Tencent Research

Shanghai, China

Tencent Research

Shanghai, China
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Andy Chen, IEEE-CS Vice President, Professional and Educational Activities Board, recognized the Asia Pacific region as one of the key areas for IEEE-CS's worldwide expansion. "China is leading the way in product innovation and technology adoptions by the consumers," said Chen. "Tencent, with its focus on advanced technology researches and product innovation, has revolutionized the way consumers interact with each other in their daily life. IEEE-CS is very pleased to collaborate with such a visionary organization." "Tencent has long been committed to building strong partnerships and relationships between Tencent Research and Development and the academic community. Now, we are excited to take the further step, and integrate the international academic community by closely collaborating with IEEE," said Juliet Wang, Vice President of Tencent. "Tencent and IEEE have common objectives focusing on international activities and the establishment of a strategic partnership in areas of artificial intelligence, technical standards, talent cultivation, and other relevant matters. We are looking forward to building a long-term, stable, and comprehensive friendly peer-to-peer relationship with IEEE." Based on mutual communication, both IEEE-CS and Tencent may determine additional activities of common interest for collaboration. About IEEE CS IEEE Computer Society, the computing industry's unmatched source for technology information and career development, offers a comprehensive array of industry-recognized products, services, and professional opportunities. Known as the community for technology leaders, IEEE Computer Society's vast resources include membership, publications, a renowned digital library, training programs, conferences, and top-trending technology events. Visit www.computer.org for more information on all products and services. About Tencent Technology Tencent is a leading provider of Internet value-added services in China, meeting the various needs of Internet users including communication, information, entertainment, financial services, and others. Since its establishment, Tencent has maintained steady growth under its user-oriented operating strategies. On June 16, 2004, Tencent Holdings Limited (SEHK 700) went public on the main board of the Hong Kong Stock Exchange. For more information, visit www.tencent.com/en-us/index.html. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/tencent-technology-co-ltd-reaches-mou-with-ieee-computer-society-and-becomes-a-global-partner-300463191.html

Huang Y.,Tencent Research | Li Z.,Peking University | Liu G.,Tencent Research | Dai Y.,Peking University
MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops | Year: 2011

Video content distribution dominates the Internet traffic. The state-of-the-art techniques generally work well in distributing popular videos, but do not provide satisfactory content distribution service for unpopular videos due to low data health or low data transfer rate. In recent years, the worldwide deployment of cloud utilities provides us with a novel perspective to consider the above problem. We propose and implement the cloud download scheme, which achieves high-quality video content distribution by using cloud utilities to guarantee the data health and enhance the data transfer rate. Specifically, a user sends his video request to the cloud which subsequently downloads the video from the Internet and stores it in the cloud cache. Then the user can usually retrieve his requested video (whether popular or unpopular) from the cloud with high data rate in any place at any time, via the intra-cloud data transfer acceleration. Running logs of our real deployed commercial system (named VideoCloud) confirm the effectiveness and efficiency of cloud download. The users' average data transfer rate of unpopular videos exceeds 1.6 Mbps, and over 80% of the data transfer rates are more than 300 Kbps which is the basic playback rate of online videos. Our study provides practical experiences and valuable heuristics for making use of cloud utilities to achieve efficient Internet services. © 2011 ACM.

Li Z.,Peking University | Huang Y.,Tencent Research | Liu G.,Tencent Research | Wang F.,Tencent Research | And 2 more authors.
Proceedings of the International Workshop on Network and Operating System Support for Digital Audio and Video | Year: 2012

Despite the increasing popularity, Internet video streaming to mobile devices is still challenging. In particular, there has been a format and resolution "gap" between Internet videos and mobile devices, so mobile users have high demand on video transcoding to facilitate their specific devices. However, as a computation-intensive work, video transcoding is greatly challenged by the limited battery capacity of mobile devices. In this paper we propose and implement "Cloud Transcoder", which utilizes an intermediate cloud platform to bridge the "gap" via its special and practical designs. Specifically, Cloud Transcoder only requires the user to upload a video request rather than the video content. After getting the video request, Cloud Transcoder downloads the original video from the Internet, transcodes it on the user's demand, and transfers the transcoded video back to the user with a high data rate via the intra-cloud data transfer acceleration. Therefore, the mobile device only consumes energy in the last step - fast retrieving the transcoded video from the cloud. Running logs of our real-deployed system confirm the efficacy of Cloud Transcoder. © 2012 ACM.

Xu Y.,Beihang University | Luan Z.,Beihang University | Cheng Z.,Beihang University | Qian D.,Beihang University | And 2 more authors.
Proceedings - 2012 IEEE International Conference on Cluster Computing Workshops, Cluster Workshops 2012 | Year: 2012

Power reducing in clusters has become increasingly important over the past few years. People have tried hard to reduce the power consumption of clusters. However, managing the power is more important than reducing the power. In this paper, we add power consumption to the list of managed resources and help developers to understand and control power profile of their clusters. MapReduce is an efficient and popular programming model for data-intensive computing, so we focus on designing green power management for MapReduce workloads. We designed these strategies to make every node in clusters run under a local power budget, and the whole cluster under a global power budget. We modified the data placement policies in HDFS, designed dynamic replica placement policies, and examined different workloads to learn power consumption models. In addition, we also right sizing the clusters according to the power budget. As our predictive power model focuses on the variation of the power, we can predict when users should take measures to reduce power usage. We also present implementation and experiments in this paper. © 2012 IEEE.

Yang H.,Beihang University | Luan Z.,Beihang University | Li W.,Beihang University | Qian D.,Beihang University | Guan G.,Tencent Research
Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012 | Year: 2012

Large-scale data-intensive computing with MapReduce framework in Cloud is becoming pervasive for the core business of many academic, government, and industrial organizations. Hadoop is by far the most successful realization of MapReduce framework. While MapReduce is easy-to-use, efficient and reliable for data-intensive computations, the excessive configuration parameters in Hadoop cause unexpected challenges when running various workloads with Hadoop cluster effectively. Consequently, developers who have less experience with the Hadoop configuration system may devote a significant effort to write an application with poor performance, because they have no idea how these configurations would influence the performance, or they are not even aware that these configurations exist. In this paper, we propose a statistic analysis approach to identify the relationships among workload characteristics, Hadoop configurations and workload performance. Several non-intuitive relationships between workload characteristics and relative performance are revealed and the experimental results demonstrate that our regression models accurately predict the performance of MapReduce workloads under different Hadoop configurations. © 2012 IEEE.

Cheng Z.,Beihang University | Luan Z.,Beihang University | Meng Y.,Beihang University | Xu Y.,Beihang University | And 4 more authors.
Proceedings - 2012 IEEE International Conference on Cluster Computing Workshops, Cluster Workshops 2012 | Year: 2012

The Hadoop Distributed File System (HDFS) is a distributed storage system that stores large-scale data sets reliably and streams those data sets to applications at high bandwidth. HDFS provides high performance, reliability and availability by replicating data, typically three copies of every data. The data in HDFS changes in popularity over time. To get better performance and higher disk utilization, the replication policy of HDFS should be elastic and adapt to data popularity. In this paper, we describe ERMS, an elastic replication management system for HDFS. ERMS provides an active/standby storage model for HDFS. It utilizes a complex event processing engine to distinguish real-time data types, and then dynamically increases extra replicas for hot data, cleans up these extra replicas when the data cool down, and uses erasure codes for cold data. ERMS also introduces a replica placement strategy for the extra replicas of hot data and erasure coding parities. The experiments show that ERMS effectively improves the reliability and performance of HDFS and reduce storage overhead. © 2012 IEEE.

He R.,Beihang University | Luan Z.,Beihang University | Huang Y.,Beihang University | Cheng Z.,Beihang University | And 2 more authors.
Proceedings of the 2012 15th International Conference on Network-Based Information Systems, NBIS 2012 | Year: 2012

With the popularity of cloud computing, high volume data needs to be processed in real-time. Therefore, many distributed stream processing systems have risen to deal with this requirement. One kind of distributed stream processing applications contains several PEs which are connected together to implement the application logic. As data is in the form of streams and it needs to be processed in real-time, high availability is important for the application. At the same time, the application requires a high performance, including a low resource cost. According to these features of the application, we propose a new replica placement method to guarantee the application's availability while controlling the resource cost under some constraint and trying to get the maximal resource usage. The result of our experiment shows that our placement method can acquire high availability and low resource cost comparing with other placement strategies. © 2012 IEEE.

Yi Z.,Beijing Institute of Technology | Dan H.,Network Security Technology | Yansong Y.,Beijing Institute of Technology | Yan H.,Tencent Research | Changjia C.,Beijing Jiaotong University
IET Conference Publications | Year: 2014

Due to the inefficient resource adjustment, the current P2P file sharing systems cannot achieve the balanced relationship between supply and demand over the resources. Especially after a popular content releasing, a burst of downloaders often can't find sufficient uploaders and their request may starve the upload capacity of server. Therefore the overall system QoS may be degraded. To tackle such issue, this paper proposes a download rate accelerate mechanism, called motivate mechanism. With it, the system can quickly find out the files becoming insufficient by monitoring the operating status of the files hourly. Then it promptly increases the number of copies of those files by using free rider nodes so that the whole system QoS is maintained and the system performance is improved. The experiment results on the practical operating system of Tencent demonstrated that the proposed mechanism increases the download rate, saves the traffic on the server and optimizes the system performance.

Zhang T.,CAS Institute of Computing Technology | Li Z.,University of Science and Technology of China | Shen H.,CAS Institute of Computing Technology | Huang Y.,Tencent Research | Cheng X.,CAS Institute of Computing Technology
Proceedings - International Conference on Computer Communications and Networks, ICCCN | Year: 2011

P2P-VoD systems have gained tremendous popularity in recent years. While existing research is mostly based on theoretical or conventional assumptions, it is particularly valuable to understand and examine how these assumptions work in realistic environments, so as to set up a solid foundation for mechanism design and optimization possibilities. In this paper, we present a comprehensive measurement study of CoolFish, a real-world P2P-VoD system. Our measurement provides several new findings which are different from the traditional assumptions or observations: the access pattern does not match Poisson distribution; session time does not have positive correlation with movie popularity; jump frequency does not have a negative correlation with movie popularity as assumed in previous studies. We analyze the reasons for these results and provide suggestions for the further study of P2P-VoD services. © 2011 IEEE.

Zhou Y.,Chinese University of Hong Kong | Fu T.Z.J.,Chinese University of Hong Kong | Chiu D.M.,Chinese University of Hong Kong | Huang Y.,Tencent Research
IEEE Transactions on Multimedia | Year: 2013

Video content downloading using the P2P approach is scalable, but does not always give good performance. Recently, subscription-based premium services have emerged, referred to as cloud downloading. In this service, the cloud storage and server caches user-interested content and updates the cache based on user downloading requests. If a requested video is not in the cache, the request is held in a waiting state until the cache is updated. We call this design server mode. An alternative design is to let the cloud server serve all downloading requests as soon as they arrive, behaving as a helper peer. We call this design helper mode. Our model and analysis show that both these designs are useful for certain operating regimes. The helper mode is good at handling a high request rate, while the server mode is good at scaling with video population size. We design an adaptive algorithm (AMS) to select the service mode automatically. Intuitively, AMS switches service mode from server mode to helper mode when too many peers request blocked movies, and vice versa. The ability of AMS to achieve good performance in different operating regimes is validated by simulation. © 1999-2012 IEEE.

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