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Efficiently allocating distributed caching resources in future smart networks

机译:在未来的智能网络中有效分配分布式缓存资源

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During the last decade, Internet Service Providers (ISPs) infrastructure has undergone a major metamorphosis driven by new networking paradigms, namely: Software Defined Networks (SDN) and Network Function Virtualization (NFV). The upcoming advent of 5G will certainly represent an important achievement of this evolution. In this context, static (planning) or dynamic (on-demand) caching resources placement remains an open issue. In this paper, we propose a new technique to achieve the best trade-off between the centralization of resources and their distribution, through an efficient placement of caching resources. To do so, we model the cache resources allocation problem as a multi-objective optimization problem, which is solved using Greedy Randomized Adaptive Search Procedures (GRASP). The obtained results confirm the quality of the out-comes compared to an exhaustive search method and show how a cache allocation solution depends on the network's parameters and on the performance metrics that we want to optimize.
机译:在过去的十年中,Internet服务提供商(ISP)基础结构经历了新的网络范式驱动的主要变态,这些新的网络范式是:软件定义网络(SDN)和网络功能虚拟化(NFV)。即将到来的5G无疑将代表这一演进的重要成就。在这种情况下,静态(计划)或动态(按需)缓存资源放置仍然是一个悬而未决的问题。在本文中,我们提出了一种新技术,可以通过对缓存资源进行有效放置来实现资源集中与分布之间的最佳权衡。为此,我们将缓存资源分配问题建模为一个多目标优化问题,可以使用贪婪随机自适应搜索过程(GRASP)来解决。与详尽的搜索方法相比,获得的结果确认了结果的质量,并显示了缓存分配解决方案如何取决于网络的参数以及我们要优化的性能指标。

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