IETF Next Steps in Signaling                                     C. Shen
Internet-Draft                                            H. Schulzrinne
Expires: April 2005                                          Columbia U.
                                                                  S. Lee
                                                             Samsung AIT
                                                            October 2004


       Internet Routing Dynamics and NSIS Related Considerations
                     draft-shen-nsis-routing-00.txt

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Copyright Notice

   Copyright (C) The Internet Society (2004).

Abstract

   This document presents the main results from a recent Internet
   routing dynamics measurement and discusses their impact on NSIS
   protocol design.  It also provides an evaluation of the simple, low
   cost packet TTL monitoring route change detection mechanism in the
   context of different NSIS deployment models.



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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  3
   2.  Measuring and Analyzing Internet Routing Dynamics from NSIS  .  3
     2.1   Measurement Methodology and Data Sets  . . . . . . . . . .  3
     2.2   Summary of Findings from an NSIS Perspective . . . . . . .  4
       2.2.1   Route Prevalence and Route Persistence . . . . . . . .  4
       2.2.2   Different Types of Route Changes . . . . . . . . . . .  4
       2.2.3   Accuracy of Measuring Path Characteristics . . . . . .  5
       2.2.4   Impact of Multi-Homing . . . . . . . . . . . . . . . .  5
   3.  NSIS-Concerned Route Changes and NSIS Deployment Models  . . .  5
   4.  Evaluation of Packet TTL Monitoring Based Route Change
       Detection  . . . . . . . . . . . . . . . . . . . . . . . . . .  7
   5.  Conclusions and Future Work  . . . . . . . . . . . . . . . . .  8
   6.  Security Considerations  . . . . . . . . . . . . . . . . . . .  8
   7.  Acknowledgements . . . . . . . . . . . . . . . . . . . . . . .  8
   8.  Informative References . . . . . . . . . . . . . . . . . . . .  9
       Authors' Addresses . . . . . . . . . . . . . . . . . . . . . .  9
       Intellectual Property and Copyright Statements . . . . . . . . 11
































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1.  Introduction

   Interaction with IP routing is an important aspect in Next Step In
   Signaling (NSIS) protocol design [1].  Solving this problem requires
   a good characterization of today's Internet's routing behavior.  In
   this memo we summarize the main results from a routing measurement
   experiment conducted between April and August 2004, and discuss their
   impact to the design of NSIS.  The focus of our routing study is
   route change.  We look at the types, duration and likely causes of
   different route changes observed.

   Various route change detection mechanisms have been proposed in [2].
   Some of them are simple and low cost, such as packet TTL monitoring.
   It will be very helpful to know how good such a mechanism will be.
   We evaluate the effectiveness of packet TTL monitoring in detecting
   route changes based on our measurement experiments.  We also
   introduce the concept of NSIS-concerned route changes, and look at
   the TTL monitoring method in the context of different NSIS deployment
   models.

   For details about our measurements and analysis, the readers are
   referred to our technical report [5].



2.  Measuring and Analyzing Internet Routing Dynamics from NSIS

2.1  Measurement Methodology and Data Sets

   We collected four data sets in our measurements that involved totally
   24 public traceroute servers located in US, Iceland, Netherlands,
   Australia, Germany, Switzerland, Bulgaria, Sweden, and Thailand.

   Data Set I (DS I) contains 12 sites and is collected from April 9,
   05:14:02 AM EDT 2004 to April 24, 00:09:34 AM EDT 2004, at an average
   per-site exponential sampling rate of one traceroute every 15
   minutes.  This corresponds to an average of 2.75 hours sampling
   interval for each path.  The independent and exponential sampling
   allows us to approximate the amount of time that the Internet spends
   in a particular route from the number of times we observe that
   particular route in our measurements.

   Data Set II (DS II) and Data Set III (DS III) contain 24 sites.  Each
   site is exponentially sampled on average every 30 minutes.  This
   corresponds to an average of 11.5 hours sampling interval for each
   path.  Data Set II is take
n from May 22 12:20:11 AM EDT to Jun 13
   12:24:19 PM EDT, 2004 and Data Set III is taken from June 14 03:05:23
   PM EDT to July 06 10:34:14 AM EDT, 2004.



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   Data Set IV (DS IV) contains both a ten-minute fixed interval and a
   two-hour exponential interval measurement conducted between four
   designated paths that are believed to be showing typical routing
   characteristics, i.e., they are not paths that display extremely high
   route fluctuations.  DS IV is collected from July 14 to August 3,
   2004.  The purpose of this data set is to provide additional insights
   on short scale routing dynamics and also to obtain some understanding
   about impacts of longer measurement intervals (posed by
   infrastructure restrictions) on the accuracy of measurements.

2.2  Summary of Findings from an NSIS Perspective

2.2.1  Route Prevalence and Route Persistence

   We identified the most frequently observed route, or dominant route,
   for each path, and computed the prevalence of each dominant route,
   which is the number of times the dominant route is sampled divided by
   the total number of samples for that path.  We also studied how long
   the path is likely to stay in one route, or route persistence, and
   obtained the duration distribution of long-lived routes.  The results
   confirmed some of the conclusions from earlier measurements [3][4],
   i.e., Internet paths are strongly dominated by a single route, but
   significant site to site variation exists.  The strong dominance of a
   single route is good for NSIS.  To cope with significant site to site
   variations, it will be helpful for NSIS to employ an adaptive
   approach in dealing with routing dynamics.  For example, the NSIS
   protocol should be more aggressive in detecting route changes on a
   path that is particularly instable, by using a shorter path refresh
   interval, or advanced route change detection mechanisms.

2.2.2  Different Types of Route Changes

   Our statistics of route changes shows that route changes occur over a
   wide range of time scales, ranging from seconds to days, and over
   different network scales, ranging from intra-AS changes to inter-AS
   changes.  The majority of route changes are found to be involving no
   change of total number of hops (i.e., TTL-invisible).  However, a
   large proportion of these TTL-invisible route changes are caused only
   by a small set of (mostly local) router pairs belonging to the same
   service provider or host network, which are probably doing route
   splitting and load balancing.  The router administrators have the
   best information on how the routers are configured and therefore are
   the best persons to take care of these route changes.

   Route splitting is a cause to extremely short period route changes
   (called route fluttering [3]), at the scale of seconds or minutes.
   It is controlled by a special option of an IPv4 router and can be
   turned off [5].  Load balancing usually leads to short or medium



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   scale route changes and are seen in routers within both host and
   service provider networks.  Extra mechanisms need to be deployed in
   these networks to allow proper functioning of NSIS in the presence of
   load balancing.  These may include special route monitoring
   mechanisms to quickly detect the route change and notify related NSIS
   modules about the change, or establishing redundant state information
   in load balancing routers.  The latter approach tends to cost more
   resources but gives a more prompt response time.

2.2.3  Accuracy of Measuring Path Characteristics

   Our analysis of DS IV showed that the accuracy of measuring the path
   characteristics with a longer measurement interval depends heavily on
   the stability of the path being studied.  Using a ten-minute fixed
   interval and a two-hour exponential interval measurement, we have
   seen examples where both measurements capture the same number of
   routes, AS-paths and their changes; we have also seen examples where
   the two-hour interval measurement missed about half the number of
   unique routes, AS-paths and their changes.  (During our processing of
   other data sets, we used several techniques to remove outliers that
   showed great instability in order to counter this effect.) This issue
   is essentially the same as the site to site variation problem
   mentioned in 3.2.1 and calls for adaptive NSIS mechanisms to
   characterize or interact with path dynamics.  An example will be an
   NSIS refresh mechanism that starts with a short interval value and
   gradually increases that interval depending on the actual routing
   characteristics it observes.

2.2.4  Impact of Multi-Homing

   Multi-homing is often seen in our measurements and causes AS level
   route changes as well as asymmetric routing.  A particular example we
   encountered in DS IV is a site that uses one ISP as the incoming ISP
   and another as the outgoing ISP.  We've also seen the same site using
   one ISP as its primary outgoing ISP in May and June and then switch
   to another one as primary outgoing ISP in July.  The result is a
   dramatic change of route characteristics, from a very stable route to
   a very fluctuating route.  Moreover, the site still occasionally uses
   the old AS-path for a while, causing short term AS level route
   changes.  If the multi-homed site has control over such behaviors, it
   should deploy appropriate mechanisms to notify related NSIS entities
   about the switch to allow a fast NSIS recovery.



3.  NSIS-Concerned Route Changes and NSIS Deployment Models

   We have mentioned that the majority of route changes are local and



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   TTL-invisible so they are best tackled by the router administrators,
   or by keeping NSIS in mind when turning on related routing options.
   The remaining of the route changes, many of which non-trivial in that
   they could affect more than one hops in different geometrical
   locations, tend to have bigger impact on the applications using NSIS
   protocol and are more concerned by the NSIS community.  It will be
   very nice if many of these changes can be detected by simple
   mechanisms like packet TTL monitoring, without special routing
   monitoring mechanisms installed by service provider or host network
   administrators.  We will look at what our experiments show about the
   effectiveness of the TTL monitoring method.  But before that, we
   introduce the concept of NSIS-concerned route changes and NSIS
   deployment models.

   From a network perspective, generic route changes can be classified
   into inter-AS and intra-AS route changes depending on whether the
   route change affects the AS set in the path.  Intra-AS route changes
   may further be divided into ingress-point, egress-point and mid-point
   route change, depending on the location of the routers in the AS
   where the route change occurred.

   NSIS needs to deal with all generic route changes only in a full NSIS
   network where all nodes are NSIS Entities (NEs).  A far more likely
   network scenario will be a mixed deployment of NEs and normal
   routers.  In this case, only a subset of all generic route changes
   will involve change of NEs.  These are NSIS-concerned route changes
   that should be dealt with by NSIS.

   To better understand NSIS-concerned route changes, we need to make
   assumptions about the actual NSIS deployment models.  We list below
   possible NSIS deployment models in a mixed environment of NEs and
   normal routers, together with the correspondin
g NSIS-concerned route
   changes in each model.

   - AS model: in the AS model each AS deploys a central NE that is
      responsible for NSIS related signaling for this AS.  The
      NSIS-concerned route changes in AS model include all route changes
      that involve AS changes, i.e., inter-AS route changes.

   - Entry model: in the entry model the ingress routers of each AS are
      NEs.  NSIS-concerned route changes in this model include inter-AS
      and intra-AS ingress point route changes.

   - Border model: in the border model both ingress and egress routers
      of each AS are NEs.  NSIS specific route changes in this model
      include inter-AS, intra-AS ingress point and intra-AS egress point
      changes.




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   - Edge model: in the edge model the access routers of the source and
      destination sites are NEs.  NSIS-concerned route changes in this
      model include inter-AS route change involving the first or last
      AS, intra-AS ingress point route change in the first AS as well as
      intra-AS egress point route change in the last AS.

   - Generic model: we define the generic model as all other mixed
      deployments that cannot be clearly mapped to any of the above four
      categories.  It might be a combination of the above models plus
      more NEs in certain parts of the network.  There is no
      straightforward way to define an NSIS-concerned route change in
      this case other than to observe whether a change of NEs is
      involved once a route change has occurred.



4.  Evaluation of Packet TTL Monitoring Based Route Change Detection

   The data in this section is abstracted from our routing measurements
   (Section 3).  We started by assuming a full NE deployment model where
   all route changes count.  Table 1 shows the proportion of TTL visible
   route changes and AS path changes in our data sets I, II and III.

        +------------------------------+------+-------+--------+
        | Item description             | DS I | DS II | DS III |
        +------------------------------+------+-------+--------+
        | TTL-visible route changes    | 38%  | 25%   | 23%    |
        | AS level route changes       | 18%  | 8%    | 8%     |
        | TTL-visible AS level changes | 77%  | 83%   | 88%    |
        +------------------------------+------+-------+--------+

           Table 1: TTL-visible route changes and AS changes

   The percentage of overall TTL-visible route changes does not seem
   very promising.  However, even in an all NE network, the route
   changes that concern us most are always non-trivial ones.  If we
   focus on those AS level changes, we can see that TTL does a fairly
   good job in detecting roughly 4 out of 5 such changes.

   We also examined the effectiveness of TTL in detecting NSIS-concerned
   route changes in four mixed NSIS deployment models as shown in Table
   2.  Clearly the TTL detection mechanism works better in these mixed
   models than in the all NE model.  This is because NSIS-concerned
   route changes in all these four models are usually non-trivial
   changes, where TTL monitoring tends to be more effective.  The table
   also shows that the successful TTL detection ratio tends to be higher
   when the deployment model is sparser in terms of NEs and thus fewer
   route changes become NSIS-concerned.  Therefore, the edge model and



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   the AS model have higher detection ratio than the entry and border
   models.  It is not possible to check the effectiveness of TTL
   detection mechanism in a generic mixed model when detailed deployment
   information is unknown, but the answer is expected to fall between
   the full NE model and the four mixed models we have studied.

                +--------------+------+-------+--------+
                | NSIS model   | DS I | DS II | DS III |
                +--------------+------+-------+--------+
                | AS model     | 77%  | 83%   | 88%    |
                | Entry model  | 51%  | 41%   | 40%    |
                | Border model | 45%  | 39%   | 38%    |
                | Edge model   | 74%  | 90%   | 92%    |
                +--------------+------+-------+--------+

    Table 2: Effectiveness of TTL detection in the four mixed models

   Overall, the TTL monitoring method appears to be a reasonably good
   way to find non-trivial route changes, although this mechanism alone
   is hardly enough to detect all NSIS-concerned route changes.



5.  Conclusions and Future Work

   We performed a routing measurement on today's Internet and studied
   the impact of general Internet routing dynamics, especially route
   changes, on NSIS design.  We concluded that different types of route
   changes require different handling.  Frequent yet local route changes
   caused by route splitting or load balancing are best handled by
   routing monitoring inside the network; many non-trivial
   NSIS-concerned route changes may be detected by hosts employing
   simple packet TTL monitoring mechanism, as we have seen in typical
   NSIS deployment models.  We are currently also investigating other
   route change detection methods to see when and how they are likely to
   be useful.



6.  Security Considerations

   There are no explicit security issues associated with current version
   of this document.



7.  Acknowledgements




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   We would like to thank Jongho Bang for their comments and support in
   this work.We would also like to thank all traceroute server sites
   that participated in our measurement.



8  Informative References

   [1]  Hancock, R., "Next Steps in Signaling: Framework",
        draft-ietf-nsis-fw-06 (work in progress), July 2004.

   [2]  Schulzrinne, H., "GIMPS: General Internet Messaging Protocol for
        Signaling", draft-ietf-nsis-ntlp-04 (work in progress), October
        2004.

   [3]  Paxson, V., "Measurements and Analysis of End-to-End Internet
        Dynamics", PhD thesis, University of California, Berkeley, 1997.

   [4]  Zhang, Y., "Characterizing End-to-End Internet Performance", PhD
        thesis, Cornell University, 2001.

   [5]  Baker, F., "Requirements for IP Version 4 Routers", RFC 1812,
        June 1995.

   [6]  Shen, C. and H. Schulzrinne, "Internet Routing Dynamics and NSIS
        Related Considerations", technical report, Columbia University,
        October 2004.


Authors' Addresses

   Charles Shen
   Columbia University
   Department of Computer Science
   1214 Amsterdam Avenue, MC 0401
   New York, NY  10027
   USA

   Phone: +1 212 854 5599
   EMail: charles@cs.columbia.edu


   Henning Schulzrinne
   Columbia University
   Department of Computer Science
   1214 Amsterdam Avenue, MC 0401
   New York, NY  10027
   USA



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   Phone: +1 212 939 7004
   EMail: schulzrinne@cs.columbia.edu


   Sung-Hyuck Lee
   SAMSUNG Advanced Institute of Technology
   San 14-1, Nongseo-ri, Giheung-eup
   Yongin-si, Gyeonggi-do  449-712
   KOREA

   Phone: +82 31 280 9585
   EMail: starsu.lee@samsung.com







































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