A cooperation-driven ICN-based caching scheme for mobile content chunk delivery at RAN

Zhang, Xu; Cao, Yue

doi:10.1109/iwcmc.2017.7986495

Cited by 5 publications

(4 citation statements)

References 26 publications

(32 reference statements)

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“…However, because of the limited cache capacity, it is necessary to update cache content to ensure that cache-enabled networks always store the most popular content [8]. The most two common content update policies are the least frequently used (LFU) policy and the least recently used (LRU) policy [9]. LRU frequently stores the content with the latest access time, and LFU frequently stores content with the largest cumulative request times.…”

Section: Introductionmentioning

confidence: 99%

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A Smart Cache Content Update Policy Based on Deep Reinforcement Learning

Kwong

Liu

et al. 2020

Wireless Communications and Mobile Computing

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This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency. In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location. Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP). Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem. Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory. The DQN agent derives the optimal policy for the cache decision. Compared with the existing policies, the simulation results show that our proposed policy is 56%–64% improved in terms of the cache hit ratio and 56%–59% decreased in terms of the average latency.

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Section: Introductionmentioning

confidence: 99%

“…The cache hit ratio is utilised to describe how frequently content is requested by mobile users. In [9], an adaptive cache policy was proposed that can reduce user access latencies. In [12], an edge cache policy was proposed to reduce the average content delivery latency.…”

Section: Introductionmentioning

confidence: 99%

A Smart Cache Content Update Policy Based on Deep Reinforcement Learning

Kwong

Liu

et al. 2020

Wireless Communications and Mobile Computing

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“…Due to the fast development of the fifth generation of mobile communication (5G) and the rapid expansion of the smart mobile devices, a massive mobile data traffic has been generated within the wireless network [1]. As such, the demand of the wireless network capacity has been increasing intensively.…”

Section: Introductionmentioning

confidence: 99%

Predicting future location in mobile cache based on variable order of prediction-by-partial-matching algorithm

Kwong

Chen

et al. 2018

IET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018 (BRAIN 2018)

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Mobile caching at the edge of the wireless network has been regarded as an ideal approach that can alleviate the user access latencies. While there is a problem that the user terminal (UT) is moving too fast when enter a serving cache area, it may not have enough time to acquire the required data from the cache. One solution is to predict the UT's future location and pre-prepare the requested content at the cache devices that will appear in the UT's future path. Once the UT arrive at the serving cache area, they can directly acquire the data since it is already at the location, rather than send in a request to update the cache. The key point to achieve this reliably is the accuracy of the location prediction. This paper presents a location prediction model based on prediction-by-partial-matching (PPM) algorithm in the mobile cache design. The performance of this model will be compared by using oneorder context, two-order context and three-order context, respectively. All the models will be evaluated in a real world data.

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“…Several cooperative caching schemes [3] [114] have been proposed to allow nodes to cooperate with each other to make the caching decision. However, in those schemes cooperation only happens among the node and its neighbors.…”

Section: How Sdn Can Improve the Caching Efficiency For Icnmentioning

confidence: 99%

Performance Improvements of In-Network Caching in ICN-Based Networks

Zhang¹

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Pursing my Ph.D. degree in Canada was an exciting and amazing journey for me, and it started when I wrote my first email to my supervisor, Prof. Chung-Horng Lung. Hence, I would like to begin this section by expressing my deeply heartfelt thanks and gratitude to him, not only for his wholehearted guidance on my life as a student, but also for his profound influence on my values as a human being. From him, I learned how to perform research with rigorousness, and how to treat others with patience. Without his encouragements and supports, I would not have such a wonderful study. I would also like to thank my co-supervisor, Prof. Marc St-Hilaire, for his valuable comments and guidance. The academic writing skills and research methodologies he taught me helped me during my entire Ph.D. study and will continue to benefit my future work. More importantly, his rigorous attitude to academic is the most valuable treasure that I learned from him. Special thanks to Prof. Ioannis Lambadaris for offering me the internship opportunity at Ericsson. Besides, his immense knowledge and kind suggestions not only improve the quality of the research but also broaden my mind for my future career. I would also like to thank the committee members, Prof. Amiya Nayak, and Prof. F. Richard Yu for their helpful and insightful questions and suggestions. Many thanks also go to my friends Andy Yin,

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A cooperation-driven ICN-based caching scheme for mobile content chunk delivery at RAN

Cited by 5 publications

References 26 publications

A Smart Cache Content Update Policy Based on Deep Reinforcement Learning

A Smart Cache Content Update Policy Based on Deep Reinforcement Learning

Predicting future location in mobile cache based on variable order of prediction-by-partial-matching algorithm

Performance Improvements of In-Network Caching in ICN-Based Networks

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