Internet DRAFT - draft-hong-t2trg-iot-edge-computing
draft-hong-t2trg-iot-edge-computing
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
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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
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