IoT Edge Challenges and Functions
draft-hong-t2trg-iot-edge-computing-05

Versions: 00 01 02 03 04 05                                             
Network Working Group                                            J. Hong
Internet-Draft                                                 Y-G. Hong
Intended status: Informational                                      ETRI
Expires: 14 January 2021                                       X. de Foy
                                        InterDigital Communications, LLC
                                                             M. Kovatsch
                                    Huawei Technologies Duesseldorf GmbH
                                                             E. Schooler
                                                                   Intel
                                                             D. Kutscher
                               University of Applied Sciences Emden/Leer
                                                            13 July 2020


                   IoT Edge Challenges and Functions
                 draft-hong-t2trg-iot-edge-computing-05

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 general model, and major components of the IoT Edge, with
   the goal to provide a common base for future discussions in T2TRG and
   other IRTF and IETF groups.

Status of This Memo

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   This Internet-Draft will expire on 14 January 2021.






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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Background  . . . . . . . . . . . . . . . . . . . . . . . . .   3
     2.1.  Internet of Things (IoT)  . . . . . . . . . . . . . . . .   3
     2.2.  Cloud Computing . . . . . . . . . . . . . . . . . . . . .   4
     2.3.  Edge Computing  . . . . . . . . . . . . . . . . . . . . .   4
     2.4.  Example of IoT Edge Computing Use Cases . . . . . . . . .   6
       2.4.1.  Smart Construction  . . . . . . . . . . . . . . . . .   6
       2.4.2.  Smart Grid  . . . . . . . . . . . . . . . . . . . . .   6
       2.4.3.  Smart Water System  . . . . . . . . . . . . . . . . .   7
   3.  IoT Challenges Leading Towards Edge Computing . . . . . . . .   7
     3.1.  Time Sensitivity  . . . . . . . . . . . . . . . . . . . .   7
     3.2.  Uplink Cost . . . . . . . . . . . . . . . . . . . . . . .   8
     3.3.  Resilience to Intermittent Services . . . . . . . . . . .   8
     3.4.  Privacy and Security  . . . . . . . . . . . . . . . . . .   8
   4.  IoT Edge Computing Functions  . . . . . . . . . . . . . . . .   9
     4.1.  Overview of IoT Edge Computing Today  . . . . . . . . . .   9
     4.2.  General Model . . . . . . . . . . . . . . . . . . . . . .  10
     4.3.  OAM Components  . . . . . . . . . . . . . . . . . . . . .  14
       4.3.1.  Virtualization Management . . . . . . . . . . . . . .  14
       4.3.2.  Resource Discovery and Authentication . . . . . . . .  15
       4.3.3.  Edge Organization and Federation  . . . . . . . . . .  15
     4.4.  Functional Components . . . . . . . . . . . . . . . . . .  16
       4.4.1.  External APIs . . . . . . . . . . . . . . . . . . . .  16
       4.4.2.  Communication Brokering . . . . . . . . . . . . . . .  16
       4.4.3.  In-Network Computation  . . . . . . . . . . . . . . .  17
       4.4.4.  Edge Caching  . . . . . . . . . . . . . . . . . . . .  18
       4.4.5.  Other Services  . . . . . . . . . . . . . . . . . . .  19
     4.5.  Application Components  . . . . . . . . . . . . . . . . .  19
       4.5.1.  IoT End Devices Management  . . . . . . . . . . . . .  19
       4.5.2.  Data Management . . . . . . . . . . . . . . . . . . .  19
     4.6.  Simulation and Emulation Environments . . . . . . . . . .  20
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  20



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   6.  Acknowledgment  . . . . . . . . . . . . . . . . . . . . . . .  21
   7.  Informative References  . . . . . . . . . . . . . . . . . . .  21
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  26

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, cloud services).

   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
   background, uses cases, challenges, and presents system models and
   functional components.

2.  Background

2.1.  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
   [Ashton], the concept of IoT has evolved.  It now reflects a vision
   of connecting the physical world to the virtual world of computers
   using (wireless) networks over which 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 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].  This limited
   storage and processing power leads to complementing IoT with cloud
   computing.




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2.2.  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 become a predominant
   technology that offers virtually unlimited capacity in terms of
   storage and processing power, at low cost.  This offering 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].

   Today, an unprecedented volume and variety of data is generated by
   Things and applications deployed in edge networks consume this data.
   Some of these applications may need very short response times, some
   may access personal data, while others may generate vast amounts of
   data.  Today's cloud-based service models are not suitable for these
   applications, which can instead leverage edge computing.

2.3.  Edge Computing

   Edge computing, in some settings 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.  Edge
   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
   processes both downstream data, e.g. originated from cloud services,
   and upstream data, e.g. originated from end devices or network
   elements.  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 cloud 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 (this is also referred to as Mist
   Computing).



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   Several standards defining organization and industry forums have
   provided definitions of edge and fog computing:

   *  ISO defines edge computing as 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 defines multi-access edge computing as 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].

   *  The Industrial Internet Consortium (formerly OpenFog) defines fog
      computing as "a horizontal, system-level architecture that
      distributes computing, storage, control and networking functions
      closer to the users along a cloud-to-thing continuum" [OpenFog].

   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) [ETSI_MEC_03].

   *  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 refers to applying IT solutions to OT problems
      such as analytics, more flexible user interfaces, or simply having
      more compute power than an automation controller.









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2.4.  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.

2.4.1.  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 and reported to an inspector.  Today, data
   produced by such measurements is collected by a local gateway and
   transferred to a remote cloud 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 the cloud server.

   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.  By leveraging 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 trigger an on-site alarm.  Furthermore,
   predicting the time of an incident can help reducing significantly
   the volume and cost of transmitted data, by transmitting video at
   high resolution during critical periods, while otherwise using a
   lower resolution.

2.4.2.  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 local electrical energy storage decisions in the smart grid
   system.






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2.4.3.  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.  Edge computing
   can help with monitoring water consumption and transport, and with
   predicting future water usage level.  Examples of application
   include: water harvesting, ground water monitoring, locally analyzing
   collected information related to water control and management to
   limit water losses.

3.  IoT Challenges Leading Towards Edge Computing

   This section describes challenges met by IoT, that are motivating the
   adoption of edge computing for IoT.  Those are distinct from research
   challenges applicable to IoT edge computing, some of which will be
   mentioned in Section 4.3.

   IoT technology is used with more and more demanding applications,e.g.
   in industrial, automotive or healthcare domains, leading to new
   challenges.  For example, industrial machines such as laser cutters
   already produce over 1 terabyte per hour, and similar amounts can be
   generated in 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 more proximate and more distributed and
   disaggregated.

3.1.  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].  In some cases
   speed-of-light limitations may simply prevent a solution based on
   remote cloud, however it is not the only challenge relative to time
   sensitivity.  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 and
   within a strict deadline.  Given the best-effort characteristics of
   the Internet this challenge is virtually impossible to address,
   without using end-to-end guarantees for individual message delivery
   and continuous data flows.



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3.2.  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.

3.3.  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.  The dual is
   also true, a cloud back-end might want to have a reading of the
   device even if it's currently asleep.

3.4.  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.

   Furthermore, passive observers can perform traffic analysis on the
   device-to-cloud path.  Hiding traffic patterns associated with sensor
   networks can therefore be another requirement for edge computing.






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4.  IoT Edge Computing Functions

   In this section we first look at the current state of IoT edge
   computing Section 4.1, and then define a general system model
   Section 4.2.  This provides context for IoT edge computing functions,
   which are listed in Section 4.3.

4.1.  Overview of IoT Edge Computing Today

   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
   [I-D-defoy-t2trg-iot-edge-computing-background].

   IoT gateways, both open-source (such as EdgeX Foundry or Home Edge)
   and proprietary (such as Amazon Greengrass, Microsoft Azure IoT Edge,
   Google Cloud IoT Core, and gateways from Bosh, Siemens), 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 (such as StarlingX, Akraino Edge Stack, Mobile EdgeX) adapt
   cloud management technologies (e.g., Kubernetes) to the edge cloud,
   i.e., to smaller, distributed computing devices running outside a
   controlled data center.  Services and application life-cycle is
   typically using a NFV-like management and orchestration model.




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   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) or a distributed storage platform (e.g.,
   Ceph).  External storage, e.g., on databases in distant or local IT
   cloud, is typically used for filtered data deemed worthy of long term
   storage, although in some case it may be 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).

4.2.  General Model

   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:











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   *  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.  Some IoT end
      devices can have enough computing capabilities to participate in
      such distributed systems due to advances in hardware technology.
      In this model, edge computing nodes can collaborate with each
      other to share their resources.




































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                +---------------------+
                |   Remote network    |  +---------------+
                |(e.g., cloud network)|  |   Service     |
                +-----------+---------+  |   Operator    |
                            |            +------+--------+
                            |                   |
             +--------------+-------------------+-----------+
             |            Edge Computing Domain             |
             |                                              |
             |   One or more Computing Nodes                |
             |   (IoT gateway, end devices, switches,       |
             |   routers, mini/micro-datacenters, etc.)     |
             |                                              |
             |   OAM Components                             |
             |   - Virtualization Management                |
             |   - Resource Discovery and 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 the above 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



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   system.  They may be implemented in a centralized or distributed
   fashion, in the edge network, or through some interworking between
   the edge network and a remote cloud network.

             +----------------------------------------------+
             |            Edge Computing Domain             |
             |                                              |
             | +--------+    +--------+        +--------+   |
             | |Compute |    |Compute |        |Compute |   |
             | |node/End|    |node/End|  ....  |node/End|   |
             | |device 1|    |device 2|  ....  |device m|   |
             | +----+---+    +----+---+        +----+---+   |
             |      |             |                 |       |
             |  +---+-------------+-----------------+--+    |
             |  |           IoT Edge Gateway           |    |
             |  +-----------+-------------------+------+    |
             |              |                   |           |
             +--------------+-------------------+-----------+
                            |                   |
                +-----------+---------+  +------+-------+
                |   Remote network    |  |   Service    |
                |(e.g., cloud network)|  |  Operator(s) |
                +-----------+---------+  +------+-------+
                            |                   |
             +--------------+-------------------+-----------+
             |              |                   |           |
             |  +-----------+-------------------+------+    |
             |  |           IoT Edge Gateway           |    |
             |  +---+-------------+-----------------+--+    |
             |      |             |                 |       |
             | +----+---+    +----+---+        +----+---+   |
             | |Compute |    |Compute |        |Compute |   |
             | |node/End|    |node/End|  ....  |node/End|   |
             | |device 1|    |device 2|  ....  |device n|   |
             | +--------+    +--------+        +--------+   |
             |                                              |
             |            Edge Computing Domain             |
             +----------------------------------------------+

      Figure 2: Example: Machine Learning over a Distributed IoT Edge
                              Computing System

   In the above example of system, the edge computing domain is composed
   of IoT edge gateways and IoT end devices which are also used as
   computing nodes.  Edge computing domains are connected with a remote/
   cloud network, and with their respective service operator's system.
   IoT end devices/computing nodes provide logical functions, as part of
   a distributed machine learning application.  The processing



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   capabilities in IoT end devices being limited, they require the
   support of other nodes: the training process for AI services is
   executed at IoT edge gateways or cloud networks and the prediction
   (inference) service is executed in the IoT end devices.

   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.  Some representative research challenges are
   associated with those functions.  We used input from co-authors, IRTF
   attendees and some comprehensive reviews of the field ([Yousefpour],
   [Zhang2], [Khan]).

4.3.  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.  Centralized (e.g., SDN) and decentralized management
   systems can be used.

   We further detail a few OAM components.

4.3.1.  Virtualization Management

   Some IoT edge computing systems make use of virtualized (compute,
   storage and networking) resources, which need to be allocated and
   configured.  This function is covered to a large extent by ETSI NFV
   and MEC standards activities.  Projects such as [LFEDGE-EVE] further
   cover virtualization and its management into distributed edge
   computing settings.

   Related challenges include:

   *  Minimizing virtual function instantiation time and resource usage



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   *  Integration of edge computing with virtualized radio networks (Fog
      RAN) [I-D.bernardos-sfc-fog-ran] and with 5G access networks

   *  Handling of multi-tenancy with regards to limited resources at the
      network edge

4.3.2.  Resource Discovery and Authentication

   Discovery and authentication may target platforms, infrastructure
   resources, such as compute, network and storage, but also other
   resources such as IoT end devices, sensors, data, code units,
   services, applications or users interacting with the system.  Broker-
   based solutions can be used, e.g. using an IoT gateway as broker to
   discover IoT resources.  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).

   Related challenges include:

   *  Discovery, authentication and trust establishment between end
      devices, compute nodes and platforms, with regards to concerns
      such as mobility, heterogeneity, scale, multiple trust domains,
      constrained devices, anonymity and traceability

   *  Intermittent connectivity to the Internet, preventing relying on a
      third-party authority [Echeverria]

   *  Resiliency to failures [Harchol], denial of service attacks,
      easier physical access for attackers

4.3.3.  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.  Organization may range from
   centralized to peer-to-peer.  Such groups can also form federations
   with other edge or remote clouds.

   Related challenges include:

   *  Sharing resources in multi-vendor/operator scenarios, with a goal
      to optimize criteria such as profit [Anglano], resource usage,
      latency or energy consumption







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   *  Support for scaling, and enabling fault-tolerance or self-healing
      [Jeong].  Besides using hierarchical organization to cope with
      scaling, another available and possibly complementary mechanism is
      multicast ([RFC7390] [I-D.ietf-core-oscore-groupcomm])

   *  Capacity planning, placement of infrastructure nodes to minimize
      delay [Fan], cost, energy, etc.

   *  Incentives for participation, e.g. in peer-to-peer federation
      schemes

4.4.  Functional Components

4.4.1.  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 or mobile
   users, for example to provide access to services and applications, or
   to manage data published by local/mobile devices.

   Related challenges include:

   *  Defining edge computing abstractions suitable for users and cloud
      systems to interact with edge computing systems.  In one example,
      this interaction can be based on the PaaS model [Yangui]

4.4.2.  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
   [I-D.mcbride-edge-data-discovery-overview].  Another related aspect
   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.






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   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
   systems may leverage direct communication between end devices, over
   device-to-device links.  A broker can ensure communication
   reliability, traceability, and in some cases transaction management.

   Related challenges include:

   *  Enabling secure and resilient communication between IoT end
      devices and remote cloud, e.g. through multipath support

4.4.3.  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, but also to orchestrate computation (in a
   centralized or distributed manner) and manage applications lifecycle.
   Support for in-network computation may vary in term of capability,
   e.g., computing nodes can host virtual machines, software containers,
   software actors or unikernels 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.

   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.  For example in
   some systems a bandwidth manager service can be exposed to enable
   allocation of bandwidth to/from an edge computing application
   instance.

   Related challenges include:

   *  (Computation placement) Selecting, in a centralized or
      distributed/peer-to-peer manner, an appropriate compute device
      based on available resources, location of data input and data
      sinks, compute node properties, etc., and with varying goals
      including for example end-to-end latency, privacy, high
      availability, energy conservation, network efficiency (e.g. using
      load balancing techniques to avoid congestion)

   *  Onboarding code on a platform or compute device, and invoking
      remote code execution, possibly as part of a distributed
      programming model and with respect to similar concerns of latency,
      privacy, etc.  These operations should deal with heterogeneous
      compute nodes [Schafer], and may in some cases also support end
      devices as compute nodes



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   *  Adapting Quality of Results (QoR) for applications where a perfect
      result is not necessary [Li]

   *  Assisted or automatic partitioning of code
      [I-D.sarathchandra-coin-appcentres]

   *  Supporting computation across trust domains, e.g. verifying
      computation results

   *  Relocating an instance from one compute node to another, while
      maintaining a given service level.

   *  Session continuity when communicating with end devices that are
      mobile, possibly at high speed (e.g. in vehicular scenarios)

   *  Defining, managing and verifying SLAs for edge computing systems.
      Pricing is a related challenge

4.4.4.  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.

   Related challenges include

   *  (Cache and data placement) Using cache positioning and data
      placement strategies to minimize data retrieval delay [Liu],
      energy consumption.  Caches may be positioned in the access
      network infrastructure or may be on end devices using device-to-
      device communication

   *  Maintaining data consistency, freshness and privacy in systems
      that are distributed, constrained and dynamic (e.g. due to end
      devices and computing nodes churn or mobility).  For example, age
      of information [Yates], a performance metric that captures the
      timeliness of information from a sender (e.g. an IoT device), can
      be exposed to networks to enable tradeoffs in this problem space









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4.4.5.  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.

4.5.  Application Components

   IoT edge computing can host applications such as the ones mentioned
   in Section 2.4.  While describing components of individual
   applications is out of our scope, some of those applications share
   similar functions, such as IoT end device management, data
   management, described below.

4.5.1.  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 by separating the scalability domain into
   edge/local networks and remote network.

   Challenges listed in Section 4.3.2 may be applicable to IoT end
   devices management as well.

4.5.2.  Data Management

   Data storage and processing at the edge is a major aspect of IoT edge
   computing, directly addressing high level IoT challenges listed in
   Section 3.  Data analysis such as performed in AI/ML tasks performed
   at the edge may benefit from specialized hardware support on
   computing nodes.

   Related challenges include:

   *  Addressing concerns on resource usage, security and privacy when
      sharing, discovering or managing data.  For example by presenting
      data in views composed of an aggregation of related data [Zhang],
      protecting data communication between authenticated peers
      [Basudan], classifying data (e.g., in terms of privacy,
      importance, validity, etc.), compressing data

   *  Data driven programming models [Renart], e.g. event-based,
      including handling of naming and data abstractions






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   *  Addressing concerns such as limited resources, privacy, dynamic
      and heterogeneous environment, to deploy machine learning at the
      edge.  For example, making machine learning more lightweight and
      distributed, supporting shorter training time and simplified
      models, and supporting models that can be compressed for efficient
      communication [Murshed]

   *  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]

4.6.  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]).

5.  Security Considerations

   As discussed in Section 4.3.2, authentication and trust (between
   computing nodes, management nodes, end devices) can be challenging as
   scale, mobility and heterogeneity increase.  The sometimes
   disconnected nature of edge resources can prevent relying on a third-
   party authority.  Distributed edge computing is exposed to issues
   with reliability and denial of service attacks.  Personal or
   proprietary IoT data leakage is also a major threat, especially due
   to the distributed nature of the systems (Section 4.5.2).






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   However, edge computing also brings solutions in the security space:
   maintaining privacy by computing sensitive data closer to data
   generators is a major use case for IoT edge computing.  An edge cloud
   can be used to take actions based on sensitive data, or anonymizing,
   aggregating or compressing data prior to transmitting to a remote
   cloud server.  Edge computing communication brokering functions can
   also be used to secure communication between edge and cloud networks.

6.  Acknowledgment

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

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Authors' Addresses

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

   Email: jhong@etri.re.kr


   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




Hong, et al.             Expires 14 January 2021               [Page 26]


Internet-Draft             IoT Edge Computing                  July 2020


   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 14 January 2021               [Page 27]