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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Copyright Notice

   Copyright (c) 2022 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
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   provided without warranty as described in the Revised BSD License.

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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