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A latency-based object placement approach in content distribution networks

机译:内容分发网络中基于延迟的对象放置方法

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Content distribution networks (CDNs) are increasingly being used to disseminate data in today's Internet. The growing interest in CDNs is motivated by a common problem across disciplines: how does one reduce the load on the origin server and the traffic on the Internet, and ultimately improve response time to users? In this direction, crucial data management issues should be addressed. A very important issue is the optimal placement of the outsourced content to CDN's servers. Taking into account that this problem is NP complete, a heuristic method should be developed. All the approaches developed so far assume the existence of adequate popularity statistics. Such information though, is not always available, or it is extremely volatile, turning such methods problematic. This paper develops a network-adaptive, non-parameterized technique to place the outsourced content to CDN's servers, which requires no a-priori knowledge of request statistics. We place the outsourced objects to these servers with respect to the network latency that each object produces. Through a detailed simulation environment, using both real and synthetic data, we show that the proposed technique can yield up to 25% reduction in user-perceived latency, compared with other heuristic schemes which have knowledge of the content popularity.
机译:内容分发网络(CDN)越来越多地用于在当今的Internet中分发数据。人们对CDN的兴趣日益增长,这是由跨学科的一个共同问题引起的:如何减少源服务器的负载和Internet上的流量,并最终改善对用户的响应时间?在这个方向上,关键的数据管理问题应得到解决。一个非常重要的问题是外包内容到CDN服务器的最佳放置。考虑到该问题是NP完全的,应开发一种启发式方法。到目前为止开发的所有方法都假定存在足够的普及统计数据。但是,此类信息并非总是可用,或者它非常易变,从而使此类方法成为问题。本文开发了一种网络自适应的非参数化技术,将外包内容放置到CDN的服务器上,不需要先验知识的请求统计信息。相对于每个对象产生的网络延迟,我们将外包对象放置到这些服务器上。通过使用真实数据和合成数据的详细模拟环境,我们表明,与其他了解内容受欢迎程度的启发式方案相比,所提出的技术可将用户感知的延迟降低多达25%。

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