates additional features related to when and how appliances are used in the home and more accurately represents the power use of individual appliances, outperforms the other unsupervised disaggregation methods. Our results show that unsupervised techniques can provide perappliance power usage information in a non-invasive manner, which is ideal for enabling power conservation efforts.

Classification of network traffic using port-based or payload-based analysis is becoming increasingly difficult with many peer-to-peer (P2P) applications using dynamic port numbers, masquerading techniques, and encryption to avoid detection. An alternative approach is to classify traffic by exploiting the distinctive characteristics of applications when they communicate on a network. We pursue this latter approach and demonstrate how cluster analysis can be used to effectively identify groups of traffic that are similar using only transport layer statistics. Our work considers two unsupervised clustering algorithms, namely K-Means and DBSCAN, that have previously not been used for network traffic classification. We evaluate these two algorithms and compare them to the previously used AutoClass algorithm, using empirical Internet traces. The experimental results show that both K-Means and DBSCAN work very well and much more quickly then AutoClass. Our results indicate that although DBSCAN has lower accuracy compared to K-Means and AutoClass, DBSCAN produces better clusters.

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Abslrucf-Thispaper presents a workload characterization study for Internet Web servers. Six diRerent data sets are used in the study: three From academic environments, two from scientific research organizations, and one from a commercial Internet provider. These data sets represent three different orders of magnitude in server activity, and two different orders of magnitude in time duration, ranging from one week of activity to one year.The workload characterization focuses on the document type distribution, the document size distribution, the document referencing behavior, and the geographic distribution of server requests. Throughout the study, emphasis is placed on tinding workload characteristics that are common to all the data sets studied. Ten such characteristics are identifted. The paper concludes with a discussion of caching and performance issues, using the observed workload characteristics to suggest performance enhancements that seem promising for Internet Web servers.

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