Smart Caching: Empower the Video Delivery for 5G-ICN Networks

Zhang, Zhe; Lung, Chung-Horng; St-Hilaire, Marc; Lambadaris, Ioannis

doi:10.1109/icc.2019.8762067

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…Therefore, we do not consider user mobility. Except for the user mobility, [6] employs the same caching scheme as [22], which is the method we compare in our paper. We utilize their caching decision module to calculate the gain of caching movies in each forwarder.…”

Section: Resultsmentioning

confidence: 99%

“…Some papers make caching decisions by predicting user preferences. The authors of [22] proposed a proactive caching method in the 5G-ICN scenario. They applied NMF [7] to predict user ratings of movies that have not been watched.…”

Section: User Preferences-based Content Cachingmentioning

confidence: 99%

“…We sort the dataset by timestamp and use 80% of it as the training dataset and 20% as the testing dataset to compare the performances of various caching strategies. Similar to papers [6,22], we consider the user rating for a movie as the user request for that movie. We randomly divide 943 users into two user communities.…”

Section: Dataset Collectionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: User Preferences-based Content Cachingmentioning

confidence: 99%

See 1 more Smart Citation

A GNN-based proactive caching strategy in NDN networks

Hou

Nayak

2023

Peer-to-Peer Netw. Appl.

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show abstract

Section: User Preferences-based Content Cachingmentioning

confidence: 99%

A GNN-based Proactive Caching Strategy in NDN Networks

Hou

Nayak

2022

Preprint

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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 network-gain 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.

show abstract