LMAP J. Seedorf
Internet-Draft NEC
Intended status: Informational V. Gurbani
Expires: August 22, 2013 Bell Labs, Alcatel-Lucent
E. Marocco
Telecom Italia
February 18, 2013
ALTO for LMAP
draft-seedorf-lmap-alto-00
Abstract
In the context of Large-Scale Measurement of Broadband Performance
(LMAP), measurment results are currently made available to the public
either at the finest granularity level (e.g. as a list of results of
all individual tests), or in a very high level human-readable format
(e.g. as PDF reports).
This document argues that there is a need for an intermediate way to
provide access to large-scale network measurement results, flexible
enough to enable querying of specific and possibly aggregated data.
The Application-Layer Traffic Optimization (ALTO) Protocol, defined
with the goal to provide applications with network information, seems
a good candidate to fulfill such a role.
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document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Example Use Cases . . . . . . . . . . . . . . . . . . . . . . 5
3. Advantages of using ALTO . . . . . . . . . . . . . . . . . . . 6
4. Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4.1. Download speeds . . . . . . . . . . . . . . . . . . . . . 7
4.1.1. Network map . . . . . . . . . . . . . . . . . . . . . 8
4.1.2. Cost map . . . . . . . . . . . . . . . . . . . . . . . 9
5. References . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5.1. Normative References . . . . . . . . . . . . . . . . . . . 10
5.2. Informative References . . . . . . . . . . . . . . . . . . 10
Appendix A. Acknowledgment . . . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 12
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1. Introduction
Recently, there is a discussion on standardizing protocols that would
allow measurements of broadband performance on a large scale (LMAP
[I-D.schulzrinne-lmap-requirements]). In principle, the vision is
that "user networks gather data, either on their own initiative or
instructed by a measurement controller, and then upload the
measurement results to a designated measurement server."
Apart from protocols that can be used to gather measurement data and
to upload such data to dedicated servers, there is also a need for
protocols to retrieve - potentially aggregated - measurement results
for a certain network (or part of a network), possibly in an
automated way. Currently, two extremes are being used to provide
access to large-scale measurement results: One the one hand, highly
aggregated results for certain networks may be made available in the
form of PDFs of figures. Such presentations may be suitable for
certain use cases, but certainly do not allow a user (or entity such
as a service provider) to select specific criteria and then create
corresponding results. On the other hand, complete and detailed
results may be made available in the form of comma-seperated-values
(csv) files. Such data sets typically include the complete results
being measured on a very fine-grained level and usually imply large
file sizes (of result data sets). Such detailed result data sets are
very useful e.g. for the scientific community because they enable to
execute complex data analytics algorithms or queries to analyse
results.
Considering the two extremes discussed above, this document argues
that there is a need for an intermediate way to provide access to
large-scale network measurement results: It must be possible to query
for specific, possibly aggregated, results in a flexible way.
Otherwise, entities interested in measurement results either cannot
select what kind of result aggregation they desire, or must always
fetch large amounts of detailed results and process these huge
datasets themselves. The need for a flexible mechanism to query for
dedicated, partial results becomes evident when considering use cases
where a service provider or a process wants to use certain
measurement results in an automated fashion. For instance, consider
a video streaming service provider which wants to know for a given
end-user request the average download speed by the end user's access
provider in the end user's region (e.g. to optimize/parametrize its
http adaptive streaming service). Or consider a website which is
interested in retrieving average connectivity speeds for users
depending on access provider, region, or type of contract (e.g. to be
able to adapt web content on a per-request basis according to such
statistics).
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This document argues that use cases as described above may enhance
the value of measurements of broadband performance on a large scale
(LMAP), given that it is possible to query for selected results in an
automated fashion. Therefore, in order to facilitate such use cases,
a protocol is needed that enables to query LMAP measurements results
while allowing to specify certain parameters that narrow down the
particular data (i.e. measurement results) the issuer of the query is
interested in. This document argues that ALTO [RFC5693]
[I-D.ietf-alto-protocol] could be a suitable candidate for such a
flexible LMAP result query protocol.
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2. Example Use Cases
To motivate the usefulness of ALTO for querying LMAP results,
consider some key use cases:
o Video Streaming Service Provider: For HTTP adaptive streaming, it
may be very useful to be able to query for average measurement
values regarding a particular end user's access network provider.
For instance, consider a video streaming service provider that
queries LMAP measurement results to retrieve for a given end-user
request the average download speed by the end user's access
provider in the end user's region. Such data could help the
service provider to optimize/parametrize its HTTP adaptive
streaming service.
o Website Front End Optimization: A website might be interested in
statistics about average connectivity types or download speeds for
a given end user request in order to dynamically adapt HTML/CSS/
JavaScript content depending on such information (sometimes
referred to as "Front End Optimization"). For instance, image
compression may be employed depending on the average connectivity
type of a user in a given region or with a given access network
provider.
o Troubleshooting: In general, any service on the Internet may be
interested in LMAP data for troubleshooting. In case a service
does not work as expected (e.g. low throughput, high packet loss,
...), it may be of value for the service provider to retrieve
(fairly) recent measurement data regarding the host that is
requesting the service.
o TBD: add more use cases
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3. Advantages of using ALTO
The ALTO protocol [I-D.ietf-alto-protocol] specifies a very
lightweigth JSON-based encoding for network information and can play
an important role in querying the measurement results as we argue in
Section 2.
ALTO is designed on two abstractions that are useful here. First is
the abstraction of the physical network topology into an aggregated
but logical topology. In this abstract topological view, referred to
as "network map", individual hosts are aggregated into a well defined
network location identifier called a PID. Hosts could be aggregated
into the PID depending on certain identifying characteristics such as
geographical location, serving ISP, network mask, nominal access
speed, or any mix of them. The "network map" abstraction is
essential for exporting network infromation in a scalable and
privacy-preserving way.
The second abstraction that is useful for LMAP is the notion of a
"cost map". Each PID identified in the network map can, in a sense,
become a vertex in a cost map, and each edge joining adjacent
vertices can have an associated cost. The cost can be defined by the
measurement server and can indicate routing hops, the financial cost
of sending data over the link, available bandwidth on the link with
bottled-up links increasing showing a smaller value, or a user-
defined cost attribute that allows arbitrary reasoning.
The ALTO protocol defines several basic services based on such
abstractions, but additional ones can be easily defined as extesions.
There are other advantages to using ALTO as well. The protocol is
defined as a set of REST APIs on top of HTTP. The data carried by
the protocol is encoded as JSON. Queries can be performed by clients
locally after downloading the entire topological and cost maps or
clients can send filtered requests to the ALTO server such that the
ALTO server performs the required computation and returns the
results. The protocol supports a set of atomic constraints related
to equality that can be used to filter results and only obtain a set
of interest to the query.
Additionally, protocol extensions that could also be useful for the
LMAP usage scenario (e.g. extensions for incremental updates, for
asynchrounous change notifications and for encoding of multiple costs
within the same cost map) have been proposed and are currently being
discussed in the ALTO WG.
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4. Examples
[NOTE: syntax most certainly wrong!]
4.1. Download speeds
This section shows, as an example, how average download speeds
measured in a given time interval can be reported. The aggregation
approach in this case is based on ISP and geographical location. Two
types of data are reported in this example:
o data collected from measurements against specific endpoints (e.g.
active measurements);
o data collected from all measurements (e.g. passive measurements).
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4.1.1. Network map
{
"meta" : {},
"data" : {
"map-vtag" : "1266506139",
"map" : {
"ISP1-GEO1" : {
"ipv4" : [ "10.1.0.0/16", 172.20.0.0/16" ]
},
"ISP2-GEO1" : {
"ipv4" : [ "10.2.0.0/17" ]
},
"ISP3-GEO1" : {
"ipv4" : [ "10.3.0.0/16" ]
},
"ISP2-GEO2" : {
"ipv4" : [ "10.2.128.0/17" ]
},
"ISP4-GEO2" : {
"ipv4" : [ "10.4.0.0/16" ]
},
.
.
.
"MSMNT-CL1" : {
"ipv4" : [ "192.168.0.0/30" ]
},
"TOTAL" : {
"ipv4" : [ "0.0.0.0/0" ]
}
}
}
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4.1.2. Cost map
{
"meta" : {},
"data" : {
"cost-mode" : "numerical",
"cost-type" : "avg-dl-speed",
"map-vtag" : "1266506139",
"time-interval" : "2629740",
"map" : {
"ISP1-GEO1": { "MSMNT-CL1" : 13.2,
"TOTAL" : 10.2},
"ISP2-GEO1": { "MSMNT-CL1" : 11.4,
"TOTAL" : 12.3},
"ISP3-GEO1": { "MSMNT-CL1" : 13.2,
"TOTAL" : 10.2},
.
.
.
}
}
}
}
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5. References
5.1. Normative References
[RFC5693] Seedorf, J. and E. Burger, "Application-Layer Traffic
Optimization (ALTO) Problem Statement", RFC 5693,
October 2009.
5.2. Informative References
[I-D.ietf-alto-protocol]
Alimi, R., Penno, R., and Y. Yang, "ALTO Protocol",
draft-ietf-alto-protocol-13 (work in progress),
September 2012.
[I-D.schulzrinne-lmap-requirements]
Schulzrinne, H., Johnston, W., and J. Miller, "Large-Scale
Measurement of Broadband Performance: Use Cases,
Architecture and Protocol Requirements",
draft-schulzrinne-lmap-requirements-00 (work in progress),
September 2012.
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Appendix A. Acknowledgment
Jan Seedorf is partially supported by the mPlane project (mPlane: an
Intelligent Measurement Plane for Future Network and Application
Management), a research project supported by the European Commission
under its 7th Framework Program (contract no. 318627). The views and
conclusions contained herein are those of the authors and should not
be interpreted as necessarily representing the official policies or
endorsements, either expressed or implied, of the mPlane project or
the European Commission.
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Authors' Addresses
Jan Seedorf
NEC
Kurfuerstenanlage 36
Heidelberg 69115
Germany
Phone: +49 6221 4342 221
Fax: +49 6221 4342 155
Email: seedorf@neclab.eu
Vijay K. Gurbani
Bell Labs, Alcatel-Lucent
Email: vkg@bell-labs.com
Enrico Marocco
Telecom Italia
Via G. Reiss Romoli, 274
Turin 10148
Italy
Email: enrico.marocco@telecomitalia.it
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