Internet DRAFT - draft-defoy-t2trg-iot-edge-computing-background
draft-defoy-t2trg-iot-edge-computing-background
Network Working Group X. de Foy
Internet-Draft InterDigital Communications, LLC
Intended status: Informational J. Hong
Expires: 26 November 2020 Y-G. Hong
ETRI
M. Kovatsch
Huawei Technologies Duesseldorf GmbH
E. Schooler
Intel
D. Kutscher
University of Applied Sciences Emden/Leer
25 May 2020
IoT Edge Computing: Initiatives, Projects and Products
draft-defoy-t2trg-iot-edge-computing-background-00
Abstract
Many IoT applications have requirements that cannot be met by
the traditional Cloud. As a result, the IoT is driving the Internet
toward Edge computing. This draft reviews initiatives, projects and
products related to IoT Edge Computing.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
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Copyright Notice
Copyright (c) 2020 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Open Source Projects . . . . . . . . . . . . . . . . . . . . 3
2.1. Gateway/CPE Platforms . . . . . . . . . . . . . . . . . . 3
2.2. Edge Cloud Management Platforms . . . . . . . . . . . . . 4
2.3. Related Projects . . . . . . . . . . . . . . . . . . . . 5
3. Products . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.1. IoT Gateways . . . . . . . . . . . . . . . . . . . . . . 5
3.2. Edge Cloud Platforms . . . . . . . . . . . . . . . . . . 6
4. Standards Initiatives . . . . . . . . . . . . . . . . . . . . 6
4.1. ETSI Multi-access Edge Computing . . . . . . . . . . . . 6
4.2. Edge Computing Support in 3GPP . . . . . . . . . . . . . 7
4.3. OpenFog and Industrial Internet Consortium . . . . . . . 8
4.4. Related Standards . . . . . . . . . . . . . . . . . . . . 8
5. Research Projects . . . . . . . . . . . . . . . . . . . . . . 8
5.1. Named Function Networking . . . . . . . . . . . . . . . . 8
5.2. 5G-CORAL . . . . . . . . . . . . . . . . . . . . . . . . 9
6. Informative References . . . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11
1. Introduction
This list of open-source or commercial products, standard initiatives
and research projects aims to provide an overview of the IoT edge
computing.
It has been developped in support of [IOT-EDGE]. This other draft
studies challenges and functions associated with IoT edge computing,
and provides further background information on IoT, cloud computing
and edge computing.
Our goal is to be representative rather than exhaustive. Please help
us complete this overview by communicating with us about entries we
have missed.
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2. Open Source Projects
2.1. 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 (Section 3).
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
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
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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.
2.2. 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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2.3. 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.
3. Products
3.1. 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.
3.2. 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.
4. Standards Initiatives
4.1. 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.
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Computing devices: Computing devices are typically application
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.
4.2. 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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4.3. 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.
4.4. 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.
5. Research Projects
5.1. 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.
5.2. 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.
6. Informative References
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[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>.
[IEEE-1934]
IEEE, ., "FOG - Fog Computing and Networking Architecture
Framework", Portal , 2019,
<https://standards.ieee.org/standard/1934-2018.html>.
[IOT-EDGE] Hong, J., Hong, Y-G., de Foy, X., Kovatsch, M., Schooler,
E., and D. Kutscher, "IoT Edge Challenges and Functions",
Work in Progress, Internet-Draft, draft-hong-t2trg-iot-
edge-computing, <https://tools.ietf.org/html/draft-hong-
t2trg-iot-edge-computing>.
[Linux_Foundation_Edge]
Linux Foundation, ., "Linux Foundation Edge", Portal ,
2019, <https://www.lfedge.org/>.
[OpenEdgeComputing]
"Open Edge Computing", Portal , 2019,
<http://openedgecomputing.org/>.
[OpenFog] "OpenFog Reference Architecture for Fog Computing",
OpenFog Consortium , 2017.
[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.
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[StarlingX]
OpenStack Foundation, ., "StarlingX", Portal , 2019,
<https://www.starlingx.io/>.
[_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/>.
Authors' Addresses
Xavier de Foy
InterDigital Communications, LLC
1000 Sherbrooke West
Montreal H3A 3G4
Canada
Email: xavier.defoy@interdigital.com
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
Matthias Kovatsch
Huawei Technologies Duesseldorf GmbH
Riesstr. 25 C // 3.OG
80992 Munich
Germany
Email: ietf@kovatsch.net
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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
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