Skip to main content

IoT Edge Challenges and Functions
draft-hong-t2trg-iot-edge-computing-03

The information below is for an old version of the document.
Document Type
This is an older version of an Internet-Draft whose latest revision state is "Replaced".
Authors Jungha Hong , Yong-Geun Hong , Xavier de Foy , Matthias Kovatsch , Eve Schooler , Dirk Kutscher
Last updated 2020-03-02 (Latest revision 2020-01-17)
Replaced by draft-irtf-t2trg-iot-edge
RFC stream (None)
Formats
Additional resources
Stream Stream state (No stream defined)
Consensus boilerplate Unknown
RFC Editor Note (None)
IESG IESG state I-D Exists
Telechat date (None)
Responsible AD (None)
Send notices to (None)
draft-hong-t2trg-iot-edge-computing-03
Network Working Group                                            J. Hong
Internet-Draft                                                 Y-G. Hong
Intended status: Informational                                      ETRI
Expires: 3 September 2020                                      X. de Foy
                                        InterDigital Communications, LLC
                                                             M. Kovatsch
                                    Huawei Technologies Duesseldorf GmbH
                                                             E. Schooler
                                                                   Intel
                                                             D. Kutscher
                               University of Applied Sciences Emden/Leer
                                                            2 March 2020

                   IoT Edge Challenges and Functions
                 draft-hong-t2trg-iot-edge-computing-03

Abstract

   Many IoT applications have requirements that cannot be met by
   the traditional Cloud (aka Cloud computing).  These include time
   sensitivity, data volume, uplink cost, operation in the face of
   intermittent services, privacy and security.  As a result, the IoT is
   driving the Internet toward Edge computing.  This document outlines
   the requirements of the emerging IoT Edge and its challenges.  It
   presents a snapshot of the state-of-the-art, a general model, and
   major components of the IoT Edge, with the goal to provide a common
   base for future discussions in T2TRG and other IETF WGs and RGs.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on 3 September 2020.

Hong, et al.            Expires 3 September 2020                [Page 1]
Internet-Draft             IoT Edge Computing                 March 2020

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 (https://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  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Conventions and Terminology . . . . . . . . . . . . . . . . .   4
   3.  Background  . . . . . . . . . . . . . . . . . . . . . . . . .   4
     3.1.  Internet of Things (IoT)  . . . . . . . . . . . . . . . .   4
     3.2.  Cloud Computing . . . . . . . . . . . . . . . . . . . . .   4
     3.3.  Edge Computing  . . . . . . . . . . . . . . . . . . . . .   5
     3.4.  Example of IoT Edge Computing Use Cases . . . . . . . . .   6
       3.4.1.  Smart Construction  . . . . . . . . . . . . . . . . .   7
       3.4.2.  Smart Grid  . . . . . . . . . . . . . . . . . . . . .   7
       3.4.3.  Smart Water System  . . . . . . . . . . . . . . . . .   8
     3.5.  Common Aspects of Current IoT Edge Computing Service
           Platforms . . . . . . . . . . . . . . . . . . . . . . . .   8
   4.  Challenges for IoT and Impacts of Edge Computing  . . . . . .   9
     4.1.  Time Sensitivity  . . . . . . . . . . . . . . . . . . . .  10
     4.2.  Uplink Cost . . . . . . . . . . . . . . . . . . . . . . .  10
     4.3.  Resilience to Intermittent Services . . . . . . . . . . .  10
     4.4.  Privacy and Security  . . . . . . . . . . . . . . . . . .  11
   5.  IoT Edge Computing Functions  . . . . . . . . . . . . . . . .  11
     5.1.  OAM Components  . . . . . . . . . . . . . . . . . . . . .  13
       5.1.1.  Resources Discovery . . . . . . . . . . . . . . . . .  13
       5.1.2.  Virtualization Management . . . . . . . . . . . . . .  13
       5.1.3.  Authentication and Authorization  . . . . . . . . . .  14
       5.1.4.  Edge Organization and Federation  . . . . . . . . . .  14
     5.2.  Functional Components . . . . . . . . . . . . . . . . . .  14
       5.2.1.  External APIs . . . . . . . . . . . . . . . . . . . .  14
       5.2.2.  Communication Brokering . . . . . . . . . . . . . . .  14
       5.2.3.  In-Network Computation  . . . . . . . . . . . . . . .  15
       5.2.4.  Edge Caching  . . . . . . . . . . . . . . . . . . . .  15
       5.2.5.  Other Services  . . . . . . . . . . . . . . . . . . .  15
     5.3.  Application Components  . . . . . . . . . . . . . . . . .  16
       5.3.1.  IoT End Devices Management  . . . . . . . . . . . . .  16
       5.3.2.  Data Management . . . . . . . . . . . . . . . . . . .  16

Hong, et al.            Expires 3 September 2020                [Page 2]
Internet-Draft             IoT Edge Computing                 March 2020

     5.4.  Simulation and Emulation Environments . . . . . . . . . .  16
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  17
   7.  Acknowledgement . . . . . . . . . . . . . . . . . . . . . . .  17
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  17
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  17
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  17
   Appendix A.  Overview of the IoT Edge Computing . . . . . . . . .  20
     A.1.  Open Source Projects  . . . . . . . . . . . . . . . . . .  21
       A.1.1.  Gateway/CPE Platforms . . . . . . . . . . . . . . . .  21
       A.1.2.  Edge Cloud Management Platforms . . . . . . . . . . .  22
       A.1.3.  Related Projects  . . . . . . . . . . . . . . . . . .  23
     A.2.  Products  . . . . . . . . . . . . . . . . . . . . . . . .  23
       A.2.1.  IoT Gateways  . . . . . . . . . . . . . . . . . . . .  23
       A.2.2.  Edge Cloud Platforms  . . . . . . . . . . . . . . . .  24
     A.3.  Standards Initiatives . . . . . . . . . . . . . . . . . .  24
       A.3.1.  ETSI Multi-access Edge Computing  . . . . . . . . . .  24
       A.3.2.  Edge Computing Support in 3GPP  . . . . . . . . . . .  25
       A.3.3.  OpenFog and Industrial Internet Consortium  . . . . .  26
       A.3.4.  Related Standards . . . . . . . . . . . . . . . . . .  26
     A.4.  Research Projects . . . . . . . . . . . . . . . . . . . .  26
       A.4.1.  Named Function Networking . . . . . . . . . . . . . .  26
       A.4.2.  5G-CORAL  . . . . . . . . . . . . . . . . . . . . . .  27
       A.4.3.  FLAME . . . . . . . . . . . . . . . . . . . . . . . .  28
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  28

1.  Introduction

   Currently, many IoT services leverage the Cloud, since it can provide
   virtually unlimited storage and processing power.  The reliance of
   IoT on  back-end cloud computing brings additional advantages such as
   flexibility and efficiency.  Today's IoT systems are fairly static
   with respect to integrating and supporting computation.  It's not
   that there is no computation, but systems are often limited to static
   configurations (edge gateways, the Cloud).

   However, IoT devices are creating vast amounts of data at the network
   edge.  To meet IoT use case requirements, that data increasingly is
   being stored, processed, analyzed, and acted upon close to the data
   producers.  These requirements include time sensitivity, data volume,
   uplink cost, resiliency in the face of intermittent connectivity,
   privacy, and security, which cannot be addressed by today's
   centralized cloud computing.  These requirements suggest a more
   flexible way to distribute computing (and storage) and to integrate
   it in the edge-cloud continuum.  We will refer to this integration of
   edge computing and IoT as "IoT edge computing".  Our draft describes
   uses cases, challenges, a proposed system model and derived
   functional components.

Hong, et al.            Expires 3 September 2020                [Page 3]
Internet-Draft             IoT Edge Computing                 March 2020

2.  Conventions and Terminology

   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 [RFC2119].

3.  Background

3.1.  Internet of Things (IoT)

   Since the term "Internet of Things" (IoT) was coined by Kevin Ashton
   in 1999 working on Radio-Frequency Identification (RFID) technology
   at the Auto-ID Center of the Massachusetts Institute of Technology
   (MIT) [Ashton], the concept of IoT had been broadened to reflect the
   vision of connecting the physical world to the virtual world of
   computers using (wireless) sensor networks with any kind of
   technology with which the Things can send and receive information
   without human intervention.  Recently, the term has become more
   literal by actually connecting Things to the Internet and converging
   on Internet and also Web technology.  Things are usually embedded
   systems of various kinds, such as home appliances, mobile equipment,
   wearable devices, etc.  Things are widely distributed, but typically
   have limited storage and processing power, which raise concerns
   regarding reliability, performance, energy consumption, security, and
   privacy [Lin].

3.2.  Cloud Computing

   Cloud computing has been defined in [NIST]: "cloud computing is a
   model for enabling ubiquitous, convenient, on-demand network access
   to a shared pool of configurable computing resources (e.g., networks,
   servers, storage, applications, and services) that can be rapidly
   provisioned and released with minimal management effort or service
   provider interaction".  Cloud computing has been a predominant
   technology which has virtually unlimited capacity in terms of storage
   and processing power.  The availability of virtually unlimited
   storage and processing capabilities at low cost enabled the
   realization of a new computing model, in which virtualized resources
   can be leased in an on-demand fashion, being provided as general
   utilities.  Companies like Amazon, Google, Facebook, etc. widely
   adopted this paradigm for delivering services over the Internet,
   gaining both economical and technical benefits [Botta].

   Now with IoT, we will reach the era of post-clouds where
   unprecedented volume and variety of data will be generated by things
   at edge/local networks and many applications will be deployed on the
   edge networks to consume these IoT data.  Some of the applications
   may need very short response times, some may contain personal data,

Hong, et al.            Expires 3 September 2020                [Page 4]
Internet-Draft             IoT Edge Computing                 March 2020

   and others may generate vast amounts of data.  Today's cloud-based
   service models are not suitable for these applications.

   It is predicted that by 2019, 45% of the data created in IoT will be
   stored, processed, analyzed and acted close to, or at the edge of the
   network and about 50 billion devices will connect to the Internet by
   2020 [Evans].  So, moving all data from edge/local networks to the
   cloud data center may not be an efficient way anymore to process vast
   amounts of data.

   In cloud computing, users traditionally only consumed IoT data
   through cloud services.  Now, however, users are also producing IoT
   data with their mobile devices.  This change requires even more
   functionality at edge/local networks [Shi], to support mobile edge
   computing considerations.

3.3.  Edge Computing

   Edge computing, under certain aspects also referred to as fog
   computing, is a new paradigm in which substantial computing and
   storage resources are placed at the edge of the Internet, that is, in
   close proximity to mobile devices, sensors, actuators, or machines,
   so that computing happens near data sources [Mahadev], or closer
   (topologically, physically, in term of latency, etc.) to where
   decisions or interactions with the physical world are happening.  It
   works on both downstream data on behalf of cloud services and
   upstream data on behalf of IoT services.

   An edge device is any computing or networking resource residing
   between data sources and cloud-based datacenters.  In edge computing,
   end devices not only consume data, but also produce data.  And at the
   network edge, devices not only request services and information from
   the cloud, but also handle computing tasks including processing,
   storage, caching, and load balancing on data sent to and from the
   cloud [Shi].  This does not preclude end devices from hosting
   computation themselves when possible, independently or as part of a
   distributed edge computing platform.

   The definition of edge computing from ISO is a "form of distributed
   computing in which significant processing and data storage takes
   place on nodes which are at the edge of the network" [ISO_TR].
   ETSI's definition of multi-access edge computing is a "system which
   provides an IT service environment and cloud-computing capabilities
   at the edge of an access network which contains one or more type of
   access technology, and in close proximity to its users"
   [ETSI_MEC_01].

Hong, et al.            Expires 3 September 2020                [Page 5]
Internet-Draft             IoT Edge Computing                 March 2020

   The similar concept of fog computing from the Industrial Internet
   Consortium (formerly OpenFog) is "a horizontal, system-level
   architecture that distributes computing, storage, control and
   networking functions closer to the users along a cloud-to-thing
   continuum" Appendix A.3.3.  The term fog computing usually represents
   the notion of a multi-tiered edge computing, that is, several layers
   of compute infrastructure between the end devices and the cloud.

   Based on these definitions, we can summarize a general philosophy of
   edge computing as to distribute the required functions close to users
   and data, while the difference to classic local systems is the usage
   of management and orchestration features adopted from cloud
   computing.

   Actors from various industries approach edge computing using
   different terms and reference models, although in practice these
   approaches are not incompatible and may integrate with each other:

   *  The telecommunication industry tends to use a model where edge
      computing services are deployed over NFV infrastructure at
      aggregation points, or in proximity to the user equipment (e.g.,
      gNodeBs) Appendix A.3.1.

   *  Enterprise and campus solutions often interpret edge computing as
      an "edge cloud", that is, a smaller data center directly connected
      to the local network (often referred to as "on-premise").

   *  The automation industry defines the edge as the connection point
      between IT from OT (Operational Technology).  Hence, here edge
      computing sometimes referres to applying IT solutions to OT
      problems such as analytics, more flexible user interfaces, or
      simply having more compute power than an automation controller.

   It is clear that the combination of these models leads to a multi-
   tier edge computing solution as mentioned above.

3.4.  Example of IoT Edge Computing Use Cases

   IoT edge computing can be used in home, industry, grid, healthcare,
   city, transportation, agriculture, and/or education scenarios.  We
   discuss here only a few examples of such use cases, to point out
   differentiating requirements.

Hong, et al.            Expires 3 September 2020                [Page 6]
Internet-Draft             IoT Edge Computing                 March 2020

3.4.1.  Smart Construction

   In traditional construction domain, heavy equipment and machinery
   pose risks to humans and property.  Thus, there have been many
   attempts to deploy technology to protect lives and property in
   construction sites.  For example, measurements of noise, vibration,
   and gas can be recorded on a remote server and reported to an
   inspector.  Today, data produced by such measurements is collected by
   a local gateway and transferred to a remote server.  This incurs
   transmission costs, e.g., over a LTE connection, and storage costs,
   e.g., when using Amazon Web Services.  When an inspector needs to
   investigate an incident, he checks the information stored on a
   server.

   If we leverage IoT edge computing, sensor data can be processed and
   analyzed on a gateway located within or near a construction site.
   And with the help of statistical analysis or machine learning
   technologies, we can predict future incidents in advance and this
   prediction can trigger an on-site alarm and a notification to an
   inspector.

   To determine the exact cause of an incident, sensor data including
   audio and video are transferred to a remote server.  In this case,
   audio and video data volume is typically very large and the cost of
   transmission can be an issue.  Edge computing can be leveraged to
   predict the time of an incident, which can reduce the volume of
   transmitted data; while during a normal time period audio and video
   data may be transmitted with a low resolution, during an emergency,
   this transmission may use a high resolution.  This adjustment can
   reduce transmission cost significantly.

3.4.2.  Smart Grid

   In future smart city scenarios, the Smart Grid will be critical in
   ensuring highly available/efficient energy control in city-wide
   electricity management.  Edge computing is expected to play a
   significant role in those systems to improve transmission efficiency
   of electricity; to react and restore power after a disturbance; to
   reduce operation costs and reuse renewable energy effectively, since
   these operations involve local decision making.  In addition, edge
   computing can help monitoring power generation and power demand, and
   making electrical energy storage decisions in the smart grid system.

Hong, et al.            Expires 3 September 2020                [Page 7]
Internet-Draft             IoT Edge Computing                 March 2020

3.4.3.  Smart Water System

   The water system is one of the most important aspects of a city.
   Effective use of water, and cost-effective and environment-friendly
   water treatment are critical aspects of this system.  They can be
   facilitated by edge computing in smart water systems, to help monitor
   water consumption, transportation and prediction of future water use.
   For example, water harvesting and ground water monitoring will be
   supported through edge computing.  Edge computing will also enable
   locally analyzing collected information related to water control and
   management, and limit water losses.

3.5.  Common Aspects of Current IoT Edge Computing Service Platforms

   This section provides an overview of today's IoT edge computing
   field, based on a limited review of standards, research, open-source
   and proprietary products in Appendix A.

   IoT gateways (Appendix A.2.1, Appendix A.1.1) represent a common
   class of IoT edge computing products, where the gateway is providing
   a local service on customer premises, and is remotely managed through
   a cloud service.  IoT communication protocols are typically used
   between IoT devices and the gateway, including CoAP, MQTT and many
   specialized IoT protocols (such as OPC UA and DDS in the Industrial
   IoT space), while the gateway communicates with the distant cloud
   typically using HTTPS.  Virtualization platforms enable the
   deployment of virtual edge computing functions (as VMs, application
   containers, etc.), including IoT gateway software, on servers in the
   mobile network infrastructure (at base station and concentration
   points), in edge datacenters (in central offices) or regional
   datacenters located near central offices.  End devices are envisioned
   to become computing devices in forward looking projects, but are not
   commonly used as such today.

   Physical or virtual IoT gateways can host application programs, which
   are typically built using an SDK to access local services through a
   programmatic API.  Edge cloud system operators host their customers'
   applications VMs or containers on servers located in or near access
   networks, which can implement local edge services.  For example,
   mobile networks can provide edge services for radio network
   information, location and bandwidth management.

   Life cycle management of services and applications on physical IoT
   gateways is often cloud-based.  Edge cloud management platforms and
   products (Appendix A.1.2, Appendix A.2.2) adapt cloud management
   technologies (e.g., Kubernetes) to the edge cloud, i.e., to smaller,
   distributed computing devices running outside a controlled data

Hong, et al.            Expires 3 September 2020                [Page 8]
Internet-Draft             IoT Edge Computing                 March 2020

   center.  Services and application life-cycle is typically using a
   NFV-like management and orchestration model.

   The platform typically includes services to advertise or consume APIs
   (e.g., Mp1 interface in ETSI MEC supports service discovery and
   communication), and enables communicating with local and remote
   endpoints (e.g., message routing function in IoT gateways).  The
   service platform is typically extensible by edge applications, since
   they can advertise an API that other edge applications can consume.
   IoT communication services include protocols translation, analytics
   and transcoding.  Communication between edge computing devices is
   enabled in tiered deployments or distributed deployments.

   An edge cloud platform may enable pass-through without storage or
   local storage (e.g., on IoT gateways).  Some edge cloud platforms use
   a distributed form of storage such as an ICN network (e.g., NFN nodes
   can store data in NDN, Appendix A.4.1) or a distributed storage
   platform (e.g., such as Ceph, Appendix A.1.2).  External storage,
   e.g., on databases in distant or local IT cloud, is typically used
   for filtered data deemed worthy of long term storage, or in some
   cases for all data, for example when required for regulatory reasons.

   Stateful computing is supported on platforms hosting native programs,
   VMs or containers.  Stateless computing is supported on platforms
   providing a "serverless computing" service (a.k.a. function-as-
   a-service), or on systems based on named function networking.

   In many IoT use cases, a typical network usage pattern is high volume
   uplink with some form of traffic reduction enabled by processing over
   edge computing devices.  Alternatives to traffic reduction include
   deferred transmission (to off-peak hours or using physical shipping).
   Downlink traffic includes application control and software updates.
   Other, downlink-heavy traffic patterns are not excluded but are more
   often associated with non-IoT usage (e.g., video CDNs).

4.  Challenges for IoT and Impacts of Edge Computing

   As the IoT is maturing, systems are converging, deployments are
   growing, and IoT technology is used with more and more demanding
   applications such as industrial, automotive, or healthcare.  This
   leads to new challenges for the network infrastructure.  In
   particular, the amount of data created at the edge is expected to be
   vast.  Industrial machines such as laser cutters already produce over
   1 terabyte per hour, the same applies for autonomous cars [NVIDIA].
   90% of IoT data is expected to be stored, processed, analyzed, and
   acted upon close to the source [Kelly], as cloud computing models
   alone cannot address the new challenges [Chiang].  Below we discuss
   IoT use case requirements that are moving cloud capabilities to be

Hong, et al.            Expires 3 September 2020                [Page 9]
Internet-Draft             IoT Edge Computing                 March 2020

   more proximate and more distributed and disaggregated.  Beyond
   addressing those requirements, however, edge computing can also bring
   flexibility to classic networking functions such as protocol
   translation in gateways.

4.1.  Time Sensitivity

   Many industrial control systems, such as manufacturing systems, smart
   grids, oil and gas systems, etc., often require stringent end-to-end
   latency between the sensor and control node.  While some IoT
   applications may require latency below a few tens of milliseconds
   [Weiner], industrial robots and motion control systems have use cases
   for cycle times in the order of microseconds [_60802].  An important
   aspect for real-time communications is not only the latency, but also
   guarantees for jitter.  This means control packets need to arrive
   with as little variation as possible with a strict deadline.  Given
   the best-effort characteristics of the Internet, this challenge is
   virtually impossible to address, without comprehending end-to-end
   guarantees for individual message delivery and continuous data flows.

4.2.  Uplink Cost

   Many IoT deployments are not challenged by a constrained network
   bandwidth to the cloud.  The fifth generation mobile networks (5G)
   and Wi-Fi 6 both theoretically top out at 10 gigabits per second
   (i.e., 4.5 terabyte per hour), which enables high-bandwidth uplinks.
   However, the resulting cost for high-bandwidth connectivity to upload
   all data to the cloud is unjustifiable and impractical for most IoT
   applications.  In some settings, e.g. in aeronautical communication,
   higher communication costs reduce the amount of data that can be
   practically uploaded even further.

4.3.  Resilience to Intermittent Services

   Many IoT devices such as sensors, data collectors, actuators,
   controllers, etc. have very limited hardware resources and cannot
   rely solely on their limited resources to meet all their computing
   and/or storage needs.  They require reliable, uninterrupted or
   resilient services to augment their capabilities in order to fulfill
   their application tasks.  This is hard and partly impossible to
   achieve with cloud services for systems such as vehicles, drones, or
   oil rigs that have intermittent network connectivity.

Hong, et al.            Expires 3 September 2020               [Page 10]
Internet-Draft             IoT Edge Computing                 March 2020

4.4.  Privacy and Security

   When IoT services are deployed at home, personal information can be
   learned from detected usage data.  For example, one can extract
   information about employment, family status, age, and income by
   analyzing smart meter data [ENERGY].  Policy makers started to
   provide frameworks that limit the usage of personal data and put
   strict requirements on data controllers and processors.  However,
   data stored indefinitely in the cloud also increases the risk of data
   leakage, for instance, through attacks on rich targets.

   Industrial systems are often argued to not have privacy implications,
   as no personal data is gathered.  Yet data from such systems is often
   highly sensitive, as one might be able to infer trade secrets such as
   the setup of production lines.  Hence, the owner of these systems are
   generally reluctant to upload IoT data to the cloud.

5.  IoT Edge Computing Functions

   Edge computing is expected to play an important role in deploying new
   IoT services integrated with Big Data and AI, enabled by flexible in-
   network computing platforms.  Although there are lots of approaches
   to edge computing, we attempt to lay out a general model and list
   associated logical functions in this section.  In practice, this
   model can map to different architectures, such as:

   *  A single IoT gateway, or a hierarchy of IoT gateways, typically
      connected to the cloud (e.g., to extend the traditionally cloud-
      based management of IoT devices and data to the edge).  A common
      role of an IoT Gateway is to provide access to an heterogeneous
      set of IoT devices/sensors; handle IoT data; and deliver IoT data
      to its final destination in a cloud network.  Whereas an IoT
      gateway needs interactions with cloud like as conventional cloud
      computing, it can also operate independently.

   *  A set of distributed computing nodes, e.g., embedded in switches,
      routers, edge cloud servers or mobile devices.  In the future,
      some IoT end devices may have enough computing capabilities to
      participate in such distributed systems.  In this model, edge
      computing nodes can collaborate with each other to share their
      resources.

Hong, et al.            Expires 3 September 2020               [Page 11]
Internet-Draft             IoT Edge Computing                 March 2020

                +---------------------+
                |   Remote network    |  +---------------+
                |(e.g., cloud network)|  |   Service     |
                +-----------+---------+  |   Operator    |
                            |            +------+--------+
                            |                   |
             +--------------+-------------------+-----------+
             |            Edge Computing Domain             |
             |                                              |
             |   Computing Nodes (edge or end devices)      |
             |                                              |
             |   OAM Components                             |
             |   - Resources Discovery                      |
             |   - Virtualization Management                |
             |   - Authentication                           |
             |   - Edge Organization and Federation         |
             |   - ...                                      |
             |                                              |
             |   Functional Components                      |
             |   - External APIs                            |
             |   - Communication Brokering                  |
             |   - In-Network Computation                   |
             |   - Edge Caching                             |
             |   - Other Services                           |
             |   - ...                                      |
             |                                              |
             |   Application Components                     |
             |   - IoT End Devices Management               |
             |   - Data Management                          |
             |   - ...                                      |
             |                                              |
             +------+--------------+-------- - - - -+- - - -+
                    |              |       |        |       |
                    |              |          +-----+--+
               +----+---+    +-----+--+    |  |compute |    |
               |  End   |    |  End   | ...   |node/end|
               |Device 1|    |Device 2| ...|  |device n|    |
               +--------+    +--------+       +--------+
                                           + - - - - - - - -+

                   Figure 1: Model of IoT Edge Computing

   In this general model, the edge computing domain is interconnected
   with IoT end devices (southbound connectivity) and possibly with a
   remote/cloud network (northbound connectivity), and with a service
   operator's system.  Edge computing nodes provide multiple logical
   functions, or components, which may not all be present in a given
   system.  They may be implemented in a centralized or distributed

Hong, et al.            Expires 3 September 2020               [Page 12]
Internet-Draft             IoT Edge Computing                 March 2020

   fashion, in the edge network, or through some interworking between
   the edge network and a remote cloud network.

   We now attempt to enumerate major edge computing domain components.
   They are here loosely organized into OAM, functional and application
   components, with the understanding that the distinction between these
   classes may not always be clear, depending on actual system
   architectures.

5.1.  OAM Components

   Edge computing OAM goes beyond the network-related OAM functions
   listed in [RFC6291].  Besides infrastructure (network, storage and
   computing resources), edge computing systems can also include
   computing environments (for VMs, software containers, functions), IoT
   end devices, data and code.

   Operation related functions include performance monitoring for
   service level agreement measurement; fault management and
   provisioning for links, nodes, compute and storage resources,
   platforms and services.  Administration covers network/compute/
   storage resources, platforms and services discovery, configuration
   and planning.  Management covers monitoring and diagnostics of
   failures, as well as means to minimize their occurrence and take
   corrective actions.  This may include software updates management,
   high service availability through redundancy and multipath
   communication.

   We further detail a few OAM components.

5.1.1.  Resources Discovery

   This component is about finding infrastructure resources, such as
   compute, network and storage, but also other resources such as IoT
   end devices, sensors, data, code, or services.

5.1.2.  Virtualization Management

   Some IoT edge computing systems make use of virtualized (compute,
   storage and networking) resources, which need to be allocated and
   configured.

Hong, et al.            Expires 3 September 2020               [Page 13]
Internet-Draft             IoT Edge Computing                 March 2020

5.1.3.  Authentication and Authorization

   This can cover authenticating platforms, end devices, data, code
   units and applications or users interacting with the system.  Today,
   centralized gateway-based systems rely for device authentication on
   the installation of a secret on IoT end devices and on computing
   devices (e.g., a device certificate stored in a hardware security
   module).

5.1.4.  Edge Organization and Federation

   In a distributed system context, once edge devices have discovered
   and authenticated each other, they can be organized, or self-
   organize, into hierarchies or clusters.  Such groups can also form
   federations with other edge or remote clouds.

5.2.  Functional Components

5.2.1.  External APIs

   An IoT edge cloud may provide a northbound data plane or management
   plane interface to a remote network, e.g., a cloud, home or
   enterprise network.  This interface does not exist in standalone
   (local-only) scenarios.  To support such an interface when it exists,
   an edge computing component needs to expose an API, deal with
   authentication and authorization, support secure communication.

   An IoT edge cloud may provide an API or interface to local users
   (e.g., to facilitate local management), or to mobile users (e.g., to
   provide access to services and applications, or to manage data
   published by the mobile device).

5.2.2.  Communication Brokering

   A typical function of IoT edge computing is to facilitate
   communication with IoT end devices: for example, enable clients to
   register as recipients for data from devices, as well as forwarding/
   routing of traffic to or from IoT end devices, enabling various data
   discovery and redistribution patterns, e.g., north-south with clouds,
   east-west with other edge devices [DATA-DISCOVERY].  Another aspect
   of a communication component is dispatching of alerts and
   notifications to interested consumers both inside and outside of the
   edge computing domain.  Protocol translation, analytics and
   transcoding may also be performed when necessary.

   Communication brokering may be centralized in some systems, e.g.,
   using a hub-and-spoke message broker, or distributed like with
   message buses, possibly in a layered bus approach.  Distributed

Hong, et al.            Expires 3 September 2020               [Page 14]
Internet-Draft             IoT Edge Computing                 March 2020

   systems may leverage direct communication between end devices and
   communication devices, such as device-to-device links.  Brokers
   functions can include ensuring communication reliability,
   traceability, and in some cases transaction management.

   QoS can be provided in some systems through the combination of
   network QoS (e.g., traffic engineering or wireless resource
   scheduling) and compute/storage resource allocations.  In some
   systems a bandwidth manager service can be exposed to enable
   allocation of bandwidth to/from an edge computing application
   instance.

5.2.3.  In-Network Computation

   A core function of IoT edge computing is to enable computation
   offloading, i.e., to perform computation on an edge node on behalf of
   a device or user.  The support for in-network computation may vary in
   term of capability, e.g., computing nodes can host a virtual machine
   able run stateful or stateless code, or a rule engine providing an
   API to register actions in response to conditions such as IoT device
   ID, sensor values to check, thresholds, etc.  Computation offloading
   includes orchestration or application lifecycle related aspects, such
   as: selecting an appropriate compute device based on available
   resources, compute node properties, etc., and with varying goals
   including for example load balancing and energy conservation;
   onboarding code on a platform or compute device; assisted or
   automatic partitioning of code; invoking remote code execution;
   relocating an instance from one compute node to another; session
   continuity when communicating with mobile end devices.

5.2.4.  Edge Caching

   A purpose of local caching may be to enable local data processing
   (e.g., pre-processing or analysis), or to enable delayed virtual or
   physical shipping.  A responsibility of the edge caching component is
   to manage data persistence, e.g., to schedule removal of data when it
   is no longer needed.  Another aspect of this component may be to
   authenticate and encrypt data.  It can for example take the form of a
   distributed storage system, and deal with related issues, e.g.,
   reaching and maintaining data consistency; enabling efficient access
   to data, for example using some form of sharding.

5.2.5.  Other Services

   Data generated by IoT devices and associated information obtained
   from the access network may be used to provide high level services
   such as end device location, radio network information and bandwidth
   management.

Hong, et al.            Expires 3 September 2020               [Page 15]
Internet-Draft             IoT Edge Computing                 March 2020

5.3.  Application Components

5.3.1.  IoT End Devices Management

   IoT end device management includes managing information about the IoT
   devices, including their sensors, how to communicate with them, etc.
   Edge computing addresses the scalability challenges from the massive
   number of IoT end devices and IoT data value by separating the
   scalability domain into edge/local networks and remote network.

5.3.2.  Data Management

   With regard to the high level challenges listed in Section 4, data
   storage and processing at the edge is a major aspect of IoT edge
   computing.  Data may therefore need to be classified (e.g., in terms
   of privacy, importance, validity, etc.).  Data analysis such as
   performed in AI/ML tasks performed at the edge may benefit from
   specialized hardware support on computing nodes.  IoT edge computing
   will face challenges in term of, for example, programmability,
   naming, data abstraction, data service management and data discovery
   (discussed in communication brokering).  Furthermore, while edge
   computing can support IoT services independently of cloud computing,
   it can also be connected to cloud computing.  Thus, the relationship
   of IoT edge computing to cloud computing, with regard to data
   management, is another potential challenge [ISO_TR].

5.4.  Simulation and Emulation Environments

   IoT Edge Computing brings new challenges to simulation and emulation
   tools used by researchers and developers.  A varied set of
   applications, network and computing technologies can coexist in a
   distributed system, which make modelling difficult.  Scale, mobility
   and resource management are additional challenges [SimulatingFog].

   Tools include simulators, where simplified application logic runs on
   top of a fog network model, and emulators, where actual applications
   can be deployed, typically in software containers, over a cloud
   infrastructure (e.g.  Docker, Kubernetes) itself running over a
   network emulating edge network conditions such as variable delays,
   throughput and mobility events.  To gain in scale, emulated and
   simulated systems can be used together in hybrid federation-based
   approaches [PseudoDynamicTesting], while to gain in realism physical
   devices can be interconnected with emulated systems.  Examples of
   related work and platforms include the publicly accessible MEC
   sandbox work recently initiated in ETSI [ETSI_Sandbox], and open
   source simulators and emulators ([AdvantEDGE] emulator and tools
   cited in [SimulatingFog]).

Hong, et al.            Expires 3 September 2020               [Page 16]
Internet-Draft             IoT Edge Computing                 March 2020

6.  Security Considerations

   T.B.D.

7.  Acknowledgement

   The authors would like to thank Joo-Sang Youn, Akbar Rahman, Michel
   Roy, Robert Gazda and Rute Sofia for their valuable comments and
   suggestions on this document.

8.  References

8.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

8.2.  Informative References

   [AdvantEDGE]
              "Mobile Edge Emulation Platform", Source Code Repository ,
              March 2020,
              <https://github.com/InterDigitalInc/AdvantEDGE>.

   [Ashton]   Ashton, K., "That Internet of Things thing", RFID J. vol.
              22, no. 7, pp. 97-114 , 2009.

   [Botta]    Botta, A., Donato, W., Persico, V., and A. Pescape,
              "Integration of Cloud Computing and Internet of Things: A
              survey", Future Gener. Comput. Syst., vol. 56, pp.
              684-700 , 2016.

   [Chiang]   Chiang, M. and T. Zhang, "Fog and IoT: An overview of
              research opportunities", IEEE Internet Things J., vol. 3,
              no. 6, pp. 854-864 , 2016.

   [DATA-DISCOVERY]
              McBride, M., Kutscher, D., Schooler, E., and CJ.
              Bernardos, "Edge Data Discovery for COIN", Work in
              Progress, Internet-Draft, draft-mcbride-edge-data-
              discovery-overview, March 2020,
              <https://tools.ietf.org/html/draft-mcbride-edge-data-
              discovery-overview>.

   [ENERGY]   Beckel, C., Sadamori, L., Staake, T., and S. Santini,

Hong, et al.            Expires 3 September 2020               [Page 17]
Internet-Draft             IoT Edge Computing                 March 2020

              "Revealing Household Characteristics from Smart Meter
              Data", Energy, vol. 78, pp. 397-410 , 2014.

   [ETSI_MEC_01]
              ETSI, ., "Multi-access Edge Computing (MEC); Terminology",
              ETSI GS 001 , 2019, <https://www.etsi.org/deliver/etsi_gs/
              MEC/001_099/001/02.01.01_60/gs_MEC001v020101p.pdf>.

   [ETSI_MEC_02]
              ETSI, ., "Multi-access Edge Computing (MEC); Phase 2: Use
              Cases and Requirements", ETSI GS 002 , 2016,
              <https://www.etsi.org/deliver/etsi_gs/
              MEC/001_099/002/02.01.01_60/gs_MEC002v020101p.pdf>.

   [ETSI_MEC_03]
              ETSI, ., "Mobile Edge Computing (MEC); Framework and
              Reference Architecture", ETSI GS 003 , 2019,
              <https://www.etsi.org/deliver/etsi_gs/
              MEC/001_099/003/02.01.01_60/gs_MEC003v020101p.pdf>.

   [ETSI_MEC_WP_28]
              ETSI, ., "MEC in 5G networks", White Paper , 2018,
              <https://www.etsi.org/images/files/ETSIWhitePapers/
              etsi_wp28_mec_in_5G_FINAL.pdf>.

   [ETSI_Sandbox]
              "Multi-access Edge Computing (MEC) MEC Sandbox Work Item",
              Portal , March 2020,
              <https://portal.etsi.org/webapp/WorkProgram/
              Report_WorkItem.asp?WKI_ID=57671>.

   [Evans]    Evans, D., "The Internet of Things: How the next evolution
              of the Internet is changing everything", CISCO White
              Paper , 2011.

   [FLAME]    Horizon 2020 Programme, ., "FLAME Project", Portal , 2019,
              <https://www.ict-flame.eu/>.

   [IEEE-1934]
              IEEE, ., "FOG - Fog Computing and Networking Architecture
              Framework", Portal , 2019,
              <https://standards.ieee.org/standard/1934-2018.html>.

   [ISO_TR]   "Information Technology - Cloud Computing - Edge Computing
              Landscape", ISO/IEC TR 23188 , 2018.

   [Kelly]    Kelly, R., "Internet of Things Data to Top 1.6 Zettabytes
              by 2022", 2016,

Hong, et al.            Expires 3 September 2020               [Page 18]
Internet-Draft             IoT Edge Computing                 March 2020

              <https://campustechnology.com/articles/2015/04/15/
              internet-of-thingsdata-to-top-1-6-zettabytes-by-
              2020.aspx>.

   [Lin]      Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., and W.
              Zhao, "A survey on Internet of Things: Architecture,
              enabling technologies, security and privacy, and
              applications", IEEE Internet of Things J., vol. 4, no. 5,
              pp. 1125-1142 , 2017.

   [Linux_Foundation_Edge]
              Linux Foundation, ., "Linux Foundation Edge", Portal ,
              2019, <https://www.lfedge.org/>.

   [Mahadev]  Satyanarayanan, M., "The Emergence of Edge Computing",
              Computer, vol. 50, no. 1, pp. 30-39 , 2017.

   [NIST]     Mell, P. and T. Grance, "The NIST definition of Cloud
              Computing", Natl. Inst. Stand. Technol, vol. 53, no. 6, p.
              50 , 2009.

   [NVIDIA]   Grzywaczewski, A., "Training AI for Self-Driving Vehicles:
              the Challenge of Scale", NVIDIA Developer Blog , 2017,
              <https://devblogs.nvidia.com/training-self-driving-
              vehicles-challenge-scale/>.

   [OpenEdgeComputing]
              "Open Edge Computing", Portal , 2019,
              <http://openedgecomputing.org/>.

   [OpenFog]  "OpenFog Reference Architecture for Fog Computing",
              OpenFog Consortium , 2017.

   [POINT]    Horizon 2020 Programme, ., "IP Over ICN - the better IP
              (POINT) Project", Portal , 2019,
              <https://www.point-h2020.eu/>.

   [PseudoDynamicTesting]
              Ficco, M., Esposito, C., Xiang, Y., and F. Palmieri,
              "Revealing Household Characteristics from Smart Meter
              Data", IEEE Communications Magazine, Nov. 2017 , 2017.

   [RFC6291]  Andersson, L., van Helvoort, H., Bonica, R., Romascanu,
              D., and S. Mansfield, "Guidelines for the Use of the "OAM"
              Acronym in the IETF", BCP 161, RFC 6291,
              DOI 10.17487/RFC6291, June 2011,
              <https://www.rfc-editor.org/info/rfc6291>.

Hong, et al.            Expires 3 September 2020               [Page 19]
Internet-Draft             IoT Edge Computing                 March 2020

   [Shi]      Shi, W., Cao, J., Zhang, Q., Li, Y., and L. Xu, "Edge
              computing: vision and challenges", IEEE Internet of Things
              J., vol. 3, no. 5, pp. 637-646 , 2016.

   [Sifalakis]
              Sifalakis, M., Kohler, B., Scherb, C., and C. Tschudin,
              "An Information Centric Network for Computing the
              Distribution of Computations", Proceedings of the 1st
              International Conference on Information-centric networking
              (INC) , 2014.

   [SimulatingFog]
              Svorobej, S. and . al, "Simulating Fog and Edge Computing
              Scenarios: An Overview and Research Challenges", MPDI
              Future Internet 2019 , 2019.

   [StarlingX]
              OpenStack Foundation, ., "StarlingX", Portal , 2019,
              <https://www.starlingx.io/>.

   [Weiner]   Weiner, M., Jorgovanovic, M., Sahai, A., and B. Nikolie,
              "Design of a low-latency, high-reliability wireless
              communication system for control applications", IEEE Int.
              Conf. Commun. (ICC), Sydney, NSW, Australia, pp.
              3829-3835 , 2014.

   [_3GPP.23.501]
              3GPP, ., "System Architecture for the 5G System", 3GPP TS
              23.501 , 2019,
              <http://www.3gpp.org/ftp/Specs/html-info/23501.htm>.

   [_5G-CORAL]
              Horizon 2020 Programme, ., "5G Convergent Virtualised
              Radio Access Network Living at the Edge (5G-CORAL)
              Project", Portal , 2019, <http://5g-coral.eu/>.

   [_60802]   IEC/IEEE, ., "Use Cases IEC/IEEE 60802 V1.3", IEC/IEEE
              60802 , 2018, <http://www.ieee802.org/1/files/public/
              docs2018/60802-industrial-use-cases-0818-v13.pdf>.

Appendix A.  Overview of the IoT Edge Computing

   This list of initiatives, projects and products aim to provide an
   overview of the IoT edge computing.

   Our goal is to be representative rather than exhaustive.

Hong, et al.            Expires 3 September 2020               [Page 20]
Internet-Draft             IoT Edge Computing                 March 2020

   Please help us complete this overview by communicating with us about
   entries we have missed.

A.1.  Open Source Projects

A.1.1.  Gateway/CPE Platforms

   EdgeX Foundry, Home Edge, Edge Virtualization Engine are Linux
   Foundation projects ([Linux_Foundation_Edge]) aiming to provide a
   platform for edge computing devices.

   Such an open source platform can, for example, host proprietary
   programs currently run on IoT gateway products (Appendix A.2).

   EdgeX Foundry develops an edge computing framework running on the IoT
   gateway.

   Home Edge develops an edge computing framework especially dedicated
   to home computing devices, controlling home appliances, sensors,
   etc., and enabling AI applications, especially distributed and
   parallel machine learning.

   The Edge Virtualization Engine (EVE) project develops a
   virtualization platform (for VMs and containers) designed to run
   outside of the datacenter, in an edge network; EVE is deployed on
   bare-metal hardware.

   Computing devices:  Hardware support for EdgeX and EVE is similar:
      they support x86 and ARM-based computing devices; A typical target
      can be a Linux Raspberry Pi with 1GB RAM, 64bit CPU, 32GB storage.

   Service platform:  EdgeX uses a micro-service architecture.  Micro-
      services on the gateway are connected together, and to outside
      applications, through REST, or messaging technologies such as
      MQTT, AMQP and 0MQ.  The gateway can communicate with external
      backend applications or other gateways (north-south in tiered
      deployments or east-west in more distributed deployments).
      Gateway-device communication can use a wide range of IoT
      protocols.  "Export services" enable on-gateway and off-gateway
      clients to register as recipient for data from devices.  Core
      services are microservices that deal with persisting data from
      devices or alternatively "streaming" device data through, without
      persistence (core data service); managing information about the
      IoT devices, including their sensors, how to communicate with
      them, etc. (metadata service); and actual communication with IoT
      devices, on behalf of other on-gateway or off-gateway services
      (command service).  A rule engine provides an API to register
      actions in response to conditions typically including an IoT

Hong, et al.            Expires 3 September 2020               [Page 21]
Internet-Draft             IoT Edge Computing                 March 2020

      device ID, sensor values to check, thresholds, etc.  The
      scheduling micro service deals with organizing the removal of data
      persisted on the gateway.  Alerts and notifications microservice
      can be used to dispatch alert/notifications from internal or
      external sources to interested consumers including backend
      servers, or human operators through email or SMS.

   Edge cloud applications:  Target applications for EdgeX include
      Industrial IoT (e.g., IoT sensor data and actuator control mixed
      with augmented reality application for technicians).  Home Edge
      focuses on smart home use cases, including using AI lifestyle and
      safety applications.

A.1.2.  Edge Cloud Management Platforms

   This set of open-source projects setup and manage clouds of
   individual edge computing devices.

   StarlingX ([StarlingX]) extends OpenStack to provide virtualization
   platform management for edge clouds, which are distributed (in the
   range of 100 compute devices), secure and highly available.

   Akraino Edge Stack, another project from the Linux Fundation Edge
   [Linux_Foundation_Edge], has a wider scope of developing a management
   platform adapted for the edge (e.g., covering 1000 plus locations),
   aiming for zero-touch provisioning, and zero-touch lifecycle
   management.

   Computing devices:  Compute devices are typically Linux-based
      application servers or more constrained devices.

   Service platform:  StarlingX adds new management services to
      OpenStack by leveraging building blocks such as Ceph for
      distributed storage, Kubernetes for orchestration.  The new
      services are for management of configuration (enabling auto-
      discovery and configuration), faults, hosts (enabling host failure
      detection and auto-recovery), services (providing high
      availability through service redundancy and multi-path
      communication) and software (enabling updates).

   Edge cloud applications:  An edge computing platform may support a
      wide range of use cases.  E.g., autonomous vehicles, industrial
      automation and robotics, cloud RAN, metering and monitoring,
      mobile HD video, content delivery, healthcare imaging and
      diagnostics, caching and surveillance, augmented/virtual reality,
      small cell services for high density locations (stadiums),
      universal CPE applications, retail.

Hong, et al.            Expires 3 September 2020               [Page 22]
Internet-Draft             IoT Edge Computing                 March 2020

A.1.3.  Related Projects

   Open Edge Computing ([OpenEdgeComputing]) is an initiative from
   universities, manufacturers, infrastructure providers and operators,
   enabling efficiently offloading cloudlets (VMs) to the edge.
   Computing devices are typically powerful, well-connected servers
   located in mobile networks (e.g., collocated with base stations or
   aggregation sites).  The service platform is built on top of
   OpenStack++, an extension of OpenStack to support cloudlets.  This
   project is mentioned here as a related project because of its edge
   computing focus, and potential for some IoT use cases.  Nevertheless,
   its primary use cases are typically non-IoT related, such as
   offloading processing-intensive applications from a mobile device to
   the edge.

A.2.  Products

A.2.1.  IoT Gateways

   Multiple products are marketed as IoT gateways (Amazon Greengrass,
   Microsoft Azure IoT Edge, Google Cloud IoT Core, and gateway
   solutions from Bosh and Siemens).  They are typically composed of a
   software frameworks that can run on a wide range of IoT gateway
   hardware devices to provide local support for cloud services, as well
   as some other local IoT gateway features such as relaying
   communication and caching content.  Remote cloud is both used for
   management of the IoT gateways, and for hosting customer application
   components.  Some IoT gateway products (Amazon Snowball) have a
   primary purpose of storing edge data on premises, to enable
   physically moving this data into the cloud without incurring digital
   data transfer cost.

   Computing devices:  Typical computing devices run Linux, Windows or a
      Real-Time OS over an ARM or x86 architecture.  The level of
      service support on the computing device can range from low-level
      packages giving maximum control to embedded developers, to high-
      level SDKs.  Typical requirements can start at 1GHz and 128MB RAM,
      e.g., ranging from Raspberry Pi to a server-level appliance.

   Service platform:  IoT gateways can provide a range of service
      including: running stateless functions; routing messages between
      connected IoT devices (using a wide range of IoT protocols);
      caching data; enabling some form of synchronization between IoT
      devices; authenticating and encrypting device data.  Association
      between IoT devices and gateway based can require a device
      certificate.

Hong, et al.            Expires 3 September 2020               [Page 23]
Internet-Draft             IoT Edge Computing                 March 2020

   Edge cloud applications:  Pre-processing of IoT data for later
      processing in the cloud is a major driver.  Use cases include
      industrial automation, farming, etc.

A.2.2.  Edge Cloud Platforms

   Services such as MobileEdgeX provide a platform for application
   developers to deploy software (e.g., as software containers) on edge
   networks.

   Computing devices:  Bare metal and virtual servers provided by mobile
      network operators are used as computing devices.

   Service platform:  The service platform provides end device location
      service, using GPS data obtained from platform software deployed
      in end devices, correlated with location information obtained from
      the mobile network.  The service platform manages the deployment
      of application instances (containers) on servers close to end
      devices, using a declarative specification of optimal location
      from the application provider.

   Edge cloud applications:  Use cases include autonomous mobility,
      asset management, AI-based systems (e.g., quality inspection,
      assistance systems, safety and security cameras) and privacy-
      preserving video processing.  There are also non-IoT use cases
      such as augmented reality and gaming.

A.3.  Standards Initiatives

A.3.1.  ETSI Multi-access Edge Computing

   The ETSI MEC industry standardization group develops specifications
   that enable efficient and seamless integration of applications from
   vendors, service providers, and 3rd parties across multi-vendor MEC
   platforms ([ETSI_MEC_03]).

   Basic principles followed include: leveraging NFV infrastructure;
   being compliant with 3GPP systems; focusing on orchestration, MEC
   services, applications and platforms.

   Phase 1 (2015-2016) focused on basic platform services.  Phase 2
   (2017-2019) focuses on: supporting non-3GPP radio access
   technologies, especially WiFi; supporting a distributed, multi-
   operator and multi-vendor architecture; supporting non-VM based
   virtualization such as containers and PaaS.

   Computing devices:  Computing devices are typically application

Hong, et al.            Expires 3 September 2020               [Page 24]
Internet-Draft             IoT Edge Computing                 March 2020

      servers, attached to an eNodeB or at a higher level of aggregation
      point, and provide service to end users.

   Service platform:  The mobile edge platform offers an environment
      where the mobile edge applications can discover, advertise,
      consume and offer mobile edge services.  The platform can provide
      certain native services such as radio network information,
      location, bandwidth management etc.  The platform manager is
      responsible for managing the life cycle of applications including
      informing the mobile edge orchestrator of relevant application
      related events, managing the application rules and requirements
      including service authorizations, traffic rules, DNS
      configuration.

   Edge cloud applications:  Some of the use cases for MEC
      ([ETSI_MEC_02]) are IoT-related, including: security and safety
      (face recognition and monitoring), sensor data monitoring, active
      device location (e.g., crowd management), low latency vehicle-to-
      infrastructure and vehicle-to-vehicle (V2X, e.g., hazard
      warnings), video production and delivery, camera as a service.

A.3.2.  Edge Computing Support in 3GPP

   The 3GPP standards organization included edge computing support in 5G
   [_3GPP.23.501].  Integration of MEC and 5G systems has been studied
   in ETSI as well [ETSI_MEC_WP_28].

   Computing devices:  From 3GPP standpoint, a mobile device may access
      any computing device located in a local data network, i.e.,
      traffic is steered towards the local data network where the
      computing device is located.

   Service platform:  An external party may influence steering, QoS and
      charging of traffic towards the computing device.  Session and
      service continuity can ensure that edge service is maintained when
      a client device moves.  The network supports multiple-anchor
      connections, which makes it possible to connect a client device to
      both a local and a remote data network.  The client device can be
      made aware of the availability of a local area data network, based
      on its location.

   Edge cloud applications:  Edge cloud applications in 3GPP can help
      support the major use cases envisioned for 5G, including massive
      IoT and V2X.

Hong, et al.            Expires 3 September 2020               [Page 25]
Internet-Draft             IoT Edge Computing                 March 2020

A.3.3.  OpenFog and Industrial Internet Consortium

   The OpenFog Consortium (now merged with the Industrial Internet
   Consortium) aims to standardize industrial IoT, fog, and edge
   computing.  It produced a reference architecture for the fog
   ([OpenFog]), which has been published as IEEE standard P1934 in 2018.
   This work continues within the Industrial Internet Consortium.

   Computing devices:  Fog nodes include computational, networking,
      storage and acceleration elements.  This includes nodes collocated
      with sensors and actuators, roadside or mobile nodes involved in
      V2X connectivity.  Fog nodes should be programmable and may
      support multi-tenancy.  Fog computing devices must employ a
      hardware-based immutable root of trust, i.e., a trusted hardware
      component which receives control at power-on.

   Service platform:  The service platform is structured around
      "pillars" including: security end-to-end, scalability by adding
      internal components or adding more fog nodes,openness in term of
      discovery of/by other nodes and networks, autonomy from
      centralized clouds (for discovery, orchestration and management,
      security and operation) and hierarchical organization of fog
      nodes.

   Edge cloud applications:  Major use cases include smart cars and
      traffic control, visual security and surveillance, smart cities.

A.3.4.  Related Standards

   The IEEE Fog Computing and Networking Architecture Framework Working
   Group [IEEE-1934] published the OpenFog architecture as an IEEE
   document, and plan to do further work on taxonomy, architecture
   framework, and compliance guidelines.

A.4.  Research Projects

A.4.1.  Named Function Networking

   Named Function Networking ([Sifalakis]) is a research project that
   aims to extend ICN concepts (especially named data networking) to
   have the network orchestrate computation.  Interests are sent for a
   combination of function and argument names, instead of using the
   content name in NDN.

   Computing devices:  NFN-capable switches are collocated with
      computing devices.

Hong, et al.            Expires 3 September 2020               [Page 26]
Internet-Draft             IoT Edge Computing                 March 2020

   Service platform:  NFN enables accessing static data and dynamic
      computation results in one data-oriented framework, thus
      benefiting from usual ICN features such as data authenticity and
      caching, as well as enabling the network to perform various
      optimizations, e.g., moving data, code or both closer to
      requesters.  NFN also enables secure access to individual elements
      within Named Data Objects, e.g., for filtering or aggregation.

   Edge cloud applications:  Use cases include some form of MapReduce
      operations and service chaining.  NDN, on which NFN is based, has
      been studied in the context of IoT, where it can provide local
      trust management and rendezvous service.

A.4.2.  5G-CORAL

   The 5G-CORAL project ([_5G-CORAL]) aims to enable convergence of
   access across multiple radio access technologies using fog computing,
   using for this purpose an edge and fog computing system (EFS).

   Computing devices:  Computing devices used in 5G-CORAL include cloud
      and central data center servers, edge data center servers, and
      fixed or mobile "fog computing devices", which can be computing
      devices located in vehicles or factories, e.g., IoT gateways,
      mobile phones, cyber-physical devices, etc.

   Service platform:  5G-CORAL architecture is based on an integrated
      virtualized edge and fog computing system (EFS), that aims to be
      flexible, scalable and interoperable with other domains including
      transport (fronthaul, backhaul), core and clouds.  An
      Orchestration and Control System (OCS) enables automatic discovery
      of heterogeneous, multiple-owner resources, and federate them into
      a unified hosting environment.  OCS monitors resource usage to
      guarantee service levels.  Finally, OCS also includes
      orchestration and life cycle functions, including live migration
      and scaling.  Applications (user and third-party) both inside and
      outside the EFS subscribe to EFS services through APIs, with
      emphasis on IoT and cyber-physical functionalities.

   Edge cloud applications:  EFS-hosted services include analytics
      obtained from IoT gateways (e.g., LORA or eNodeB gateways),
      context information services from RATs, transport (fronthaul and
      backhaul) and core networks.  EFS-hosted functions include network
      performance acceleration functions, virtualized C-RAN functions
      for access nodes and possible end user devices.

Hong, et al.            Expires 3 September 2020               [Page 27]
Internet-Draft             IoT Edge Computing                 March 2020

A.4.3.  FLAME

   The FLAME project ([FLAME]) aims to improve performance of
   interactive media systems while keeping infrastructure costs low.

   It builds over virtualization technologies such as XOS, OpenStack and
   ONOS/ODL to offer a programmable media service platform.

   FLAME leverages IP-over-ICN technology developed through earlier
   projects including POINT ([POINT]).

   Computing devices:  The FLAME platform provides a service layer on
      top of an infrastructure platform, which can include cloud servers
      as well as computing devices collocated with WiFi access points.

   Service platform:  The FLAME platform can be seen as an "edge +
      cloud" computing platform with a use case focus on media
      dissemination, although the basic platform can be suitable for
      micro-services in general.  The computing platform is comprised
      of: computing devices, an infrastructure platform (XOS, OpenStack,
      ONOS/ODL), NFV-MANO components (orchestrator, virtual
      infrastructure manager) and FLAME platform core services (PCE,
      network access point, surrogate manager).

   Edge cloud applications:  IoT use cases include public safety, such
      as supporting body-worn camera for police and social workers.  As
      opposed to other multi-media applications that are also envisioned
      (pre-processing, user reporting, curation...), where a typical
      goal is to curate content early at the edge, to reduce expected
      high data volume, public safety use cases are typically about
      implementing triggers at the edge: everything needs to be kept
      anyway, to be available in case of an audit.  Content is stored
      offline during off peak-hours delivery.  For privacy and data
      volume concerns, triggers for, e.g., alerting police, cannot be
      performed in the cloud and should be performed as close to the
      data source as possible.

Authors' Addresses

   Jungha Hong
   ETRI
   218 Gajeong-ro, Yuseung-Gu
   Daejeon

   Email: jhong@etri.re.kr

Hong, et al.            Expires 3 September 2020               [Page 28]
Internet-Draft             IoT Edge Computing                 March 2020

   Yong-Geun Hong
   ETRI
   218 Gajeong-ro, Yuseung-Gu
   Daejeon

   Email: yghong@etri.re.kr

   Xavier de Foy
   InterDigital Communications, LLC
   1000 Sherbrooke West
   Montreal  H3A 3G4
   Canada

   Email: xavier.defoy@interdigital.com

   Matthias Kovatsch
   Huawei Technologies Duesseldorf GmbH
   Riesstr. 25 C // 3.OG
   80992 Munich
   Germany

   Email: ietf@kovatsch.net

   Eve Schooler
   Intel
   2200 Mission College Blvd.
   Santa Clara, CA,  95054-1537
   United States of America

   Email: eve.m.schooler@intel.com

   Dirk Kutscher
   University of Applied Sciences Emden/Leer
   Constantiaplatz 4
   26723 Emden
   Germany

   Email: ietf@dkutscher.net

Hong, et al.            Expires 3 September 2020               [Page 29]