大规模移动数据传输的近似缓存和路由算法

Konstantinos Poularakis, G. Iosifidis, L. Tassiulas
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引用次数: 39

摘要

小型蜂窝为管理令网络运营商不堪重负的移动数据增长提供了一个很有前途的解决方案。为了降低连接这些基站和核心网络的回程链路的容量和成本,已经提出了在小型蜂窝基站上对流行内容进行本地缓存。然而,导出最优缓存策略仍然是一个具有挑战性的开放问题,特别是如果考虑到诸如基站带宽限制之类的现实参数。后一种约束对于用户请求非常大的情况尤其重要。我们考虑了这样一个场景,并制定了联合缓存和路由问题,旨在最大化部署的小蜂窝基站所服务的内容请求的比例。这是一个np困难问题,因此我们不能得到一个精确的最优解。因此,我们提出了一种新的近似框架,该框架基于对设施定位问题的一个众所周知的变体的简化。这允许我们利用丰富的文献在设施定位问题,为了建立有界近似算法为我们的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approximation caching and routing algorithms for massive mobile data delivery
Small cells constitute a promising solution for managing the mobile data growth that has overwhelmed network operators. Local caching of popular content items at the small cell base stations has been proposed in order to decrease the capacity-and hence the cost- of the backhaul links that connect these base stations with the core network. However, deriving the optimal caching policy remains a challenging open problem especially if one considers realistic parameters such as the bandwidth limitation of the base stations. The latter constraint is particularly important for cases when users requests are massive. We consider such a scenario and formulate the joint caching and routing problem aiming to maximize the fraction of content requests served by the deployed small cell base stations. This is an NP-hard problem and hence we cannot obtain an exact optimal solution. Thus, we present a novel approximation framework based on a reduction to a well known variant of the facility location problem. This allows us to exploit the rich literature in facility location problems, in order to establish bounded approximation algorithms for our problem.
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