Efficient Pooling and Collaborative Cache Management for NDN/IoT Networks

Abstract

Named Data Networking (NDN) has been recognized as a lever to the Internet of Things (IoT). One of the most founding features of NDN is in-network caching to improve data availability and reduce retrieval delays and network load. Despite the existence of several caching decision algorithms, the fetching and distribution of contents with minimum resource utilization remains a great challenge. In this paper, we propose an efficient caching technique named PoolCache that augments the effective caching capacity of some defined conglomerates of nodes. This is accomplished by pooling the various caches and manage them in a way to insure zero content redundancy within any defined node conglomerate. The resulting high diversity of cached contents throughout the network tacitly amounts to much better overall performances. We conducted extensive simulations using the CCNsim simulator to evaluate the performance of PoolCache and compare it to that of some well known caching strategies. Simulations using a large Transit Stub topology show that PoolCache clearly outperforms the other caching strategies in terms of a much greater content diversity and consequently a limited number of content evictions, a much better cache hit ratio, and a much lower content retrieval delay. Simulations also showed that PoolCache benefits from any eventual content popularity.

PF-ClusterCache: Popularity and Freshness-Aware Collaborative Cache Clustering for Named Data Networking of Things

“…Results showed that the proposed scheme decreased the caching cost while improving the overall network performance. The authors of [29] introduced PoolCache, where they forced caching at the edge of the network and disallowed caching at internal nodes of the network. PoolCache presents a good level of diversity and zero redundancy within each pool of routers.…”

“…In this section, we first evaluate the performance of our proposed caching technique, PF-ClusterCache, and then we perform a comparative analysis with three different caching approaches. These are the well-known Leave Copy Everywhere LCE [27], which is used as a yardstick for comparison in many studies on caching in NDN; the Caching Fresh and Popular Content (CFPC) [23], which is, similar to PF-ClusterCache, an approach based on both popularity and freshness; and the PoolCache [29], which is, similar to PF-ClusterCache, a collaborative caching scheme based on pooling the cache storage of several nodes but with no explicit treatment of data popularity and no consideration of data freshness. The performance evaluation and the comparative analysis are conducted by simulation using the ndnSIM simulator [40,41].…”

“…The transit domains, however, are intended to efficiently interconnect the stub domains. The TS topology has been used to represent named data networking of things in works such as [10,24,29]. We used the BRITE library for ndnSIM to generate a TS topology integrated within the ndnSIM simulator [40,41].…”

“…The comparative analysis of PF-ClusterCache is performed with three different caching approaches: the well-known Leave Copy Everywhere (LCE) [27], customarily used as a yardstick for comparison in many studies on caching in NDN; the Caching Fresh and Popular Content (CFPC) [23], which is based on both popularity and freshness; and the PoolCache [29], which is a collaborative caching scheme based on pooling the cache storage of several nodes but with no explicit treatment of data popularity and no consideration of data freshness. These three caching policies use the Least Recently Used (LRU) replacement scheme.…”

CCJRF-ICN: A Novel Mechanism for Coadjuvant Caching Joint Request Forwarding in Information Centric Networks

“…The CCJRF-ICN protocol aims to perform superior to the ProbCache approach as it is efficient compared to the whole course caching method. For NDN based IoT networks, the authors in [27] have introduced an efficient collaborative caching mechanism called PoolCache that increases the caching capability of some defined clusters of a node. It requires very limited resources though attains better network performance.…”