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Optimal Cache Allocation under Network-Wide Capacity Constraint

机译:网络范围的容量约束下的最佳缓存分配

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Network cache allocation and management are important aspects of an Information-Centric Network (ICN) design, such as one based on Named Data Networking (NDN). We address the problem of optimal cache size allocation and content placement in an ICN in order to maximize the caching gain resulting from routing cost savings. While prior art assumes a given cache size at each network node and focuses on content placement, we study the problem when a global, network-wide cache storage budget is given and we solve for the optimal per-node cache allocation. This problem arises in cloud-based network settings where each network node is virtualized and housed within a cloud data center node with associated dynamic storage resources acquired from the cloud node as needed. As the offline centralized version of the optimal cache allocation problem is NP-hard, we develop a distributed adaptive algorithm that provides an approximate solution within a constant factor from the optimal. Performance evaluation of the algorithm is carried out through extensive simulations over multiple network topologies, demonstrating that our proposal significantly outperforms existing cache allocation algorithms.
机译:网络缓存分配和管理是以信息为中心的网络(ICN)设计的重要方面,例如基于命名数据​​网络(NDN)。我们解决了ICN中最佳高速缓存大小分配和内容放置的问题,以最大化由路由成本节省的缓存增益。虽然现有技术在每个网络节点处假设给定的高速缓存大小并侧重于内容放置,但我们研究了给出全局网络广泛的高速缓存存储预算时的问题,并且我们解决了最佳的每个节点高速缓存分配。此问题出现在基于云的网络设置中,其中每个网络节点都被虚拟化并在云数据中心节点内,根据需要使用从云节点获取的相关动态存储资源。随着最佳缓存分配问题的离线集中式,我们开发了一种分布式自适应算法,该算法在最佳的恒定因子内提供近似解。通过多个网络拓扑的广泛模拟进行算法的性能评估,表明我们的提议显着优于现有的高速缓存分配算法。

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