Single-Choice Aided Marking System Research Based on Back Propagation  Neural Network

Zhang, Yunzuo; Li, Yang; Guo, Wenfei; Huo, Lei; Zhang, Jiayu; Guo, Kaina

doi:10.32604/jcs.2021.017071

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…In this work, to decide whether to accept the incoming request, the admission control uses the FNN [16], which computes the admission probability for each content request AP(u,w), where u represents the object features and w represents the weights of the neural network. The object's features can be its size, frequency of previous requests or its recency as shown in Table 1.…”

Section: Phase 1: Admission Control Phasementioning

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: Phase 1: Admission Control Phasementioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

show abstract

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

Abdo

Ahmad

Abed

2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Single-Choice Aided Marking System Research Based on Back Propagation Neural Network

Cited by 2 publications

References 9 publications

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

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

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

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