vSkyConf: Cloud-assisted Multi-party Mobile Video Conferencing

As an important application in the busy world today, mobile video conferencing facilitates virtual face-to-face communication with friends, families and colleagues, via their mobile devices on the move. However, how to provision high-quality, multi-party video conferencing experiences over mobile devices is still an open challenge. The fundamental reason behind is the lack of computation and communication capacities on the mobile devices, to scale to large conferencing sessions. In this paper, we present vSkyConf, a cloud-assisted mobile video conferencing system to fundamentally improve the quality and scale of multi-party mobile video conferencing. By novelly employing a surrogate virtual machine in the cloud for each mobile user, we allow fully scalable communication among the conference participants via their surrogates, rather than directly. The surrogates exchange conferencing streams among each other, transcode the streams to the most appropriate bit rates, and buffer the streams for the most efficient delivery to the mobile recipients. A fully decentralized, optimal algorithm is designed to decide the best paths of streams and the most suitable surrogates for video transcoding along the paths, such that the limited bandwidth is fully utilized to deliver streams of the highest possible quality to the mobile recipients. We also carefully tailor a buffering mechanism on each surrogate to cooperate with optimal stream distribution. We have implemented vSkyConf based on Amazon EC2 and verified the excellent performance of our design, as compared to the widely adopted unicast solutions.Comment: 10 page

Online Influence Maximization in Non-Stationary Social Networks

Social networks have been popular platforms for information propagation. An important use case is viral marketing: given a promotion budget, an advertiser can choose some influential users as the seed set and provide them free or discounted sample products; in this way, the advertiser hopes to increase the popularity of the product in the users' friend circles by the world-of-mouth effect, and thus maximizes the number of users that information of the production can reach. There has been a body of literature studying the influence maximization problem. Nevertheless, the existing studies mostly investigate the problem on a one-off basis, assuming fixed known influence probabilities among users, or the knowledge of the exact social network topology. In practice, the social network topology and the influence probabilities are typically unknown to the advertiser, which can be varying over time, i.e., in cases of newly established, strengthened or weakened social ties. In this paper, we focus on a dynamic non-stationary social network and design a randomized algorithm, RSB, based on multi-armed bandit optimization, to maximize influence propagation over time. The algorithm produces a sequence of online decisions and calibrates its explore-exploit strategy utilizing outcomes of previous decisions. It is rigorously proven to achieve an upper-bounded regret in reward and applicable to large-scale social networks. Practical effectiveness of the algorithm is evaluated using both synthetic and real-world datasets, which demonstrates that our algorithm outperforms previous stationary methods under non-stationary conditions.Comment: 10 pages. To appear in IEEE/ACM IWQoS 2016. Full versio

Efficient computations on meshes with express links

At head of title: Computer science publication.Cover title.Includes bibliographical references (p. 10-12).published_or_final_versio