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Caching on the Move: A User Interest-Driven Caching Strategy for D2D Content Sharing

机译:移动中的缓存:D2D内容共享的用户兴趣驱动的缓存策略

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Device-to-device (D2D) content sharing helps to accommodate the exponentially surge in mobile data traffic. However, how to cache in mobile users is crucial to ensure the above advantages. There are three issues that have not been fully considered in the previous works. First, the ignorance of mobility cannot depict the random connectivity of mobile users in D2D content sharing. Second, the lack of the diverse and complete information on user interest leads to unsatisfied demands of users. Third, caching a complete content will be wasteful due to the limited cache capacity, which is also not likely to be obtained in one connection. Regarding the three issues, we construct not only a user mobility model, which helps to provide the contact opportunity for the users sharing contents, but also a user interest prediction model, which combines the social proximity and the dynamic content popularity. Moreover, we exploit the maximum distance separable code to encode the contents into smaller partitions. Accordingly, we formulate a mobility-aware and user interest-driven caching problem as a 0-1 multiple knapsack problem. Due to its NP-hard property, we prove that this problem falls into the category of monotone sub-modular function over one matroid and multiple knapsack constraints. Then, we develop a corresponding algorithm based on a greedy approach, which approximates the optimum within a constant factor in polynomial time. Numerical results demonstrate the performance and the effectiveness of our proposed algorithm.
机译:设备到设备(D2D)内容共享有助于适应移动数据流量的指数级增长。但是,如何在移动用户中进行缓存对于确保上述优势至关重要。在先前的作品中,有三个问题尚未得到充分考虑。首先,移动性的无知无法描述D2D内容共享中移动用户的随机连接性。其次,缺乏关于用户兴趣的多样化和完整的信息导致用户的需求无法满足。第三,由于有限的缓存容量,缓存一个完整的内容将是浪费的,这在一个连接中也不太可能获得。针对这三个问题,我们不仅构建了有助于为共享内容的用户提供联系机会的用户移动性模型,而且还构建了将社交接近度和动态内容流行度相结合的用户兴趣预测模型。此外,我们利用最大距离可分离代码将内容编码为较小的分区。因此,我们将移动意识和用户兴趣驱动的缓存问题公式化为0-1多重背包问题。由于其NP-hard属性,我们证明了该问题属于一个拟阵和多个背包约束的单调子模函数。然后,我们开发了一种基于贪婪方法的相应算法,该算法在多项式时间内恒定因子内逼近最佳算法。数值结果证明了该算法的性能和有效性。

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