Social networks boast a huge number of worldwide users who join, connect, and publish various content, often very large, e.g. videos, images etc. For such very large-scale data storage, data replication using geo-distributed cloud services with virtually unlimited capabilities are suitable to fulfill the users’ expectations, such as low latency when accessing their and their friends’ data. However, service providers ideally want to spend as little as possible on replicating users’ data. Moreover, social networks have a dynamic nature and thus replicas need to be adaptable according to the environment, users’ behaviors, social network topology, and workload at runtime. Hence, it is not only crucial to have an optimized data placement and request distribution – meeting individual users’ acceptable latency requirements while incurring minimum cost for service providers – but the data placement must be adapted based on changes in the social network to keep it efficient and effective over time. In this paper, we model data placement as a dynamic set cover problem and propose a novel approach to solve this problem. We have run several experiments using two large-scale, open Facebook and Gowala datasets and real latencies derived from Amazon cloud datacenters to demonstrate our novel strategy’s efficiency and effectiveness.

社交网络引以为傲的是，全世界的用户都可以加入，连接和发布各种内容，通常是非常大的内容，例如视频，图像等。对于如此大规模的数据存储，使用具有几乎无限功能的地理分布云服务进行数据复制适合满足用户的期望，例如访问他们及其朋友的数据时的低延迟。但是，理想情况下，服务提供商希望在复制用户数据上花费尽可能少的钱。此外，社交网络具有动态性质，因此副本需要根据环境，用户的行为，社交网络拓扑和运行时的工作量进行调整。因此，优化数据放置和请求分发不仅很重要-在满足单个用户可接受的等待时间要求的同时为服务提供商带来最低的成本-而且必须根据社交网络的变化来调整数据放置以保持其高效性随着时间的推移。在本文中，我们将数据放置建模为一个动态集覆盖问题，并提出了一种解决该问题的新颖方法。我们已经使用两个大规模的开放式Facebook和Gowala数据集以及从Amazon云数据中心派生的实际延迟进行了几次实验，以证明我们这种新颖策略的效率和有效性。我们将数据放置建模为动态集覆盖问题，并提出一种新颖的方法来解决此问题。我们已经使用两个大规模的开放式Facebook和Gowala数据集以及从Amazon云数据中心派生的实际延迟进行了几次实验，以证明我们这种新颖策略的效率和有效性。我们将数据放置建模为动态集覆盖问题，并提出一种新颖的方法来解决此问题。我们已经使用两个大规模的开放式Facebook和Gowala数据集以及从Amazon云数据中心派生的实际延迟进行了几次实验，以证明我们这种新颖策略的效率和有效性。