Caching Salon: From Classical to Learning-Based Approaches

Zhao, Yangchao; Zhang, Xu; Yang, Kai; Fan, Qilin; Guo, Dongchao; Lyu, Yeoung-Su; Yin, Hao; Ma, Zhan

doi:10.1109/sose.2019.00046

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…Many researchers have discussed the caching systems and techniques utilizing the caches in the best way to maximize the hit rate. Zhao et al [2] reviewed most of the caching techniques recently used in networks such as CDNs. The authors first discussed traditional caching strategies, such as LRU, LFU, and size-aware approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

A smart admission control and cache replacement approach in content delivery networks

2023

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Content Delivery Networks (CDNs) distribute most data traffic nowadays by caching the contents in a network of servers to provide users with the requested objects, and helping to reduce latency when delivering contents to the user. The content caching system performance depends upon many factors such as where the objects should be stored, which object to store, and when to cache them. The proposed methodology includes two main phases: an admission control phase and a cache replacement phase. The admission control phase is responsible for accepting or rejecting the incoming request based on training the Reinforcement Learning (RL) algorithm to make the best decision in the near future to maximize its reward, which, in this case, is the hit ratio. The cache replacement phase estimates the object's future popularity. This is achieved by building a predictive model based on the popularity prediction mechanism, where the Long-Short-Term Memory (LSTM) model is used to compute the object's popularity. The LSTM model's outcome can help decide which objects to cache and which objects to evict from the cache. The proposed methodology is tested on a dataset to demonstrate its effectiveness in enhancing the hit ratio compared to conventional replacement policies such as First-in-First-Out (FIFO), Least Recently Used (LRU), Least Frequently Used (LFU) and a recent machine learning-based algorithm. The experimental results on the dataset revealed that the proposed methodology outperformed the baseline algorithms by 34.7% to 97.17% with a cache size of 130.

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Section: Literature Reviewmentioning

confidence: 99%

A smart admission control and cache replacement approach in content delivery networks

2023

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“…Objective ( 5) is to minimize the content delivery latency, where P m,i,t represents the popularity of content o i in server s m at time t. Constraint (6) ensures that the total sizes of all the cached contents at server s m should not exceed the server's capacity. Constraint (7) represents that the cloud data center stores all the contents.…”

Section: B Problem Formulationmentioning

confidence: 99%

Cooperative Edge Caching Based on Temporal Convolutional Networks

Zhang

Min

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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With the rapid growth of networked multimedia services in the Internet, wireless network traffic has increased dramatically. However, the current mainstream content caching schemes do not take into account the cooperation of different edge servers, resulting in deteriorated system performance. In this paper, we propose a learning-based edge caching scheme to enable mutual cooperation among different edge servers with limited caching resources, thus effectively reducing the content delivery latency. Specifically, we formulate the cooperative content caching problem as an optimization problem, which is proven to be NP-hard. To solve this problem, we design a new learning-based cooperative caching strategy (LECS) that encompasses three key components. Firstly, a temporal convolutional network driven content popularity prediction model is developed to estimate the content popularity with high accuracy. Secondly, with the predicted content popularity, the concept of content caching value (CCV) is introduced to weigh the value of a content cached on a given edge server. Thirdly, an novel dynamic programming algorithm is developed to maximize the overall CCV. Extensive simulation results have demonstrated the superiority of our approach. Compared with the state-of-the-art caching schemes, LECS can improve the cache hit rate by 8.3%-10.1%, and reduce the average content delivery delay by 9.1%-15.1%.

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