Overview of Edge Data Discovery
draft-mcbride-edge-data-discovery-overview-00
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| Document | Type | Active Internet-Draft (individual) | |
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| Authors | Mike McBride , Dirk Kutscher , Eve Schooler , Carlos J. Bernardos | ||
| Last updated | 2018-10-22 | ||
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draft-mcbride-edge-data-discovery-overview-00
T2TRG M. McBride
Internet-Draft D. Kutscher
Intended status: Standards Track Huawei
Expires: April 25, 2019 E. Schooler
Intel
CJ. Bernardos
UC3M
October 22, 2018
Overview of Edge Data Discovery
draft-mcbride-edge-data-discovery-overview-00
Abstract
This document describes the problem of distributed data discovery in
edge computing. Increasing numbers of IoT devices and sensors are
generating a torrent of data that originates at the very edges of the
network and that flows upstream, if it flows at all. Sometimes that
data must be processed or transformed (transcoded, subsampled,
compressed, analyzed, annotated, combined, aggregated, etc.) on edge
equipment along the way, particularly in places where multiple high
bandwidth streams converge and where resources are limited. Support
for edge data analysis is critical to make local, low-latency
decisions (e.g., regarding predictive maintenance, the dispatch of
emergency services, identity, authorization, etc.). In addition,
(transformed) data may be cached, copied and/or stored at multiple
locations in the network on route to its final destination. Although
the data might originate at the edge, for example in factories,
automobiles, video cameras, wind farms, etc., as more and more
distributed data is created, processed and stored, it becomes
increasingly dispersed throughout the network and there needs to be a
standard way to find it. New and existing protocols will need to be
identified/developed/enhanced for distributed data discovery at the
network edge and beyond.
Status of This Memo
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time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on April 25, 2019.
Copyright Notice
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 4
1.2. Terminology . . . . . . . . . . . . . . . . . . . . . . . 4
2. The Edge Data Discovery Scope . . . . . . . . . . . . . . . . 4
2.1. Types of Discovery . . . . . . . . . . . . . . . . . . . 5
3. Protocols for Discovering Resources . . . . . . . . . . . . . 6
4. Protocols for Discovering Functions . . . . . . . . . . . . . 7
5. Naming the Data . . . . . . . . . . . . . . . . . . . . . . . 8
6. Edge Data Discovery . . . . . . . . . . . . . . . . . . . . . 8
7. Use Cases of edge data discovery . . . . . . . . . . . . . . 8
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
9. Security Considerations . . . . . . . . . . . . . . . . . . . 9
10. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 9
11. Normative References . . . . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 9
1. Introduction
Edge computing is an architectural shift that migrates Cloud
functionality (compute, storage, networking, control, data
management, etc.) out of the back-end data center to be more
proximate to the IoT data being generated at the edges of the
network. Edge computing provides local compute, storage and
connectivity services, often required for latency- and bandwidth-
sensitive applications. Thus, Edge Computing plays a key role in
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verticals such as Energy, Manufacturing, Automotive, Video Analytics,
Gaming, Healthcare, Mining, Buildings and Smart Cities.
Edge computing is motivated at least in part by the sheer volume of
data that is being created by IoT devices (sensors, cameras, lights,
vehicles, drones, wearables, etc.) at the very network edge and that
flows upstream, in a direction for which the network was not
originally provisioned. In fact, in dense IoT deployments (e.g.,
many video cameras are streaming high definition video), where
multiple data flows collect or converge at edge nodes, data is likely
to need transformation (transcoded, subsampled, compressed, analyzed,
annotated, combined, aggregated, etc.) to fit over the next hop link,
or even to fit in memory or storage. Note also that the act of
performing compute on the data creates yet another new data stream!
In addition, (transformed) data may be cached, copied and/or stored
at multiple locations in the network on route to its final
destination. With an increasing percentage of devices connecting to
the Internet being mobile, support for in-the-network caching and
replication is critical for continuous data availability, not to
mention efficient network and battery usage for endpoint devices.
Additionally, as mobile devices' memory/storage fill up, in an edge
context they may have the ability to offload their data to other
proximate devices or resources, leaving a bread crumb trail of data
in their wakes. Therefore, although data might originate at edge
devices, as more and more data is continuously created, processed and
stored, it becomes increasingly dispersed throughout the physical
world (outside of or scattered across managed local data centers),
increasingly isolated in separate local edge clouds or data silos.
Thus there needs to be a standard way to find it. New and existing
protocols will need to be identified/developed/enhanced for these
purposes. Being able to discover distributed data at the edge or in
the middle of the network - will be an important component of Edge
computing.
An IETF T2T RG Edge discussion was held and a comparative study on
the definition of Edge computing was presented in multiple sessions
in T2T RG this last year. An IETF BEC (beyond edge computing) effort
has been evaluating potential gaps in existing edge computing
architectures. Edge Data Discovery is one potential gap that needs
evaluation and a solution.
And businesses, such as industrial companies, are starting to
understand how valuable the data is that they've kept in silo's.
Once this data is able to be aggregated on edge computing platforms,
they will be able to monetize the value of the data. But this will
happen only if data can be discovered and searched among equipment in
a standard way. Discovering the data, that its most useful to a
given market segment, will be extremely useful in building business
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revenues. Having a mechanism to provide this granular discovery is
the problem that needs solving either with existing, or new,
protocols.
1.1. Requirements Language
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 RFC 2119 [RFC2119].
1.2. Terminology
o Edge: The device edge is the boundary between digital and physical
entities in the last mile network. Sensors, gateways, compute
nodes are included. The infrastructure edge includes equipment on
the network operator side of the last mile network including cell
towers, edge data centers, cable headends, etc.
o Edge Computing: distributed computation that is performed near the
edge, where the nearness is determined by the system requirements.
This includes high performance compute, storage and network
equipment on either the device or infrastructure edge.
o Data Discovery: process of finding required data from edge
databases and consolidating it into a single source, perhaps name,
that can be evaluated
o NDN: Named Data Networking. IP packets name information, content
or endpoints (IP addresses) at the network layer.
2. The Edge Data Discovery Scope
Edge Computing data will typically be found at the device or
infrastructure edges. This is where we are focusing our efforts in
defining this edge data discovery problem space. Edge data will also
be sent to the cloud as needed. Discovering data which has be sent
to the cloud is out of scope of this document.
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+-------------------------------+
| Core Data Center |
+-------------------------------+
*** Backbone
* * Network
***
+-------------------------------+
| Regional Data Center |
+-------------------------------+
*** Metropolitan
* * Network
***
+-------------------------------+
| Infrastructure Edge|
+-------------------------------+
*** Access
* * Network
***
+-------------------------------+
| |Device Edge
+-------------------------------+
Figure 1: Edge Data Discovery Scope
2.1. Types of Discovery
There are many aspects of discovery.
Discovery of new devices added to an environment. Discovery of their
capabilities/services in client/server environments. Discovery of
these new devices automatically. Discovering a device and then
synchronizing the device inventory and configuration for edge
services. There are many existing protocols to help in this
discovery: UPnP, mDNS, DNS-SD, SSDP, NFC, XMPP, W3C network service
discovery, etc.
Edge devices discover each other in a standard way. We can use DHCP,
SNMP, SMS, COAP, LLDP, and routing protocols such as OSPF for devices
to discovery one another.
Discovery of link state and traffic engineering data/services by
external devices. BGP-LS is one solution.
There is discovery of aggregated data on edge compute device, which
is the focus of this draft. How can we discover aggregated data on
the edge and make use of it.
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Besides sensor data being aggregated on the edge computing
infrastructure, there will also be streaming data (from a camera),
meta data (about the data or about the device that generated the data
or about the context, etc), or control data regarding an event that
triggered, or an executable that embodies a function, method or
service, or other piece of code or algorithm. And it could be new
data that is created after (multiple) streams converge at the edge
node and are processed/transformed in some manner.
Discovery of functions in an SFC environment: Service function
chaining (SFC) allows the instantiation of an ordered set of service
functions and subsequent "steering" of traffic through them. Service
functions provide an specific treatment of received packets,
therefore they need to be known so they can be used in a given
service composition via SFC. So far, how the SFs are discovered and
composed has been out of the scope of discussions in IETF. While
there are some mechanisms that can be used and/or extended to provide
this functionality, work needs to be done. An example of this can be
found in "I-D.bernardos- sfc-discovery".
Discovery of resources in an NFV environment: virtualized resources
do not need to be limited to those available in traditional data
centers, where the infrastructure is stable, static, typically
homogeneous and managed by a single admin entity. Computational
capabilities are becoming more and more ubiquitous, with terminal
devices getting extremely powerful, as well as other types of devices
that are close to the end users at the edge (e.g., vehicular onboard
devices for infotainment, micro data centers deployed at the edge,
etc.). It is envisioned that these devices would be able to offer
storage, computing and networking resources to nearby network
infrastructure, devices and things (the fog paradigm). These
resources can be used to host functions, for example to offload/
complement other resources available at traditional data centers, but
also to reduce the end-to- end latency or to provide access to
specialized information (e.g., context available at the edge) or
hardware. Similarly to the discovery of functions, while there are
mechanisms that can be reused/extended, there is no complete solution
yet defined. An example of work in this area is I-D.bernardos-
intarea-vim-discovery"
3. Protocols for Discovering Resources
Mainly two types of situations need to be covered:
1. A set of resources appears (e.g., by a mobile node hosting them
joining a network) and they have to be discovered by an existing
virtualization infrastructure.
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2. A mobile device wants to discover virtualization resources
available at the current location.
Different alternatives of protocols can be used for this: from
approaches coupled with the access technology used, to solutions over
the top such as UPnP, mDNS, DNS-SD, SSDP, also including solutions
embedded into IP discovery/autoconfiguration, such as Neighbor
Discovery or DHCP.
4. Protocols for Discovering Functions
In an SFC environment deployed at the edge, the discovery protocol
may need to make available the following information per SF:
o Service Function Type, identifying the category of SF provided.
o SFC-aware: Yes/No. Indicates if the SF is SFC-aware.
o Route Distinguisher (RD): IP address indicating the location of
the SF(I).
o Pricing/costs details.
o Migration capabilities of the SF: whether a given function can be
moved to another provider (potentially including information about
compatible providers topologically close).
o Mobility of the device hosting the SF, with e.g. the following
sub- options:
Level: no, low, high; or a corresponding scale (e.g., 1 to 10).
Current geographical area (e.g., GPS coordinates, post code).
Target moving area (e.g., GPS coordinates, post code).
o Power source of the device hosting the SF, with e.g. the following
sub- options:
Battery: Yes/No. If Yes, the following sub-options could be
defined:
Capacity of the battery (e.g., mmWh).
Charge status (e.g., %).
Lifetime (e.g., minutes).
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5. Naming the Data
Named Data Networking (NDN) is one of five research projects funded
by the U.S. National Science Foundation under its Future Internet
Architecture Program. NDN has its roots in an earlier project,
Content-Centric Networking (CCN), which Van Jacobson started at Xerox
PARC around the time of his Google talk, to turn his architecture
vision into a running prototype (see also his CoNEXT 2009 paper and
especially Jacobsons ACM Queue interview). The motivation is the
mis-match of todays Internet architecture and its usage. Today we
build, support, and use Internet applications and services on top of
an extremely capable architecture not designed to support them. What
if we had an architecture designed to support them? Specifically,
todays IP packets can name only endpoints of conversations (IP
addresses) at the network layer. What if we generalize this layer to
name any information (or content), not just endpoints? We make it
easier to develop, manage, secure, and use our networks. NDN can be
applied to edge data discovery to make it much easier to extract data
by naming it. If data was named we would be able to discover the
appropriate data simply by its name.
6. Edge Data Discovery
How can we discover aggregated data on the edge and make use of it?
There are proprietary implementations of collecting data from various
databases and consolidating it for evaluation. We need a standard
protocol set for doing this data discovery, on the device or
infrastructure edge, in order to meet the requirements of many use
cases. We will have terabytes of data on the edge and need a way to
identify its existance and find the desired data. A user requires
the need to search for specific data in a data set and evaluate it
using their own tools. The tools are outside the scope of this
document, but the discovery of that data is in scope.
7. Use Cases of edge data discovery
1. Autonomous Vehicles
Description: Autonomous vehicles rely on the processing of huge
amounts of complex data in real-time for fast and accurate decisions.
These vehicles will rely on high performance compute, storage and
network resources to process the volumes of data they produce in a
low latency way. Various systems will need a standard way to
discover the pertinent data for decision making
1. Video Surveillance
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Description: The majority of the video surveillance footage will
remain at the edge infrastructure (not sent to the cloud data
center). This footage is coming from vehicles, factories, hotels,
universities, farms, etc.Much of the video footage will not be
interesting to those evaluating the data. A mechanism, set of
protocols perhaps, is needed to identify the interesting data at the
edge. The data will be in storage systems or in flight in networking
equipment.
1. Elevator Networks
Description: Elevators are one of many industrial applications of
edge computing. Edge equipment receives data from 100's of elevator
sensors. The data coming into the edge equipment is vibration,
temperature, speed, level, video, etc. We need the ability to
identify where the data we need to evalute is located.
8. IANA Considerations
N/A
9. Security Considerations
Security considerations will be a critical component of edge data
discovery particularly as intelligence is moved to the extreme edge
where data is to be extracted.
10. Acknowledgement
11. 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>.
Authors' Addresses
Mike McBride
Huawei
Email: michael.mcbride@huawei.com
Dirk Kutscher
Huawei
Email: dirk.kutscher@huawei.com
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Eve Schooler
Intel
Email: eve.m.schooler@intel.com
Carlos J. Bernardos
Universidad Carlos III de Madrid
Av. Universidad, 30
Leganes, Madrid 28911
Spain
Phone: +34 91624 6236
Email: cjbc@it.uc3m.es
URI: http://www.it.uc3m.es/cjbc/
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