The Experts below are selected from a list of 153 Experts worldwide ranked by ideXlab platform
Shiqiang Yang - One of the best experts on this subject based on the ideXlab platform.
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Enhancing Internet-Scale Video Service Deployment Using Microblog-Based Prediction
IEEE Transactions on Parallel and Distributed Systems, 2015Co-Authors: Zhi Wang, Lifeng Sun, Shiqiang YangAbstract:Online microblogging has been very popular in today’s Internet, where users follow other people they are interested in and exchange information between themselves. Among these exchanges, video links are a representative type on a microblogging Site. The impact is fundamental—not only are viewers in a video service directly coming from the microblog sharing and recommendation, but also are the users in the microblogging Site representing a promising sample to all the viewers. It is intriguing to study a proactive service deployment for such videos, using the propagation patterns of microblogs. Based on extensive traces from Youku and Tencent Weibo, a popular video sharing Site and a favored microblogging system, we explore how video propagation patterns in the microblogging system are correlated with video popularity on the video sharing Site. Using influential factors summarized from the measurement studies, we further design a neural network-based learning framework to predict the number of potential viewers and their geographic distribution. We then design proactive video deployment algorithms based on the prediction framework, which not only determines the upload capacities of servers in different regions, but also strategically replicates videos to these regions to serve users. Our PlanetLab-based experiments verify the effectiveness of our design.
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INFOCOM - Guiding internet-scale video service deployment using microblog-based prediction
2012 Proceedings IEEE INFOCOM, 2012Co-Authors: Zhi Wang, Lifeng Sun, Shiqiang YangAbstract:Online microblogging has been very popular in today's Internet, where users exchange short messages and follow various contents shared by people that they are interested in. Among the variety of exchanges, video links are a representative type on a microblogging Site. More and more viewers of an Internet video service are coming from microblog recommendations. It is intriguing research to explore the connections between the patterns of microblog exchanges and the popularity of videos, in order to potentially use the propagation patterns of microblogs to guide proactive service deployment of a video sharing system. Based on extensive traces from Youku and Tencent Weibo, a popular video sharing Site and a favored microblogging system in China, we explore how patterns of video link propagation in the microblogging system are correlated with video popularity on the video sharing Site, at different times and in different geographic regions. Using influential factors summarized from the measurement studies, we further design neural network-based learning frameworks to predict the number of potential viewers of different videos and the geographic distribution of viewers. Experiments show that our neural network-based frameworks achieve better prediction accuracy, as compared to a classical approach that relies on historical numbers of views. We also briefly discuss how proactive video service deployment can be effectively enabled by our prediction frameworks.
Zhi Wang - One of the best experts on this subject based on the ideXlab platform.
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Enhancing Internet-Scale Video Service Deployment Using Microblog-Based Prediction
IEEE Transactions on Parallel and Distributed Systems, 2015Co-Authors: Zhi Wang, Lifeng Sun, Shiqiang YangAbstract:Online microblogging has been very popular in today’s Internet, where users follow other people they are interested in and exchange information between themselves. Among these exchanges, video links are a representative type on a microblogging Site. The impact is fundamental—not only are viewers in a video service directly coming from the microblog sharing and recommendation, but also are the users in the microblogging Site representing a promising sample to all the viewers. It is intriguing to study a proactive service deployment for such videos, using the propagation patterns of microblogs. Based on extensive traces from Youku and Tencent Weibo, a popular video sharing Site and a favored microblogging system, we explore how video propagation patterns in the microblogging system are correlated with video popularity on the video sharing Site. Using influential factors summarized from the measurement studies, we further design a neural network-based learning framework to predict the number of potential viewers and their geographic distribution. We then design proactive video deployment algorithms based on the prediction framework, which not only determines the upload capacities of servers in different regions, but also strategically replicates videos to these regions to serve users. Our PlanetLab-based experiments verify the effectiveness of our design.
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INFOCOM - Guiding internet-scale video service deployment using microblog-based prediction
2012 Proceedings IEEE INFOCOM, 2012Co-Authors: Zhi Wang, Lifeng Sun, Shiqiang YangAbstract:Online microblogging has been very popular in today's Internet, where users exchange short messages and follow various contents shared by people that they are interested in. Among the variety of exchanges, video links are a representative type on a microblogging Site. More and more viewers of an Internet video service are coming from microblog recommendations. It is intriguing research to explore the connections between the patterns of microblog exchanges and the popularity of videos, in order to potentially use the propagation patterns of microblogs to guide proactive service deployment of a video sharing system. Based on extensive traces from Youku and Tencent Weibo, a popular video sharing Site and a favored microblogging system in China, we explore how patterns of video link propagation in the microblogging system are correlated with video popularity on the video sharing Site, at different times and in different geographic regions. Using influential factors summarized from the measurement studies, we further design neural network-based learning frameworks to predict the number of potential viewers of different videos and the geographic distribution of viewers. Experiments show that our neural network-based frameworks achieve better prediction accuracy, as compared to a classical approach that relies on historical numbers of views. We also briefly discuss how proactive video service deployment can be effectively enabled by our prediction frameworks.
Kenji Yoshihira - One of the best experts on this subject based on the ideXlab platform.
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Understanding Internet Video sharing Site workload: A view from data center design
Journal of Visual Communication and Image Representation, 2010Co-Authors: Xiaozhu Kang, Hui Zhang, Guofei Jiang, Haifeng Chen, Xiaoqiao Meng, Kenji YoshihiraAbstract:Internet Video sharing Sites, led by YouTube , have been gaining popularity in a dazzling speed, which also brings massive workload to their service data centers. In this paper we analyze Yahoo! Video, the 2nd largest U.S. video sharing Site, to understand the nature of such unprecedented massive workload as well as its impact on online video data center design. We crawled the Yahoo! Video web Site for 46days. The measurement data allows us to understand the workload characteristics at different time scales (minutes, hours, days, weeks), and we discover interesting statistical properties on both static and temporal dimensions of the workload including file duration and popularity distributions, arrival rate dynamics and predictability, and workload stationarity and burstiness. Complemented with queueing-theoretic techniques, we further extend our understanding on the measurement data with a virtual design on the workload and capacity management components of a data center assuming the same workload as measured, which reveals key results regarding the impact of workload arrival distribution, Service Level Agreements (SLAs), and workload scheduling schemes on the design and operations of such large-scale video distribution systems.
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WWW - Understanding internet video sharing Site workload: a view from data center design
Proceeding of the 17th international conference on World Wide Web - WWW '08, 2008Co-Authors: Xiaozhu Kang, Hui Zhang, Guofei Jiang, Haifeng Chen, Xiaoqiao Meng, Kenji YoshihiraAbstract:In this paper we measured and analyzed the workload on Yahoo! Video, the 2nd largest U.S. video sharing Site, to understand its nature and the impact on online video data center design. We discovered interesting statistical properties on both static and temporal dimensions of the workload including file duration and popularity distributions, arrival rate dynamics and predictability, and workload stationarity and burstiness. Complemented with queueing-theoretic techniques, we further extended our understanding on the measurement data with a virtual design on the workload and capacity management components of a data center assuming the same workload as measured, which reveals key results regarding the impact of Service Level Agreements (SLAs) and workload scheduling schemes on the design and operations of such large-scale video distribution systems.
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ICWS - Measurement, Modeling, and Analysis of Internet Video Sharing Site Workload: A Case Study
2008 IEEE International Conference on Web Services, 2008Co-Authors: Xiaozhu Kang, Hui Zhang, Guofei Jiang, Haifeng Chen, Xiaoqiao Meng, Kenji YoshihiraAbstract:In this paper we measured and analyzed the workload on Yahoo! Video, the 2nd largest U.S. video sharing Site, to understand its nature and the impact on online video data center design. We discovered interesting statistical properties on both static and temporal dimensions of the workload; they include file duration and popularity distributions, arrival rate dynamics and predictability, and workload stationarity and burstiness. Complemented with queueing-theoretic techniques, we extended our understanding on the measurement data with a virtual data center design assuming the same workload as measured, which reveals results regarding the impact of workload arrival distribution, service level agreements (SLAs) and workload scheduling schemes on the design and operations of such large-scale video distribution systems.
Lifeng Sun - One of the best experts on this subject based on the ideXlab platform.
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Enhancing Internet-Scale Video Service Deployment Using Microblog-Based Prediction
IEEE Transactions on Parallel and Distributed Systems, 2015Co-Authors: Zhi Wang, Lifeng Sun, Shiqiang YangAbstract:Online microblogging has been very popular in today’s Internet, where users follow other people they are interested in and exchange information between themselves. Among these exchanges, video links are a representative type on a microblogging Site. The impact is fundamental—not only are viewers in a video service directly coming from the microblog sharing and recommendation, but also are the users in the microblogging Site representing a promising sample to all the viewers. It is intriguing to study a proactive service deployment for such videos, using the propagation patterns of microblogs. Based on extensive traces from Youku and Tencent Weibo, a popular video sharing Site and a favored microblogging system, we explore how video propagation patterns in the microblogging system are correlated with video popularity on the video sharing Site. Using influential factors summarized from the measurement studies, we further design a neural network-based learning framework to predict the number of potential viewers and their geographic distribution. We then design proactive video deployment algorithms based on the prediction framework, which not only determines the upload capacities of servers in different regions, but also strategically replicates videos to these regions to serve users. Our PlanetLab-based experiments verify the effectiveness of our design.
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INFOCOM - Guiding internet-scale video service deployment using microblog-based prediction
2012 Proceedings IEEE INFOCOM, 2012Co-Authors: Zhi Wang, Lifeng Sun, Shiqiang YangAbstract:Online microblogging has been very popular in today's Internet, where users exchange short messages and follow various contents shared by people that they are interested in. Among the variety of exchanges, video links are a representative type on a microblogging Site. More and more viewers of an Internet video service are coming from microblog recommendations. It is intriguing research to explore the connections between the patterns of microblog exchanges and the popularity of videos, in order to potentially use the propagation patterns of microblogs to guide proactive service deployment of a video sharing system. Based on extensive traces from Youku and Tencent Weibo, a popular video sharing Site and a favored microblogging system in China, we explore how patterns of video link propagation in the microblogging system are correlated with video popularity on the video sharing Site, at different times and in different geographic regions. Using influential factors summarized from the measurement studies, we further design neural network-based learning frameworks to predict the number of potential viewers of different videos and the geographic distribution of viewers. Experiments show that our neural network-based frameworks achieve better prediction accuracy, as compared to a classical approach that relies on historical numbers of views. We also briefly discuss how proactive video service deployment can be effectively enabled by our prediction frameworks.
Xiaozhu Kang - One of the best experts on this subject based on the ideXlab platform.
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Understanding Internet Video sharing Site workload: A view from data center design
Journal of Visual Communication and Image Representation, 2010Co-Authors: Xiaozhu Kang, Hui Zhang, Guofei Jiang, Haifeng Chen, Xiaoqiao Meng, Kenji YoshihiraAbstract:Internet Video sharing Sites, led by YouTube , have been gaining popularity in a dazzling speed, which also brings massive workload to their service data centers. In this paper we analyze Yahoo! Video, the 2nd largest U.S. video sharing Site, to understand the nature of such unprecedented massive workload as well as its impact on online video data center design. We crawled the Yahoo! Video web Site for 46days. The measurement data allows us to understand the workload characteristics at different time scales (minutes, hours, days, weeks), and we discover interesting statistical properties on both static and temporal dimensions of the workload including file duration and popularity distributions, arrival rate dynamics and predictability, and workload stationarity and burstiness. Complemented with queueing-theoretic techniques, we further extend our understanding on the measurement data with a virtual design on the workload and capacity management components of a data center assuming the same workload as measured, which reveals key results regarding the impact of workload arrival distribution, Service Level Agreements (SLAs), and workload scheduling schemes on the design and operations of such large-scale video distribution systems.
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WWW - Understanding internet video sharing Site workload: a view from data center design
Proceeding of the 17th international conference on World Wide Web - WWW '08, 2008Co-Authors: Xiaozhu Kang, Hui Zhang, Guofei Jiang, Haifeng Chen, Xiaoqiao Meng, Kenji YoshihiraAbstract:In this paper we measured and analyzed the workload on Yahoo! Video, the 2nd largest U.S. video sharing Site, to understand its nature and the impact on online video data center design. We discovered interesting statistical properties on both static and temporal dimensions of the workload including file duration and popularity distributions, arrival rate dynamics and predictability, and workload stationarity and burstiness. Complemented with queueing-theoretic techniques, we further extended our understanding on the measurement data with a virtual design on the workload and capacity management components of a data center assuming the same workload as measured, which reveals key results regarding the impact of Service Level Agreements (SLAs) and workload scheduling schemes on the design and operations of such large-scale video distribution systems.
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ICWS - Measurement, Modeling, and Analysis of Internet Video Sharing Site Workload: A Case Study
2008 IEEE International Conference on Web Services, 2008Co-Authors: Xiaozhu Kang, Hui Zhang, Guofei Jiang, Haifeng Chen, Xiaoqiao Meng, Kenji YoshihiraAbstract:In this paper we measured and analyzed the workload on Yahoo! Video, the 2nd largest U.S. video sharing Site, to understand its nature and the impact on online video data center design. We discovered interesting statistical properties on both static and temporal dimensions of the workload; they include file duration and popularity distributions, arrival rate dynamics and predictability, and workload stationarity and burstiness. Complemented with queueing-theoretic techniques, we extended our understanding on the measurement data with a virtual data center design assuming the same workload as measured, which reveals results regarding the impact of workload arrival distribution, service level agreements (SLAs) and workload scheduling schemes on the design and operations of such large-scale video distribution systems.
