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
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This Internet-Draft will expire on 3 September 2020.
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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
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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.
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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,
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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].
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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.
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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.
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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
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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
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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.
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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.
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+---------------------+
| 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
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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.
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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
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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.
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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]).
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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,
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"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,
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<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>.
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[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.
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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
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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.
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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.
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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
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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.
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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.
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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.
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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]