Internet DRAFT - draft-dwon-t2trg-multiedge-arch
draft-dwon-t2trg-multiedge-arch
Network Working Group D. Kim
Internet-Draft ETRI
Intended status: Informational J-S. Youn
Expires: 25 January 2023 Dong-eui Univ
24 July 2022
Multi-cluster Edge System Architecture and Network Function Requirements
draft-dwon-t2trg-multiedge-arch-02
Abstract
Artificial intelligence based IoT applications demand more massive
computing resource through networks for the process of AI tasks. To
support these applications, some new technologies based an edge
computing and fog computing are emerging. Especially, the
computation-intensive and latency-sensitive IoT applications such as
augmented reality, virtual reality and AI based inference application
is deployed with an edge computing and fog computing which are
connected with cloud computing. Recently, cluster-based edge system
is deployed to extend computation capacity of an edge server. The
cluster-based edge system has the advantage that can enhace the
resource scalability and availability in edge computing and fog
computing. In this draft, we present cluster-based edge system
architecture and multi-cluster edge network topology that consists of
multi-cluster edge system and core cloud. Also, we define the
network functions and network node to configurate and operate multi-
cluster edge network collaboratively.
Status of This Memo
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This Internet-Draft will expire on 25 January 2023.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions and Terminology . . . . . . . . . . . . . . . . . 3
3. The cluster-based edge system architecture and multi-cluster
edge network topology . . . . . . . . . . . . . . . . . . 3
3.1. The cluster-based edge system architecture . . . . . . . 4
3.2. Multi-cluster edge network topology . . . . . . . . . . . 4
4. Collaborative computation service . . . . . . . . . . . . . . 6
5. Network management function of multi-cluster edge system . . 6
6. Resource management function of multi-cluster edge system . . 6
7. High-speed network connection function in multi-cluster edge
network . . . . . . . . . . . . . . . . . . . . . . . . . 6
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 6
9. Security Considerations . . . . . . . . . . . . . . . . . . . 7
10. Normative References . . . . . . . . . . . . . . . . . . . . 7
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 7
1. Introduction
Recently, artificial intelligence (AI) based diverse IoT applications
utilizing computing resources in cloud are emerging. These
applications are deployed with a computation offloading service which
offloads the AI task in IoT devices to a cloud which has the enough
computing resources. However, this centralized processing service is
not suitalbe for latency-sensitive and computing-intensive AI
applications, since the unpredictable delay in the dynamic network
and computing environments may occur due to the network congestion
and the available computing resource may vary dynamically.
Recently, as edge computing or fog computing evolve, some solutions
are emerging to overcome the shortcoming of cloud computing.
Specially, these solutions can quickly offload and deploy tasks for
latency-sensitive and computation-intensive application to edge
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computing server because edge computing and fog computing is
geographically closer to IoT devices and service users. Also, IoT
application can get better quality of service (QoS), such as fast
task response time. This means that edge computing has an advantage
in terms of the development of computation-intensive and latency-
sensitive intelligence IoT applications, such as augmented reality
(AR), virtual reality (VR) and AI based inference
application.[I-D.irtf-t2trg-iot-edge]
Nevertheless, it is difficult for the edge computing itself to
strictly satisfy the quality of service requested in the task due to
the hardware constraints and the consideration of computing power in
the edge computing server. Thus, one solution proposes the
collaborative processing that offloads the part of tasks to the
remote cloud or neighbor edge server. This solution adopts the
collaborative resource allocation in a distributed computing manner
between the edge computing server and the cloud and between the edge
computing servers. Also, to extend the computation capacity of an
edge computing server, cluster-based edge system is deployed and
extended with Kubernetes technology. Kubernetes is an open-source
platform which is optimized for configuring the infrastructures to
deploy the cluster-based edge system. In this draft, we present
cluster-based edge system architecture and multi-cluster edge network
topology that consists of multi-cluster edge system and core cloud.
Also, we define the network functions and network node to configurate
and operate multi-cluster edge network collaboratively.
2. Conventions and Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
3. The cluster-based edge system architecture and multi-cluster edge
network topology
The detailed cluster-based edge system architecture and multi-cluster
edge network topology is presented in this section. The cluster edge
system architecture will be shown below and the definition of each
element in the cluster edge system will be given, and then multi-
cluster edge network topology is shown. Also, the required network
functions and network node will be explained the next section.
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3.1. The cluster-based edge system architecture
The cluster-based edge system architecture is shown in figure below.
The cluster edge system consists of an edge controller (a master
node) and N edge nodes (worker nodes) which can execute an offloaded
computation task and application service provision. At the case of a
computation offloading for tasks, on the procedures of a offloading
task, the mobile node (MN) requests task offloading to the cluster-
based edge system and then the edge controller determines appropriate
edge node (worker) deployed with the application which can perform
the offloaded task with a scheduler. After that, the task offloading
is performed at the selected edge node, the edge controller collects
and then responses the task results to the mobile node requesting
task offloading.
+------------+ +-------------------------------------+
| Device | | |
+------+-----+ | cluster-bsed edge system |
| | |
+-------+--------+ | +------+------+ |
| Access Point +-------------------+ Master | |
+----------------+ +----------------+------+------+ |
| | | | |
| | | +-----------+-----------+ |
+----------------+ | | | | | |
| Access Point +--- | v v v |
+-------+--------+ | +----+----+ +----+----+ +----+----+ |
| | | Worker | | Worker | | Worker | |
+-----+-----+ | +---------+ +---------+ +---------+ |
| Device | | |
+-----------+ +-------------------------------------+
Figure 1: Figure 1: cluster-based edge system
3.2. Multi-cluster edge network topology
The multi-cluster edge network topology is shown in figure below. It
provides an edge network which can support a distributed computing
environment for collaboration among cluster-based edge systems and
between multi-cluster edge systems and core cloud. The following
network functions are required to smoothly provide distributed
computing services in a multi-cluster edge network environment.
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+-----------------+ +------------+
| Management node +---------+ Core cloud |
+---------+-------+ +------------+
|
|
+------------------------+-------------------+
| | |
| +---------+--------+ |
| | Shared storge | |
| +------------------+ |
| |
| |
+--------------+-------------+ +--------------------+----------+
| | | |
| Cluster-edge | | Cluster-edge |
| computing system | | computing system |
| | | |
| +------+------+ | | +------+------+ |
| | Master | | | | Master | |
| +------+------+ | | +------+------+ |
| | | | | |
| +------+-----+ | | +--------+------+ |
| | | | | | | |
| +----+----+ +----+---+ | | +----+---+ +----+---+ |
| | Worker | .. | Worker | | | | Worker | ... | Worker | |
| +---------+ +--------+ | | +--------+ +--------+ |
| | | |
+----------------------------+ +--------------+----------------+
| |
| |
+-------+-------+ +------+-------+
| Access Point | | Access Point |
+-------+-------+ +------+-------+
| |
+------+-----+ +----------+---------+
| | | | |
+----+---+ +----+---+ +----+---+ +----+---+ +---+----+
| Device | | Device | | Device | | Device | | Device |
+--------+ +--------+ +--------+ +--------+ +--------+
Figure 2: Figure 2: Multi cluster edge network topology
* Network management function of multi-cluster edge system
* Resource management function of multi-cluster edge system
* High-speed network connection function in multi-cluster edge
network
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4. Collaborative computation service
In multi-cluster edge network topology, two collaborative computation
models are possible. One is vertical collaborative computation. The
other is horizontal collaborative computation. Vertical
collaborative computation is a collaboration service between a multi-
cluster edge network and the core cloud, and horizontal collaborative
computation is a collaboration service between cluster edge systems
in a multi-cluster edge network. For at all, to provide
collaborative computation, high-speed network connection is required
between cluster edge systems. This can be configurated with a
tunneling protocol. In addition, a storage, or a cache for sharing
data and operating service collaboratively should be configured
between cluster edge systems. Thus, a management function for multi-
cluster edge network management is required. Also, the monitoring
function to monitor resource state in multi-cluster edge network and
when the computation offloading or caching service is required in
multi-cluster edge network, a scheduler and the resource allocation
policy for allocating the resource of multi-cluster edge network is
necessary. And a computation resource, a storage and a cache in
multi-cluster edge network shall be driven and managed
collaboratively. In multi-cluster edge network, the management
function takes a role of management to support the collaborative
computation. The monitoring function takes a role of the collection
of information of current resource state per cluster-based edge
system and the estimation of the collected resource state. The
scheduler takes a role of allocating an edge resource for the
computation offloading or caching service through the resource
allocation policy. Thus, in multi-cluster edge network, the resource
allocation policy shall provide the policy which can support the
collaborative computation model.
5. Network management function of multi-cluster edge system
TBD
6. Resource management function of multi-cluster edge system
TBD
7. High-speed network connection function in multi-cluster edge network
TBD.
8. IANA Considerations
This document contains no requests to IANA.
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9. Security Considerations
TBD.
10. Normative References
[I-D.irtf-t2trg-iot-edge]
Hong, J., Hong, Y., de Foy, X., Kovatsch, M., Schooler,
E., and D. Kutscher, "IoT Edge Challenges and Functions",
Work in Progress, Internet-Draft, draft-irtf-t2trg-iot-
edge-03, 18 August 2021, <https://www.ietf.org/archive/id/
draft-irtf-t2trg-iot-edge-03.txt>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", DOI 10.17487/RFC2119, BCP 14,
RFC 2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", DOI 10.17487/RFC8174, RFC 8174, BCP 14,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
Authors' Addresses
Dae Won Kim
Electronics and Telecommunications Research Institute
218 Gajeongno, Yuseung-gu
Daejeon
Phone: +82 42 860 1624
Email: won22@etri.re.kr
Joo-Sang Youn
DONG-EUI University
176 Eomgwangno Busan_jin_gu
Busan
Phone: +82 51 890 1993
Email: joosang.youn@gmail.com
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