edge ai a ssists partial content caching with smart...
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Edge AI assists Partial Content Caching with Smart
Content Prefetching Scheme
Kyi Thar, and Choong Seon Hong
Department of Computer Science and Engineering, Kyung Hee University
{kyithar, cshong}@khu.ac.kr
Abstract
Watching videos (Contents) from mobile devices has been causing most of the network traffic and is projected
to remain to increase exponentially. Thus, numerous types of content and chunk based caching schemes have
been proposed to handle the increasing traffic. Those caching schemes cache the whole videos at the edge
nodes, but most of the users view only the beginning of the videos. Hence, caching the complete video on the
edge node is an ineffective solution to reduce the network traffic as well as to improve the cache utilization.
Thus, in this paper, we proposed a chunk-level caching scheme to store popular videos partially and a smart
prefetching scheme to provide the missing chunks of the video. The simulation results confirm that our proposed
scheme outperforms the existing schemes.
1. Introduction
According to the CISCO, watching videos from mobile
devices has been causing most of the network traffic and is
projected to remain to increase exponentially [1]. Thus, many
researchers are proposing numerous types of caching
schemes based on reactive approaches and proactive
approaches to handle the growing video traffic. In the reactive
caching, the edge node decides to store videos when the
requests or videos arrived [2]. In the proactive approach,
popular videos are cached based on the prediction results
before requested by any users [3]. So, the performance of
the proactive approach is changing based on the efficiency
of the prediction model. Currently, the deep learning models
get huge attention to utilize in content's popularity prediction
scheme because of the advances in big data and high
computing power. The aforementioned caching schemes
consider storing the complete popular videos at the edge
nodes (i.e., Base station). The main issue is that most of the
users view only the beginning of the videos because they stop
watching videos when they do not like the beginning. Hence,
caching the whole video is an ineffective solution to reduce
network traffic as well as to improve the users' Quality of
Experience (QoE).
Therefore, in this paper, we proposed edge Artificial
Intelligence (AI) assists partial video caching to improve the
cache performance. We additionally proposed the smart
prefetching scheme to reduce the latency to access the
missing chunks. The goal of this paper is to minimize the
latency to access the videos from the users' devices.
This work was supported by Institute of Information &
communications Technology Planning & Evaluation (IITP) grant
funded by the Korea goverment(MSIT) (No.2019-0-01287,
Evolvable Deep Learning Model Generation Platform for Edge
Computing). *Dr. CS Hong is the corresponding author
Our contributions are summarized as follows:
We formulate the video accessing latency
minimization problem.
We proposed the popularity prediction model based
on the deep neural network models.
We proposed deep reinforcement learning based
caching and prefetching scheme.
Figure 1 System Model
2. System Model
The system model of the proposed scheme is shown
in Fig.1, where Content Server 𝑐 is located at outside
of the current Autonomous System and it provides the
set of videos 𝐹 = {1,2, . . , 𝑓} . Assume that all of the
videos have the same size or the same number of
chunks. A set of edge nodes (base stations) is denoted
as 𝐵 = {1,2, . . , 𝑏} and located nearest to the user. A set
of users is denoted as 𝑈 = {1,2, . . , 𝑢}. The edge node 𝑏
provides videos from its cache storage when the
requested videos are located at its cache storage.
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Otherwise, it retrieves requested videos from the
Content Server 𝑐 and provides content to the
requested user 𝑢. The cache storage size of the edge
node 𝑏 is denoted as 𝑠𝑏 and the total cache storage
size of the whole network is denote as ∑ 𝑠𝑏𝐵𝑏 = 𝑆.
3. Problem Formulation
In this section, we formulate the partial content
caching and prefetching problem as content access
latency minimizing problem.
min𝑥
∑ ∑ 𝑟𝑓𝑖(𝑡+1)
(𝑥𝑓𝑖(𝑡+1)
𝛿𝑢𝑏 + (1 − 𝑥𝑓𝑖
(𝑡+1))𝛿𝑏
𝑐𝑝)
𝐼
𝑖
𝐹
𝑓, (1)
𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜: ∑ ∑ 𝑥𝑓𝑖(𝑡+1)
≤ 𝑠𝐼
𝑖
𝐹
𝑓,
𝑥𝑓𝑖(𝑡+1)
∈ {0,1},
where 𝛿𝑢𝑏 is the latency of retrieving videos from the
edge node 𝑏 and 𝛿𝑏𝑐𝑝
is the latency of retrieving videos
from the edge node 𝑏 from content server 𝑐𝑝 . The
future request for the chunk 𝑖 of content 𝑓 is denoted
as 𝑟𝑓𝑖(𝑡+1)
. The cache decision variable to store the
chunk 𝑖 of content f based on future requests is
denoted as 𝑥𝑓𝑖(𝑡+1)
∈ {0,1}. The chunk 𝑖 of content f will
store on edge node when 𝑥𝑓𝑖(𝑡+1)
= 1. Then, we define
the constraint where the cache storage of edge 𝑏
cannot store the contents more than its capacity. The
optimization problem (1) is a combinational problem
and it needs an exhaustive search approach to solve
the problem. Also, it is not easy to solve because the
future request count information of each video is
unknown. Therefore, we apply a deep learning
approach to solve this problem.
4. Edge AI assists partial caching and smart prefetching
In this section, we solve the optimization problem (1)
by using edge AI assist cache decision and perfection
decision scheme. Fig.2 shows the overview system
components needed to implement the proposed
scheme. Fig.2 (a) and (b) represent the important inputs
parameters to make the cache decision. Fig.2 (a) shows
that the example of content popularity profile which is
the output of the Popularity Prediction module. Fig.2 (b)
shows the sample collected videos accessed data,
where red blocks show the most accessed chunks of
each video. As shown in Fig.2 (c) the cache storage
space is divided into two partitions: i) Content Storage
and ii) Prefetching Buffer. The Content Storage partition
stores the partial popular videos and the prefetching
buffer stores the current prefetching chunks of videos.
The Popularity Prediction module predicts video
popularity with the help of a deep learning model and
the details deep learning model used in this prediction
module is discussed in Section 5. The Cache Decision
module decides to store the chunks of the video based
on the popularity profile and historical data. The
Prefetching Decision module performs the missing
chunks retrieving process. Note that both Cache
Decision and Prefetching modules utilize deep
reinforcement learning.
The process of partial content caching and the
prefetching process is shown in ALG. 1. The inputs for
this algorithm are raw collected data at the edge node
b. The outputs of this algorithm are making cache
decisions and prefetching decisions. The initial deep
learning models are constructed at the cloud data
center because this process needs high computing
power (line 3). Then the deep learning models are
transferred to the edge node b. The edge node b makes
a content popularity prediction based on the deep
learning model and constructs the popularity profile of
the videos. Then, the edge node makes a cache
decision based on the collected videos accessed data
and predicted popularity profile. When the retrieving
contents from the user reach to the certain chunks level,
the prefetching decision module pre-download the
chunks before requested by users.
Figure 2 Overview System Components
Algorithm 1: Content caching and prefetching process
1: Input: Raw collected Data
2: Output: caching the popular chunks and
prefetching the missing chunks
▷Cloud Datacenter
3: The initial deep learning models are constructed at
the cloud data center and transfer to edge node 𝑏
▷Edge Node
4: The content popularity of each content is
predicted by using deep learning model and
construct the Popularity Profile
5: Caching the chunks 𝑖 of content 𝑓 based on
popularity profile and videos accessed data, where
the popular chunks are stored until the cache
space is full
6: Prefetching the missing chunks based on the
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threshold ℎ and current requests pattern
5. Performance Evaluations
In this section, we examine the performance of the
proposed scheme and the current scheme with python
based simulator. From the MovieLens dataset, we
extract the request pattern of the videos. The dataset
covers 26, 000, 000 ratings given to 45, 000 movies by
270, 000 users. Then, we assume each video has 100
chunks. We used the bi-directional LSTM model with
the configuration LSTM <28, 28, 28>, Dense <76, 87,
64 >, (i.e., 28 cells in layer 1,2 and 3 of LSTM and 76
cells, 87 cells, and 64 cells, respectively, in layers 1,2
and 3 of dense layers). For the deep reinforcement
learning we choose Deep Neural Network with
<128,128,128> configuration. We choose the
probability of hit and content accessing latency which
is the objective function, as Key Performance Indicator
metrics.
Fig. 3 shows the measurement of a cache hit
probability where a greater probability of cache hit
indicates better the caching utilization performance. As
the increase in cache storage size, the cache hit
probability also rises due to the larger capacity to cache
more videos.
Fig. 4 shows the content retrieval latency comparison.
The lower the amount of retrieval latency, the better the
performance. The results in Fig. 4 show that the content
retrieval latency of our proposed scheme reduces the
network traffic by 10% on average compared to other
schemes.
6. Conclusion
In this paper, we proposed a scheme to cache the
popular chunks with the joint partial content caching
and smart prefetching. The proposal in this paper aims
to enable the user to get the requested videos instantly.
As a result, the proposed scheme improves not only the
probability of cache hit but also the average latency to
access the videos. We intensively simulated the
proposed scheme. The preliminary results confirm that
for our proposed scheme, the cache hit is higher and
the average latency to get the content is lower than
other proposals.
Figure 3 Cache hit comparison between optimal,
proposed and randomized solutions
Figure 4 latency comparison between optimal,
proposed and randomized solutions
References
[1] CISCO VNI. Accessed: Feb. 7, 2019. [Online].
[2] Saeed Ullah, Kyi Thar and Choong Seon Hong,
“Management of Scalable Video Streaming in Information
Centric Networking,”Multimedia Tools and Applications,
Multimed Tools Appl (2016), 28pages, October 2016.
[3] Anselme Ndikumana and Choong Seon Hong, "Self-
Driving Car Meets Multi-access Edge Computing for Deep
Learning-Based Caching," The International Conference on
Information Networking (ICOIN 2019), Jan. 9-11, 2019,
Kuala Lumpur, Malaysia
[4] K. Thar, T. Z. Oo, Y. K. Tun, D. H. Kim, K. T. Kim and C.
S. Hong, "A Deep Learning Model Generation Framework for
Virtualized Multi-Access Edge Cache Management," in IEEE
Access, vol. 7, pp. 62734-62749, 2019. doi:
10.1109/ACCESS.2019.2916080
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