Internet DRAFT - draft-mcbride-data-discovery-use-cases
draft-mcbride-data-discovery-use-cases
Network Working Group M. McBride
Internet-Draft J. Guichard
Intended status: Informational Y. Qu
Expires: August 23, 2021 Futurewei
T. Hardjono
MIT
CJ. Bernardos
UC3M
February 19, 2021
Data Discovery Use Cases
draft-mcbride-data-discovery-use-cases-00
Abstract
There needs to be a solution for locating and capturing data in a
standardized way. Data may be cached, copied and/or stored at
multiple locations in the network on route to its final destination.
With an increasingly high volume of devices connecting to the
Internet, support for network caching and replication is critical for
continuous data availability. There are data repositories throughout
a modern network and there needs to be a standardized way to locating
the repositories and discovering the desired data within.
There are several use cases which illustrate a need for a data
discovery solution. An application might need to query the network
to discover resources (program, service, resource) that can help the
local application perform a particular task. Additionally, there
could be volumes of data which needs to be searched and discovered in
order to provide a result to be acted upon by the application. These
are a couple of the use cases being addressed in this document.
Status of This Memo
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This Internet-Draft will expire on August 23, 2021.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Requirements Language . . . . . . . . . . . . . . . . . . . . 3
3. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
4. Problem Statement . . . . . . . . . . . . . . . . . . . . . . 3
4.1. Types of Data . . . . . . . . . . . . . . . . . . . . . . 4
5. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 4
5.1. Application-Aware Service Function Chaining . . . . . . . 4
5.2. Available CPU and Memory Resources . . . . . . . . . . . 5
5.3. Data Dependency . . . . . . . . . . . . . . . . . . . . . 5
5.4. Distributed Ledgers . . . . . . . . . . . . . . . . . . . 5
5.5. Edge Computing . . . . . . . . . . . . . . . . . . . . . 6
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 6
7. Security Considerations . . . . . . . . . . . . . . . . . . . 6
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 6
9. Normative References . . . . . . . . . . . . . . . . . . . . 6
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 7
1. Introduction
An application might need to query the network to discover resources
that can help the local application perform a particular task. There
could be volumes of data which needs to be searched and discovered in
order to provide a result to be acted upon.
Data discovery might involve an application requesting data. It
might involve a device looking to store data or to request the
processing from a data store and then gather the result. Or it could
be execution of a set of instructions at an appropriate device in the
network. Another possible area is service chaining where an
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application needs to run its data through a firewall but the selected
firewall must have a particular rule set applicable to this
particular application. Perhaps the service function has to be
located within a particular environment (security level). Or a
particular device must be found that is capable of executing upon a
set of instructions provided in the data packet. This document
focuses on various data discovery use cases.
2. 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].
3. Terminology
o SFC: Service Function Chaining
o APN: Application-Aware Networking
o DLT: Distributed Ledger Technologies
4. Problem Statement
As discussed in [I-D.mcbride-data-discovery-problem-statement], there
are many proprietary and standardized ways of discovering networking
devices and hosts. There are many solutions for discovering data
within a database. There are proprietary, non-standardized, ways of
discovering the data that may be stored throughout an environment of
networking devices. We can discover information about the devices
but can't locate and capture stored data (resource, program, service,
etc) in a standard way. With more networking devices storing
collected data there needs to be a standard way of discovering the
specific data needed amongst a potentially huge lake of databases.
This data discovery problem is particularly true for use cases where
it will be important to have the capability to express a data request
within the data packets and have the network route the traffic
accordingly. This might be an application requesting data. It might
be a device looking to store data or to request the processing, and
result, from a data store. It could be execution of a set of
instructions at an appropriate device in the network. An application
may need to run its data through a firewall but the selected firewall
must have a particular rule set applicable to this particular
application. Perhaps a service function needs to be located within a
particular environment (security level). Or a particular device must
be found that is capable of executing upon a set of instructions
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provided in the data packet. This document focuses on data discovery
use cases.
4.1. Types of Data
Discoverable data can be a resource, program, service etc. And an
infinite amount, and types, of data can be discoverable including
statistics, measurements, temperature, location, metadata, health,
transactions and so on.
Program: applets, graphics, games, spreadsheets, database systems,
browsers, etc
Service: firewalls, load balancers, spam filters, header
manipulators, etc
Resource: CPU, memory, etc
5. Use Cases
Here are some use cases to illustrate the need for data discovery:
5.1. Application-Aware Service Function Chaining
Application Aware Networking (APN), as described in
[I-D.li-apn-problem-statement-usecases], allows applications to
specify finer granularity requirements to the network operator by
providing application knowledge to the network layer. This
granularity includes the ability to convey the characteristics of an
application's traffic flow and program the network infrastructure
accordingly to provide service assurance.
An application might need to query the network to discover resources
that can help the local application perform a particular task.
Additionally, there could be volumes of data which needs to be
searched and discovered in order to provide a result to be acted upon
by the application.
End-to-end service delivery often needs to go through various service
functions, including traditional network service functions such as
firewalls, DPIs as well as new application-specific functions, both
physical and virtual. APN provides assigning a given traffic flow to
a specific service function chain (SFC) but also specifically allows
the subsequent steering according to the application information
carried in the APN packets.
When an application needs to run its data through a firewall, but the
selected firewall must have a particular rule set applicable to this
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particular application, then the application can leverage data
discovery functionality. The service function may be required to be
located within a particular environment such as a with a certain
security level. Data discovery is needed to find that particular
rule set (amongst the various firewalls) and then steer the packet
accordingly. Or a particular device, along the SFC, may need to be
found that is capable of executing upon a set of instructions
provided in the data packet. The data capabilities of devices needs
to be discoverable in order to steer the application packets towards
them along a SFC.
5.2. Available CPU and Memory Resources
An application, or service, may need to discover the available server
memory and compute resources from the network. A certain amount of
CPU resources may be required to support a particular application
workload. And the application may need to know the maximum CPU
utilization threshold available on a compute device. Gathering info
on available clock speeds and amount of cores can help determine how
quickly servers load and interact with a set of applications. The
network can provide the discoverability of the necessary data (cpu,
memory) in order for applications to properly execute. A network
planning app can also utilize this information to help predict future
resource demands in order to meet applications performance
requirements.
5.3. Data Dependency
There may be scenarios where it's critical to find X type of data
that can help a local application, or service, successfully perform a
particular task. Perhaps an industrial application needs real time
measurement data, such as temperature, in order to execute a process.
This required 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. In order for some
applications to properly execute, we need to find a way for the
network to provide support for data discovery.
5.4. Distributed Ledgers
DLT Gateways, as discussed in
[I-D.sardon-blockchain-gateways-usecases], will be given a
permissioned view of assets/transactions, that they are requested to
transfer, within their attached DLT domain. GW's may also need to
discover assets/transactions, not explicitly provided, within the DLT
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domain. It may become necessary for the GW (or other network
element.. if permitted) to discover the data (asset, resource,
service...) in order to transfer the required asset. Discovery of
the data parts is also needed to validate the transfer after the
asset movement. The ledger in the DLT will not hold all the relevant
information pertaining to a previous asset transfer. So there needs
to be ways to search/discover these. The data parts, to be
discovered, include:
Relevant DLT transaction public-keys of the involved entities
(i.e. public-keys (addresses) used on both DLTs.
Relevant entity public-keys and X.509 certs (Originator, owner of
gateway G1, owner of gateway G2, Beneficiary). This is similar to
the X.509 certs and cert-profiles used in the SWIFT banking
network.
Relevant asset-related JSON documents (e.g. asset profiles).
5.5. Edge Computing
As described in [I-D.mcbride-edge-data-discovery-overview], the
required data may be distributed across thousands of edge computing
devices. Edge computing is motivated by the sheer volume of data
that is being created by endpoint devices (sensors, cameras, lights,
vehicles, drones, wearables, etc.) at the very network edge. 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. This data, distributed across the edge, will need to be
discovered in order to perform any number of functions such as an IoT
application needing elevator vibration data in order to execute a
process.
6. IANA Considerations
7. Security Considerations
8. Acknowledgements
9. Normative References
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[I-D.li-apn-problem-statement-usecases]
Li, Z., Peng, S., Voyer, D., Xie, C., Liu, P., Qin, Z.,
Ebisawa, K., Previdi, S., and J. Guichard, "Problem
Statement and Use Cases of Application-aware Networking
(APN)", draft-li-apn-problem-statement-usecases-01 (work
in progress), September 2020.
[I-D.mcbride-data-discovery-problem-statement]
McBride, M., Kutscher, D., Schooler, E., Bernardos, C.,
and D. Lopez, "Data Discovery Problem Statement", draft-
mcbride-data-discovery-problem-statement-00 (work in
progress), July 2020.
[I-D.mcbride-edge-data-discovery-overview]
McBride, M., Kutscher, D., Schooler, E., Bernardos, C.,
Lopez, D., and X. Foy, "Edge Data Discovery for COIN",
draft-mcbride-edge-data-discovery-overview-05 (work in
progress), November 2020.
[I-D.sardon-blockchain-gateways-usecases]
Sardon, A. and T. Hardjono, "Blockchain Gateways: Use-
Cases", draft-sardon-blockchain-gateways-usecases-00 (work
in progress), October 2020.
[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
Futurewei
Email: michael.mcbride@futurewei.com
Jim Guichard
Futurewei
Email: james.n.guichard@futurewei.com
Yingzhen Qu
Futurewei
Email: yingzhen.qu@futurewei.com
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Thomas Hardjono
MIT
Email: hardjono@mit.edu
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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