Understanding the characteristics and predicting the popularity of the newly published online videos can provide direct implications in various contexts such as service design, advertisement planning, network manageme...Understanding the characteristics and predicting the popularity of the newly published online videos can provide direct implications in various contexts such as service design, advertisement planning, network management and etc. In this paper, we collect a real-world large-scale dataset from a leading online video service provider in China, namely Youku. We first analyze the dynamics of content publication and content popularity for the online video service. Then, we propose a rich set of features and exploit various effective classification methods to estimate the future popularity level of an individual video in various scenarios. We show that the future popularity level of a video can be predicted even before the video's release, and by introducing the historical popularity information the prediction performance can be improved dramatically. In addition, we investigate the importance of each feature group and each feature in the popularity prediction, and further reveal the factors that may impact the video popularity. We also discuss how the early monitoring period influences the popularity level prediction. Our work provides an insight into the popularity of the newly published online videos, and demonstrates promising practical applications for content publishers,service providers, online advisers and network operators.展开更多
Given the large volume of video content and the diversity of user attention, it is of great importance to understand the characteristics of online video popularity for technological, economic and social reasons. In th...Given the large volume of video content and the diversity of user attention, it is of great importance to understand the characteristics of online video popularity for technological, economic and social reasons. In this paper, based on the data collected from a leading online video service provider in China, namely Youku, the dynamics of online video popularity are analyzed in-depth from four key aspects： overall popularity distribution, individual popularity distribution, popularity evolution pattern and early-future popularity relationship. How the popularity of a set of newly upload videos distributes throughout the observation period is first studied. Then the notion popularity distributions of individual videos are carefully studied. of active days is proposed, and the per-day and per-hour Next, how the popularity of an individual video evolves over time is investigated. The evolution patterns are further defined according to the number and temporal locations of popularity bursts, in order to describe the popularity growth trend. At last, the linear relationship between early video popularity and future video popularity are examined on a log-log scale. The relationship is found to be largely impacted by the popularity evolution patterns. Therefore, the specialized models are proposed to describe the correlation according to the popularity evolution patterns. Experiment results show that specialized models can better fit the correlation than a general model. Above all, the analysis results in our work can provide direct help in practical for the interested parties of online video service such as service providers, online advisers, and network operators.展开更多
Video surveillance applications need video data center to provide elastic virtual machine （VM） provisioning. However, the workloads of the VMs are hardly to be predicted for online video surveillance service. The un...Video surveillance applications need video data center to provide elastic virtual machine （VM） provisioning. However, the workloads of the VMs are hardly to be predicted for online video surveillance service. The unknown arrival workloads easily lead to workload skew among VMs. In this paper, we study how to balance the workload skew on online video surveillance system. First, we design the system framework for online surveillance service which con- sists of video capturing and analysis tasks. Second, we propose StreamTune, an online resource scheduling approach for workload balancing, to deal with irregular video analysis workload with the minimum number of VMs. We aim at timely balancing the workload skew on video analyzers without depending on any workload prediction method. Furthermore, we evaluate the performance of the proposed approach using a traffic surveillance application. The experimental results show that our approach is well adaptive to the variation of workload and achieves workload balance with less VMs.展开更多
文摘Understanding the characteristics and predicting the popularity of the newly published online videos can provide direct implications in various contexts such as service design, advertisement planning, network management and etc. In this paper, we collect a real-world large-scale dataset from a leading online video service provider in China, namely Youku. We first analyze the dynamics of content publication and content popularity for the online video service. Then, we propose a rich set of features and exploit various effective classification methods to estimate the future popularity level of an individual video in various scenarios. We show that the future popularity level of a video can be predicted even before the video's release, and by introducing the historical popularity information the prediction performance can be improved dramatically. In addition, we investigate the importance of each feature group and each feature in the popularity prediction, and further reveal the factors that may impact the video popularity. We also discuss how the early monitoring period influences the popularity level prediction. Our work provides an insight into the popularity of the newly published online videos, and demonstrates promising practical applications for content publishers,service providers, online advisers and network operators.
基金supported by the Video Super-Resolution Reconstruction Project (20130005110017)
文摘Given the large volume of video content and the diversity of user attention, it is of great importance to understand the characteristics of online video popularity for technological, economic and social reasons. In this paper, based on the data collected from a leading online video service provider in China, namely Youku, the dynamics of online video popularity are analyzed in-depth from four key aspects： overall popularity distribution, individual popularity distribution, popularity evolution pattern and early-future popularity relationship. How the popularity of a set of newly upload videos distributes throughout the observation period is first studied. Then the notion popularity distributions of individual videos are carefully studied. of active days is proposed, and the per-day and per-hour Next, how the popularity of an individual video evolves over time is investigated. The evolution patterns are further defined according to the number and temporal locations of popularity bursts, in order to describe the popularity growth trend. At last, the linear relationship between early video popularity and future video popularity are examined on a log-log scale. The relationship is found to be largely impacted by the popularity evolution patterns. Therefore, the specialized models are proposed to describe the correlation according to the popularity evolution patterns. Experiment results show that specialized models can better fit the correlation than a general model. Above all, the analysis results in our work can provide direct help in practical for the interested parties of online video service such as service providers, online advisers, and network operators.
文摘Video surveillance applications need video data center to provide elastic virtual machine （VM） provisioning. However, the workloads of the VMs are hardly to be predicted for online video surveillance service. The unknown arrival workloads easily lead to workload skew among VMs. In this paper, we study how to balance the workload skew on online video surveillance system. First, we design the system framework for online surveillance service which con- sists of video capturing and analysis tasks. Second, we propose StreamTune, an online resource scheduling approach for workload balancing, to deal with irregular video analysis workload with the minimum number of VMs. We aim at timely balancing the workload skew on video analyzers without depending on any workload prediction method. Furthermore, we evaluate the performance of the proposed approach using a traffic surveillance application. The experimental results show that our approach is well adaptive to the variation of workload and achieves workload balance with less VMs.
