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Intelligent Caching at the Mobile Edge Network
Kyi Thar, Nguyen H. Tran, and Choong Seon Hong
Department of Computer Science and Engineering, Kyung Hee University.
Email: {kyithar, nguyenth, cshong}@khu.ac.kr
Abstract
Caching popular contents at edge nodes (base stations) becomes a promising solution to improve the user’s quality
of services, as well as to reduce the network traffic. However, to i) correctly predict future popularity of contents, and ii)
to efficiently store popular contents are challenging issues. So, in this paper, we proposed learning based caching
scheme which is two folds: i) predict the future popularity score of contents using deep learning, and ii) cache the
popular video with proactive caching. Then, we develop and train the prediction scheme using Tensorflow libraries,
and test the performance of caching scheme with python based simulator. The simulation results show that the
proposed scheme outperforms existing algorithms in terms of content hit probability, access delay and etc.
1. Introduction
According to the Cisco Visual Networking Index,
watching videos from wireless devices has been
generating most of the Internet traffic and is forecast to
continue to grow exponentially [1]. In order to handle the
overwhelming Internet traffic, several future Internet
network architectures have been proposed with in-network
caching capability[2]. With the help of in-network caching,
edge nodes (Base Stations (BSs) and Small-cell Base
Stations (SBSs)) temporarily store video contents in their
cache to satisfy user requests in the near future, whereas
cache capacity of the edge node is limited. So, each edge
node should store only popular video contents. Video’s
popularity can be defined as the ratio of the number of
requests for the particular content to the total number of
requests from users, usually obtained for a certain region
during a given period of time. In reality, it is not easy to
know whether the video content is popular or not because
the video popularity can temporally and/or spatially vary.
Therefore, content’s popularity prediction becomes one of
the most challenging issues to design an efficient caching
scheme. Caching scheme can be classified into two
categories; i) reactive caching, and ii) proactive caching.
Reactive caching: On the request arrives, the edge node
makes a decision whether to store the requested content
or not is known as reactive caching [3]. Proactive caching:
In the proactive caching scheme, every time period, the
edge node stores the set of contents, depending on
predicted content’s popularity, before requested by users
[4].
Our contributions are summarized as follows:
We design the system to jointly work with deep
neural network based prediction system and mobile
edge computing architecture.
We utilize Deep Recurrent Neural Network to
predict the popularity of video contents with
multidimensional sequences of information.
We use MovieLens 1M dataset to train and test our
proposed scheme. Test the performance of
proposed caching scheme using the python based
simulator.
2. System Model
The system model of proposed scheme is shown in Fig.1,
where BSs/SBSs are attached with the cache storage.
BSs/SBSs collects the information as log files and send
these log files to controller, which is located at the data
center, to train the prediction model. Popular videos are
stored at BSs/SBs with the help of prediction model.
When request arrive, BS/SBS checks the requested video
is located in its cache or not. If it is located, serve the
content to the user. Otherwise, BS/SBS downloads the
content directly from original servers.
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Figure 1 System Model
3. Popularity Score Prediction System Design
The high level prediction system design is shown in Fig.
2, where dotted lines show the training process and dark
lines show the real time prediction process. The prediction
system consists of two parts i) Prediction module for
controller, and ii) Prediction module for BSs/SBSs. The
prediction module for controller is responsible for training
the popularity score prediction and located at the high-end
computing node (e.g. Data Center) because BSs/SBSs do
not have enough capacity to train the deep learning
models. Then, trained model is distributed to all nodes.
The prediction module for node is responsible for
popularity score prediction and cache decision, located at
the BSs/SBSs.
Prediction module for controller: consists of i) Data
Collector and ii) prediction module. Data controller collects
log files which included related information of the contents
and users from BSs/SBSs at every time period t. The
prediction module preprocesses the data from the log files
to get dataset to train the prediction model. Prediction
model can be different type of deep learning model such
as Deep Belief Network (DBN), Deep Convolutional
Neural Network (DCNN) and Deep Recurrent Neural
Network (DRNN). For the popularity score prediction, we
utilize the Recurrent Neural Network (RNN) because the
incoming requests for videos are in a sequential manner.
Among the variants of RNN such as Long Short-Term
Memory (LSTM), Gated Recurrent Unit (GRU), we choose
LSTM [5] as the RNN’s cell for distributed training, which
uses asynchronous stochastic gradient descent optimizing
on large clusters. Finally, the optimized trained model will
be stored in the Trained Model module and transfers the
trained model to the all of the nodes, when training is
finished.
Prediction module for BSs/SBSs: consists of i) Data
Collector, ii) Prediction Module, and iii) Cache Decision.
Prediction Module extracts information and feed those
information to the trained model and gets predicted
popularity scores of videos. Then, the Cache Decision
module utilizes predicted popularity scores to make cache
decision, whether the video content should store or not on
its cache.
4. Performance Evaluations
We use many to one RNN model which has 50 hidden
layers. We choose sigmoid function as the activation
Figure 2. Popularity Score Prediction System Model
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function. We choose root mean square error as loss
function and Adam optimizer to minimize the training loss.
We developed the python based chunk level simulator to
simulate our proposed scheme. Fig.3 shows the root
mean square error comparison between LSTM, GRU and
Simple RNN, where the LSTM gives the smallest root
mean square error. Then, in Fig.4, we compare the
caching performance of proposed scheme with LSTM,
GRU and Simple RNN, where LSTM gives the highest
probability of cache hit among others.
5. Conclusion
In this paper, we proposed the scheme to predict the
popularity score of each content by utilizing deep
recurrent neural network. Then, we proposed the caching
scheme which utilizes output (popularity score) of the
deep recurrent neural network to make a cache decision,
where each node their own cache decision independently.
Finally, we developed the prediction model by using
Tensorflow and train the model with MoveLens dataset.
As for the future work, we will apply the reinforcement
learning with the recurrent neural network for the video
contents predicting.
Acknowledgment
This work was supported by the National Research
Foundation of Korea(NRF) grant funded by the Korea
government(MSIT) (NRF- 2017R1A2A2A05000995). *Dr.
CS Hong is the corresponding author.
This work was supported by Institute for Information &
communications Technology Promotion(IITP) grant
funded by the Korea government(MSIT) (No. 2013-0-
00409, Research and Development of 5G Mobile
Communications Technologies using CCN-based Multi-
dimensional Scalability) *Dr. CS Hong is the
corresponding author.
References
[1] http://www.cisco.com/c/en/us/solutions/serviceprovider/visual-
networking-index-vni/index.html.
[2] Thar, Kyi, et al. "Hybrid caching and requests forwarding in
information centric networking." Network Operations and
Management Symposium (APNOMS), 2015 17th Asia-Pacific.
IEEE, 2015.
[3] Suoheng Li, Jie Xu, Mihaela van der Schaar, and Weiping Li.
Trendaware video caching through online learning. IEEE
Transactions on Multimedia, 18(12):2503–2516, 2016.
[4] Mingzhe Chen, Walid Saad, Changchuan Yin, and Merouane
Deb bah. Echo state networks for proactive caching in cloud-
based radio access networks with mobile users. IEEE
Transactions on Wireless Communications, 16(6):3520–3535,
2017.
[5] Has¸im Sak, Andrew Senior, and Franc¸oise Beaufays. Long
short-term memory recurrent neural network architectures for
large scale acoustic modeling. In Fifteenth Annual Conference of
the International Speech Communication Association, 2014.
Figure 3. Root Mean Squared Error Comparison Figure 4. Probability of Cache Hit Comparison
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