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LMAP                                                          J. Seedorf
Internet-Draft                                                       NEC
Intended status: Informational                                D. Goergen
Expires: April 24, 2014                                         R. State
                                                University of Luxembourg
                                                              V. Gurbani
                                               Bell Labs, Alcatel-Lucent
                                                              E. Marocco
                                                          Telecom Italia
                                                        October 21, 2013

                     ALTO for Querying LMAP Results


   In the context of Large-Scale Measurement of Broadband Performance
   (LMAP), measurement 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.  Finally, we describe our methodology for
   analyzing the United States Federal Communication Commission's (FCC)
   Measuring Broadband America (MBA) dataset to derive required topology
   and cost maps suitable for consumption by an ALTO server.

Status of this Memo

   This Internet-Draft is submitted in full conformance with the
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   This Internet-Draft will expire on April 24, 2014.

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Copyright Notice

   Copyright (c) 2013 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (http://trustee.ietf.org/license-info) in effect on the date of
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   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

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.  Discussion of Useful ALTO Extensions . . . . . . . . . . . . . 10
   6.  Case study: Analyzing a large-scale dataset  . . . . . . . . . 11
     6.1.  Challenges in data analysis  . . . . . . . . . . . . . . . 11
     6.2.  Geo-locating the units . . . . . . . . . . . . . . . . . . 12
   7.  Security considerations  . . . . . . . . . . . . . . . . . . . 15
   8.  IANA considerations  . . . . . . . . . . . . . . . . . . . . . 16
   9.  Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 17
   10. References . . . . . . . . . . . . . . . . . . . . . . . . . . 18
     10.1. Normative References . . . . . . . . . . . . . . . . . . . 18
     10.2. Informative References . . . . . . . . . . . . . . . . . . 18
   Appendix A.  Acknowledgment  . . . . . . . . . . . . . . . . . . . 19
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 20

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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

   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

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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 or may not be employed depending on the average
      connectivity type/speed of a user in a given region or with a
      given access network provider.

   o  Display estimation of service quality or total download time to
      users: A webservice could use statistics about average download
      speeds for a given ISP and/or region to estimate Quality-of-
      Service for provided services (e.g. to indicate to the user what
      Quality-of-Experience to expect when clicking on a given link) or
      to estimate (and display to the user) the total download time for
      given content.

   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 increasingly 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

   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" : [ ""," ]
       "ISP2-GEO1" : {
         "ipv4" : [ "" ]
       "ISP3-GEO1" : {
         "ipv4" : [ "" ]
       "ISP2-GEO2" : {
         "ipv4" : [ "" ]
       "ISP4-GEO2" : {
         "ipv4" : [ "" ]


       "MSMNT-CL1" : {
         "ipv4" : [ "" ]
       "TOTAL" : {
         "ipv4" : [ "" ]

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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.  Discussion of Useful ALTO Extensions

   The base ALTO Protocol as specified in [I-D.ietf-alto-protocol] can
   in principle be used to enable a more flexible way to provide access
   to large-scale network measurement results as discussed in the
   previous sections of this document.  However, certain extensions to
   the base ALTO Protocol that have recently been proposed in the ALTO
   WG would allow to better enable the use cases discussed in Section 2:

   o  Server-initiated Notifications: In [I-D.marocco-alto-ws], it has
      been proposed to enhance the ALTO protocol such that servers can
      notify clients about newly available ALTO maps.  In the context of
      this document, this extension would allow applications to be
      notified when certain new LMAP measurements are available, such as
      new measurement results on average download speeds.  These new
      results could then be downloaded and used immediately by

   o  Incremental Updates: In [I-D.schwan-alto-incr-updates], it has
      been proposed to enhance the ALTO protocol with incremental
      updates, such that clients can retrieve partial updates for ALTO
      maps instead of always downloading a full ALTO map (even when only
      a small fraction of the ALTO map has changed compared to a
      previous version).  When ALTO is used for querying LMAP results,
      the corresponding ALTO maps may potentially be quite large (e.g.
      when a webservice queries for particular, detailed results
      regarding a whole ISP).  In this case, incremental ALTO updates
      would be a very useful mechanism for applications to retrieve
      updates of ALTO maps, as a reduced amount of data would be needed
      for transmitting these maps.

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6.  Case study: Analyzing a large-scale dataset

   Measuring broadband performance is increasingly important as
   communications continue to move towards the Internet.  Internet
   service providers (ISP), national agencies and other entities gather
   broadband data and may provide some, or all, of the dataset to the
   public for analysis.  As we argue above, there are two extremes
   prevalent for presenting large-scale data.  One is in the form of
   charts, figures, or summarized reports amenable for easy and quick
   consumption.  The other extreme includes releasing raw data in the
   form of large files containing tables formatted as values separated
   by a delimiter.  While the former is indispensable to acquire a
   summary view of the dataset, it does not suffice for additional
   analysis beyond what is presented.  Conversely, the problem with the
   latter option (raw files) is that the unsuspecting user perusing them
   is lost in the deluge of data.

   We offer the argument that a reasonable medium between the two
   extremes may be the ALTO protocol [I-D.ietf-alto-protocol].  A
   necessary prerequisite for using ALTO is abstracting the network
   information into a form that is suitable for consumption by the
   protocol.  The implication of using ALTO is that data from any large-
   scale measurement effort must first be distilled in two maps: a
   topology map and a cost map.  Further analysis and ad-hoc queries can
   be subsequently performed on the normalized dataset.

   In the United States, the Federal Communication Commission (FCC) has
   embarked on a nationwide performance study of residential wireline
   broadband service [fcc].  Our aim is to use the raw datasets from
   this study for analysis and to create a topology map and a cost map
   from this dataset.  ALTO queries aimed at these maps will enable
   users and interested parties to fulfill the use cases listed in
   Section 2.

6.1.  Challenges in data analysis

   The FCC Measuring Broadband America (MBA) study consisted of 7,782
   volunteers spread across the United States with adequate geographic
   diversity.  Volunteers opted in for the study, however, each of the
   volunteers remained anonymous.  An opaque integral number (unit_id)
   represented a subscriber in the raw dataset.  This unit_id remains
   constant during the duration of the study in the dataset and uniquely
   identifies a volunteer subscriber, even if the subscriber switches
   the ISP.  More detail about the methodology used is described in

   The dataset consisted of 12 tables, each table corresponding to the
   data drawn from a certain performance test.  For the analysis we

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   present in this document we focus on the "curr_dns" table, which
   contains the time taken for the ISP's recursive DNS resolver to
   return a DNS A RR for a popular website domain name.  This test was
   ran approximately every hour in a 24-hour period, and produced about
   75-78 million records per month.  This resulted in a typical file
   size in the range of 6-7 GBytes per month.  We note that the
   "curr_dns" table is one of the smaller tables in the dataset.

   The first challenge, therefore, was to arrive at computing resources
   comparable in scale with respect to the dataset consisting of
   millions of records spread across gigabyte-sized files.  To analyze
   the volume of data we used a canonical Map-Reduce computational
   paradigm on a Hadoop cluster (more details on the methodology are
   outlined in Section 6.2).

   A second, more pressing challenge, was to identify the geographic
   location of the unit_ids generating the data.  In order to derive a
   topological map and impose costs on the links, it is important to
   know the physical locations of the unit_ids that contributed the
   measurements.  However, in the MBA dataset, the population is
   anonymized and the individual subscriber reporting the measurement
   data is simply referred to by an opaque integral number.  Therefore,
   an important task was to use the information in the public tables to
   reveal a coarse location of the subscriber.

   We outline the methodology we used to do so in the next section.  We
   stress that this methodology does not identify the specific location
   of a subscriber, who still remains anonymous.  Instead, it simply
   locates the subscriber in a larger metropolitan region.  This level
   of granularity suffices for our work.

6.2.  Geo-locating the units

   To geo-locate the units, we simply note that broadband subscriber
   devices are likely to be configured using DHCP by their ISP.  Besides
   imparting an IP address to the subscriber device, DHCP also populates
   the DNS name servers the subscriber devices uses for DNS queries.  In
   most installations, these DNS name servers are located in close
   physical proximity of the subscriber device.  The FCC technical
   appendix states that the DNS resolution tests were targeted directly
   at the ISP's recursive resolvers to circumvent caching and users
   configuring the subscriber device to circumvent the ISP's DNS
   resolvers.  Therefore, a reasonable approximation of a subscribers
   geo-location could be the geographic location of the DNS name server
   serving the subscriber.  We use this very heuristic to geo-locate a

   Thus our first, and very simple filter consisted of obtaining a

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   mapping from a unit_id (representing a subscriber) to one or more DNS
   name servers that the unit_id is sending DNS requests to.  It turned
   out that while this was a necessary condition for advancing, it was
   not a sufficient one.  The raw data would need to be further
   processed to reduce inconsistencies and remove outliers.  A number of
   interesting artifacts were uncovered during further processing of the
   data.  These artifacts informed the selection of the unit_ids for
   further analysis.

   The artifacts are documented below.

   o  A handful of unit_ids were geo-located in areas outside the
      contiguous United States, such as Ukraine, Poland or the United
      Kingdom.  We theorize that the subscribers corresponding to the
      unit_ids geo-located outside the contiguous United States had
      simply configured their devices to use alternate DNS servers,
      probably located outside the United States.  We removed these
      records before conducting our analysis.

   o  We also observed a reasonable number of non-ISP DNS resolvers,
      especially Google's and and OpenDNS
      and  These 4 public DNS servers are geo-located in
      California.  We removed these records to ensure that the specific
      location that these resolvers represented was not oversampled.

   o  We noticed that a large number of unit_ids were being geo-located
      in Potwin, Kansas.  Intrigued as to why there appeared to be a
      large population of Internet users being located in a small rural
      community in Kansas, we investigated further.  It appears that
      Potwin, Kansas is the geographical center of the United States and
      a number of ISPs have chosen to establish data centers in or
      around the Potwin area.  These ISPs generally locate their primary
      or secondary DNS name servers in Potwin-area data centers, thus
      accounting for the popularity of Potwin as an Internet
      destination.  We continue to further investigate on minimizing the
      impact of such natural aggregation points that, if not accounted
      for, will skew our results in an unwarranted direction.

   o  We observed some unit_ids changing ISPs during the observation
      period.  This is a normal occurrence and to the extent that the
      unit_id is geo-located in the same geographical area after the
      change in ISP, we do not exclude such unit_ids from further

   Subsequent filters extracted the stable unit_ids from our dataset.
   In order to determine which unit_id are stable, i.e., remain constant
   with respect to their geographic location over the observation period
   from January to December 2012, we extracted for each unit_id the IP

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   address of each DNS name server it consulted.  This is obtained by
   applying the map reduce paradigm on the DNS dataset.  We extracted
   for each unit_id the triggered DNS servers and obtained the
   individual DNS servers accessed by a unit_id.  This was repeated for
   each month of the observation period.  The resulting sets were
   cleaned up of private IP addresses and other artifacts discussed
   above.  The cleaned set consisted of about 8000 distinct unit_id.

   In order to determine the stability of each unit_id we proceeded to
   sum up the occurrences of IP addresses over the whole observation
   period separated in monthly files.  If the IP address of a DNS server
   occurred 12 times this meant that the unit_id always accessed the
   same DNS server and therefore remained stable over the observation
   period.  The obtained stable unit_ids, around 1500, will be used for
   further analysis.  Assuming a 99% confidence level and +/- 3 point
   margin of error, we will require a sample of 1494 unit_ids.  With our
   stable unit_id set of 1500 unit_ids, we are now positioned to perform
   further analysis on the dataset to create the full topology and cost

   Table 1 presents a sample of the geographic location data that we
   have uncovered for unit_ids.  A complete list of identified units
   superimposed on the geographical map of the United States is
   available at http://cdb.io/13UOHgD.

         | Unit ID | City, State     | Latitude/Longitude       |
         | 872     | Morganville, NJ | 40.35950089,-74.26280212 |
         |         |                 |                          |
         | 885     | Madison, WI     | 43.07310104,-89.40119934 |
         |         |                 |                          |
         | 898     | Foley, AL       | 30.40660095,-87.68360138 |
         |         |                 |                          |
         | 7969    | Manteca, CA     | 37.79740143,-121.2160034 |
         |         |                 |                          |
         | 8024    | Quincy, MA      | 42.25289917,-71.00229645 |

                     Sample unit identification tuples

                                  Table 1

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7.  Security considerations

   There are no security artifacts invalidated due to our analysis in
   Section 6.  All of our analysis was performed on publicly available
   data.  However, we do note that some privacy may have been lost based
   on our analysis.  In the raw dataset, the unit identifiers are opaque
   strings with no immediate correlation with a geographic location.
   After our analysis, while the unit identifiers still remain opaque,
   they are nonetheless correlated to a specific, though coarse,
   geographic location.

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8.  IANA considerations

   This document does not contain any IANA considerations.

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9.  Conclusion

   This document argues that, compared to existing solutions, there may
   be a need for a more flexible way to provide access to large-scale
   network measurement results.  Further, the document argues that the
   ALTO protocol is a good candidate to enable querying for specific,
   possibly aggregated, measurement results in a flexible way.  Examples
   of how such a flexible query meachnism for large-scale measurement
   results could look like based on ALTO are given.

   With respect to the case study in Section 6, identification of the
   geographic location of the unit_ids generating the performance data
   is essential in order to continue the work.  We have presented a
   methodology and some early results in identifying a geographic
   location.  This location, although coarse, suffices for our future
   work that will consist of further data mining and analysis to create
   appropriate ALTO network and cost maps.

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10.  References

10.1.  Normative References

   [RFC5693]  Seedorf, J. and E. Burger, "Application-Layer Traffic
              Optimization (ALTO) Problem Statement", RFC 5693,
              October 2009.

10.2.  Informative References

              Alimi, R., Penno, R., and Y. Yang, "ALTO Protocol",
              draft-ietf-alto-protocol-20 (work in progress),
              October 2013.

              Marocco, E. and J. Seedorf, "WebSocket-based server-to-
              client notifications for the Application-Layer Traffic
              Optimization (ALTO) Protocol", draft-marocco-alto-ws-01
              (work in progress), July 2012.

              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.

              Schwan, N. and B. Roome, "ALTO Incremental Updates",
              draft-schwan-alto-incr-updates-02 (work in progress),
              July 2012.

   [fcc]      United States Federal Communications Commission,
              "Measuring Broadband America", Accessed July 12,
              2013, http://www.fcc.gov/measuring-broadband-america.

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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
   Kurfuerstenanlage 36
   Heidelberg  69115

   Phone: +49 6221 4342 221
   Fax:   +49 6221 4342 155
   Email: seedorf@neclab.eu

   David Goergen
   University of Luxembourg

   Email: david.goergen@uni.lu

   Radu State
   University of Luxembourg

   Email: radu.state@uni.lu

   Vijay K. Gurbani
   Bell Labs, Alcatel-Lucent

   Email: vkg@bell-labs.com

   Enrico Marocco
   Telecom Italia
   Via G. Reiss Romoli, 274
   Turin  10148

   Email: enrico.marocco@telecomitalia.it

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