Toward Edge-Assisted Video Content Intelligent Caching With Long Short-Term Memory Learning

Abstract

Nowadays video content has contributed to the majority of Internet traffic, which brings great challenge to the network infrastructure. Fortunately, the emergence of edge computing has provided a promising way to reduce the video load on the network by caching contents closer to users.But caching replacement algorithm is essential for the cache efficiency considering the limited cache space under existing edge-assisted network architecture. To investigate the challenges and opportunities inside, we first measure the performance of five state-of-the-art caching algorithms based on three real-world datasets.Our observation shows that state-of-the-art caching replacement algorithms suffer from following weaknesses: 1) the rule-based replacement approachs (e.g., LFU,LRU) cannot adapt under different scenarios; 2) data-driven forecast approaches only work efficiently on specific scenarios or datasets, as the extracted features working on one dataset may not work on another one. Motivated by these observations and edgeassisted computation capacity, we then propose an edge-assisted intelligent caching replacement framework LSTM-C based on deep Long Short-Term Memory network, which contains two types of modules: 1) four basic modules manage the coordination among content requests, content replace, cache space, service management; 2) three learning-based modules enable the online deep learning to provide intelligent caching strategy. Supported by this design, LSTM-C learns the pattern of content popularity at long and short time scales as well as determines the cache replacement policy. Most important, LSTM-C represents the request pattern with built-in memory cells, thus requires no data pre-processing, pre-programmed model or additional information. Our experiment results show that LSTM-C outperforms state-of-the-art methods in cache hit rate on three real-traces of video requests. When the cache size is limited, LSTM-C outperforms baselines by 20%∼32% in cache hit rate. We also show that the training and predicting time of one iteration are 8.6 ms and 300 µs on average respectively, which are fast enough for online operations.INDEX TERMS Edge-assisted caching replacement, intelligent content caching, long short term memory.

Educational Innovation in Higher Education: Use of Role Playing and Educational Video in Future Teachers’ Training

“…Videos combine images, sounds and words that can be presented in different ways and at different lengths to facilitate learning [32]. Many studies in recent years have shown the benefits of using video in the classroom [33,34], as the multimedia factor turns any new element in the learning process into long-term memory learning [35].…”

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

“…Li et al [59] considered the video propagation as well as popularity evolution patterns and developed an integration of ARIMA, multiple linear regression (MLR), and k-nearest neighbor regression (kNN) to predict the social patterns to improve caching performance. Zhang et al [60] proposed an intelligent edge-assisted caching framework LSTM-C based on LSTM to better learn to content popularity patterns both at long and short time scale. Zhu et al [61] proposed to leverage DRL to automatically learn an end-to-end caching policy, where the user requests, network constraints, and external information are all embedded in the learning environment.…”

Using Long Short-Term Memory for Building Outdoor Agricultural Machinery

“…Zhou et al ( 2019 ) presented a system that predicts sunlight and estimates the electricity produced by solar power systems through LSTM, which becomes the basis of solar power equipment management. Finally, Zhang et al ( 2019 ) utilized LSTM in edge computing; when video files are too large, an LSTM technique helps predict the buffer memory for each edge computing, node. Guardo et al ( 2018 ) mainly used Fog Computing to reduce the workload of centralized servers.…”