Information‐Centric Networking Cache Placement Method Based on Cache Node Status and Location

Man, Dapeng; Wang, Yao; Wang, Hanbo; Guo, Jiafei; Lv, Jiguang; Xuan, Shichang; Yang, Wu

doi:10.1155/2021/5648765

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…Similar to papers [32,33], we employ a real-world network topology GEANT [31], which has 45 nodes associated with 71 edges. The server is placed at the "UK" node, and all other nodes are forwarders.…”

Section: Network Topologymentioning

confidence: 99%

A GNN-based proactive caching strategy in NDN networks

Hou

Nayak

2023

Peer-to-Peer Netw. Appl.

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As people spend more time watching and sharing videos online, it is critical to provide users with a satisfactory quality of experience (QoE). Leveraging the in-network caching and named-based routing features in Named Data Networks (NDNs), our paper aims to improve user experience through caching. We propose a graph neural networkgain maximization (GNN-GM) cache placement algorithm. First, we use a GNN model to predict users' ratings of unviewed videos. Second, we consider the total predicted rating of a video as the gain of the cached video. Third, we propose a cache placement algorithm to maximize the caching gain and actively cache videos. Cache replacement is implemented based on the cache gain ranking of videos, with higher cache gain videos replacing lower cache gain videos. We compare GNN-GM with two state-of-the-art caching strategies, namely the NMF-based caching strategy and GNN-CPP. GNN-GM is also compared with two traditional caching strategies, LCE and LRU, LCE and FIFO. We evaluate the five caching strategies using real-world datasets in a tree network topology, a real-world network topology GEANT, and various random topologies. The experimental results show that our caching policy significantly improves cache hit ratio, latency and server load. Notably, GNN-GM achieves a 25% higher cache hit rate, 5% lower latency and 7% lower server load than GNN-CPP in GEANT.

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Section: Network Topologymentioning

confidence: 99%

A GNN-based proactive caching strategy in NDN networks

Hou

Nayak

2023

Peer-to-Peer Netw. Appl.

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“…A detection method based on a gradient boost decision tree (GBDT) was designed in [8] for an information-centric network with lesser packet delivery. An on-path implicit cooperative caching algorithm was investigated in [9] to consider the state and location of the cache node. The designed algorithm minimizes the redundancy of the data as well as the average request length.…”

Section: Related Workmentioning

confidence: 99%

IoT Content Cache Efficacy in Health Care Data Diagnostics Using Isotonic Regressive Adaptive Boost Classification

2023

IJIES

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Information-centric networking (ICN) with the internet of health things (IoHT) provides a secure network between medical experts and patients. Several researchers have investigated cache with IoHT data in ICN. However, there are still issues like which content to be cached or node selection for placing the content. In this work, an efficient caching strategy called, isotonic regressive adaptive boost classification-based information centric network (IRABC-ICN) is proposed with IoHT data. Initially, patient healthcare data requests are obtained from maternal healthcare. The patient information is registered and an information copy is deployed to the cache of every router node. Each request to the router node is searched in the content storage (CS). Next, isotonic regressive analysis is to analyze patient healthcare data request search. Here CS is checked and upon successful validation, the particular router node is selected to place the content and on contrary, the patient information is stored in the cache. Finally, the modest adaptive boost classification is carried out to obtain strong classification results with higher efficiency of content distribution and lesser network traffic, and server bandwidth. To prove the performance of this proposed IRABC-ICN, its cache hit rate, network latency, and average request length are used as the evaluation metrics. The efficacy of IRABC-ICN is proved by comparing it with existing PCSRC, cooperative caching, and context-based caching mechanisms with the maternal health risk dataset. The outcome of the proposed IRABC-ICN achieves enhancement in cache hit rate by 9%, minimization of network latency, and average request length by 38%, and 13% as compared to PCSRC, cooperative caching, and context-based caching mechanism respectively.

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“…However, this approach ignores the storage capacity of other nodes on the path, so it cannot maximize the cache revenue. Man et al proposed measuring the state value of each node on the path for cache decision [42]. Inspired by this mechanism, we apply its idea to our scenario and design a caching mechanism based on content value and node importance.…”

Section: Caching Mechanism Based On Node Cache Valuementioning

confidence: 99%

An Active Path-Associated Cache Scheme for Mobile Scenes

2022

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With the widespread growth of mass content, information-centric networks (ICN) have become one of the research hotspots of future network architecture. One of the important features of ICN is ubiquitous in-network caching. In recent years, the explosive growth of mobile devices has brought content dynamics, which poses a new challenge to the original ICN caching mechanism. This paper focuses on the WiFi mobile scenario of ICN. We design a new path-associated active caching scheme to shorten the time delay of users obtaining content to enhance the user experience. In this article, based on the WiFi scenario, we first propose a solution for neighbor access point selection from a theoretical perspective, considering the caching cost and transition probability. The cache content is then forwarded based on the selected neighbor set. For cached content, we propose content freshness according to mobile characteristics and consider content popularity at the same time. For cache nodes, we focus on the size of the remaining cache space and the number of hops from the cache to the user. We have implemented this strategy based on the value of caching on the forwarding path. The simulation results show that our caching strategy has a significant improvement in performance compared with active caching and other caching strategies.

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Information‐Centric Networking Cache Placement Method Based on Cache Node Status and Location

Cited by 8 publications

References 29 publications

A GNN-based proactive caching strategy in NDN networks

A GNN-based proactive caching strategy in NDN networks

IoT Content Cache Efficacy in Health Care Data Diagnostics Using Isotonic Regressive Adaptive Boost Classification

An Active Path-Associated Cache Scheme for Mobile Scenes

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