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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Dispersing Instant Social Video Service Across Multiple Clouds
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Dispersing Instant Social Video Service Across Multiple Clouds

机译：在多个云中分散即时社交视频服务

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摘要

Instant social video sharing which combines the online social network and user-generated short video streaming services, has become popular in today’s Internet. Cloud-based hosting of such instant social video contents has become a norm to serve the increasing users with user-generated contents. A fundamental problem of cloud-based social video sharing service is that users are located globally, who cannot be served with good service quality with a single cloud provider. In this paper, we investigate the feasibility of dispersing instant social video contents to multiple cloud providers. The challenge is that inter-cloud social is indispensable with such multi-cloud social video hosting, yet such inter-cloud traffic incurs substantial operational cost. We analyze and formulate the multi-cloud hosting of an instant social video system as an optimization problem. We conduct large-scale measurement studies to show the characteristics of instant social video deployment, and demonstrate the trade-off between satisfying users with their ideal cloud providers, and reducing the inter-cloud data propagation. Our measurement insights of the social propagation allow us to propose a heuristic algorithm with acceptable complexity to solve the optimization problem, by partitioning a propagation-weighted social graph in two phases: a preference-aware initial cloud provider selection and a propagation-aware re-hosting. Our simulation experiments driven by real-world social network traces show the superiority of our design.
机译：结合了在线社交网络和用户生成的短视频流服务的即时社交视频共享已在当今的Internet中流行。这种即时社交视频内容的基于云的托管已经成为为越来越多的用户提供用户生成的内容的规范。基于云的社交视频共享服务的一个基本问题是用户遍布全球，而单个云提供商无法为他们提供优质的服务。在本文中，我们研究了将即时社交视频内容分散到多个云提供商的可行性。挑战在于，云间社交对于此类多云社交视频托管是必不可少的，但是这种云间流量却招致了巨大的运营成本。我们将即时社交视频系统的多云托管分析并制定为优化问题。我们进行了大规模的测量研究，以显示即时社交视频部署的特征，并展示了让满意的用户与理想的云提供商合作以及减少云间数据传播之间的权衡。我们对社交传播的测量洞察力使我们可以通过将传播加权的社交图划分为两个阶段来提出具有可接受复杂度的启发式算法，以解决优化问题：偏好感知的初始云提供商选择和传播感知的重新感知托管。我们在现实世界中的社交网络轨迹驱动下的仿真实验证明了我们设计的优越性。

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