INTERNET-DRAFT
Internet Engineering Task Force (IETF) Hong, Choong Seon
Category: Standards Track Kyung Hee University
Expires: October 10, 2020 Kyi Thar
Kyung Hee University
Ki Tae Kim
Kyung Hee University
Seok Won Kang
Kyung Hee University
October 2020
Edge AI assists Partial Content Caching with Smart Content
Prefetching Scheme
draft-edge-ai-cache-00.txt
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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, a chunk-level caching scheme to store popular
videos partially and a smart prefetching scheme is needed to
provide the missing chunks of the video.
This Internet-Draft will expire on August 09, 2021.
Copyright Notice
Copyright (c) 2020 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(http://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . .. . . . . . 2
1.1. Terminology and Requirements Language . . . . . . . . . 2
2. System Model . . . . . . . . . . . . . . . . . . . . . 3
3. Process of Sending Learning Model to predict the popularity . . 4
4. Process of Content retrieving process from the Edge Node. . . . 5
5. Process of Content retrieving process from the Content Server
via Edge Node. . . . . . . . . . . . .. . . . . . . . . . . . 6
6. Process of Content prefetching . . . . . . . . . . . .. . . . . 7
4. IANA Considerations . . . . . . .. . . . . . . . . . . . . . . 8
5. Security Considerations . . . . . . . . . . . . . . . . . . . 8
7. References . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
7.1. Normative References . . . . . . . . . . . . . . . . . . . . . 8
7.2. Informative References . . . . . .. . . . . . . . . . . . . . 8
Authors' Addresses . . . . . . . . . . . . . . . . . . . . .. . . . 9
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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 [a]. 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 [b]. In the proactive approach, popular
videos are cached based on the prediction results before requested
by any users [c][d].
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, edge Artificial Intelligence (AI) assists partial video
caching can be improved the cache performance. Additionally, edage
AI based smart prefetching scheme can reduce the latency to access
the missing chunks. The goal of this work is to minimize the
latency to access the videos from the users' devices.
1.1. Terminology and Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
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2. System Model
Figure.1 shows the overview system components needed to implement the
proposed scheme. As shown in Figure.2 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.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.
+-----------+ +------------+
| Collected |<----------->| Popularity |
| Data |<- ->| Prediction |
+-----------+ | | +------------+
| |
----- | +---------+
+-------------------+ | ->| | +-------+
| Cache | ---->|Cache |<->|Content|
| +---------------+ |<----->|Decision | ->|Server |
| | Content | | +---------+ | +-------+
| | Storage | | | ^
| +---------------+ | +------------+| |
| +---------------+ |<---->|Prefetching |<- |
| | Prefetching | | --->| Decision | |
| | Buffer | | | +------------+ |
| +---------------+ | | |
+-------------------+ | |
^ | +--------+ |
| -> |Request |<----------
-----------------> |Handler |
+--------+
Figure 1: System Model
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2. Process of Sending Learning Model to predict the popularity
Figure 1 shows that the process of sending the learning models from the
cloud data center to the edge node, where the initial learning models
are constructed at the cloud data center. Then, the edge node utilized
the received learning models to predict the popularity of content and
chunks.
+----------+ +-------+
|Cloud | | Edge |
|Datacenter| | Node |
+----------+ +-------+
| Stage-1 |
| --------------------> |
| +--------------------+ |
| | Send Deeplearning | |
| | Model | |
| +--------------------+ |
| |
| |
Figure 2: Sending Learning model from Cloud Datacenter to Edge
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3. Process of Content retrieving process from the Edge Node
Figure 3 shows that the content retrieving process form the edge node
with the case where the requested chunk of the content is located at
the edge node. When retrieving contents from the user reach a certain
chunk level threshold, the prefetching decision module pre-download
the chunks before requested by users.
+----------+ +-------+ +-------+
| User | | Edge | |Content|
| | | Node | |Server |
+----------+ +-------+ +-------+
| Stage-1 | |
| -------------------------> | |
| +--------------------+ | |
| | Request chunk 1 of| | |
| | content A | | |
| +--------------------+ | |
| Stage-2 | |
| <------------------------ | |
| +---------------------------+ | |
| | Return Content | | |
| | If the requested chunk of | | |
| | content A is in chace | | |
| +---------------------------+ | |
Figure 3: Content retrieving process from the Edge Node
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4.Process of Content retrieving process from the Content Server via
Edge Node
Figure 4 shows that the process of the content retrieving process from
the Content server via edge node with the case where the edge node does
not have the requested chunk of the content. The edge node 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.
+----------+ +-------+ +-------+
| User | | Edge | |Content|
| | | Node | |Server |
+----------+ +-------+ +-------+
| Stage-1 | Stage-2 |
| -------------------------> | -------------------------> |
| +--------------------+ |+---------------------------+ |
| | Request chunk | || Forward the request | |
| | of content A | || because the requested | |
| +--------------------+ || Content is not in cache. | |
| Stage-4 |+---------------------------+ |
| <------------------------ | Stage-3 |
| +---------------------------+ | <------------------------ |
| | Cache (if popular) and | | +-------------------------+ |
| | return requsted chunk of | | | Return requested chunk | |
| | content A | | | of content A | |
| +---------------------------+ | +-------------------------+ |
Figure 4: Content retrieving process from the Content Server via
Edge Node
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5. Process of Content prefetching
Figure 5 shows the process of content prefetching where the edge node
autonomously retrieve the next chunks of the currently requested content
chunk.
+----------+ +-------+ +-------+
| User | | Edge | |Content|
| | | Node | |Server |
+----------+ +-------+ +-------+
| Stage-1 | Stage-2 |
| -------------------------> | -------------------------> |
| +--------------------+ |+-----------------------------+|
| | Request chunk 1 | || Forward the request chunk 1 ||
| | of content B | || and chunk 1+n,cos requested ||
| +--------------------+ || Content is not in cache. ||
| Stage-4 |+-----------------------------+|
| <------------------------ | Stage-3 |
| +---------------------------+ | <------------------------ |
| | Cache and return | | +---------------------------+ |
| | requsted chunk 1 and 1+n | | | Return requested chunk 1 | |
| | of content B | | | and chunk 1+n of content B| |
| +---------------------------+ | +---------------------------+ |
Figure 5: Content prefetching process
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4. IANA Considerations
There are no IANA considerations related to this document.
5. Security Considerations
This note touches communication security as in M2M communications and
COAP protocol.
6. References
6.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
[a] CISCO VNI. Accessed: Feb. 7, 2019.
[b] 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.
[c] 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.
[d] 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-AccessEdge Cache Management," in IEEE Access, vol. 7,
pp. 62734-62749, 2019. doi: 10.1109/ACCESS.2019.2916080
6.2. Informative References
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Authors' Addresses
Choong Seon Hong
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (0)31 201 2532
Email: cshong@khu.ac.kr
Kyi Thar
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (0)31 201 2987
Email: kyithar@khu.ac.kr
Ki Tae Kim
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (0)31 201 2987
Email: glideslope@khu.ac.kr
Seok Won Kang
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (0)31 201 2987
Email: dudtntdud@khu.ac.kr