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Network Working Group                                         F. Devetak
Internet-Draft                                                 S. Kapoor
Expires: September 14, 2017             Illinois Institute of Technology
                                                          March 13, 2017


                       Dynamic MultiPath Routing
            draft-kapoor-rtgwg-dynamic-multipath-routing-00

Abstract

   In this draft we consider dynamic multipath routing and introduce two
   methods that use additive increase and multiplicative decrease for
   flow control, similar to TCP.  Our first method allows for congestion
   control and re-routing flows as users join in or leave the network.
   As the number of applications and services supported by the Internet
   grows, bandwidth requirements increase dramatically so it is
   imperative to design methods to ensure not only that network
   throughput is maximized but also to ensure a level of fairness in
   network resource allocation.  Our second method provides fairness
   over multiple streams of traffic.  We drive the multiplicative
   decrease part of the algorithm with link queue occupancy data
   provided by an enhanced routing protocol.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
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   This Internet-Draft will expire on September 14, 2017.

Copyright Notice

   Copyright (c) 2017 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



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1.  Introduction

   Internet packet traffic keeps growing as the number of applications
   and services it supports as well as their bandwidth requirements
   explode.  It has then become necessary to find ways to ensure that
   network throughput is maximized.  In this draft we propose dynamic
   multi-path routing to improve network throughput.

   Multipath routing is important, not only for throughput but also for
   reliability and security.  In multipath routing, improvements in
   performance are achieved by utilizing more than one feasible path
   [M75].  This approach to routing makes for more effective network
   resource utilization.  Various research on multipath routing have
   addressed network redundancy, congestion, and QoS issues [CRS99]
   [ST92].  Prior work on multipath routing includes work on bounding
   delays as well as delay variance [DSAK11] [SKDA13].

   The prior work is primarily from the viewpoint of static network
   design but, in practice, congestion control is necessary to prevent
   some user flows from being choked due to link bottlenecks.  Single
   path routing implementations of TCP achieve that by rate control on
   specified paths.  TCP is able to handle elastic traffic from
   applications and establishes a degree of fairness by reducing the
   rate of transmission rapidly upon detecting congestion.  Regular TCP
   has been shown to provide Pareto-optimal allocation of resources
   [PU12].  However, unlike the single path approach of TCP, we consider
   multipath routing with associated issues of path selection and
   congestion.  We may note that multipath TCP (MPTCP) has been studied
   extensively [RG10] [GWH11] [RH10] [AP13] with a number of IETF
   proposals [M05] [M06] [M07] [M08].  Prior work on multipath TCP is
   defined over a specific set of paths and the choice of paths or the
   routing is independent of congestion control; determining the right
   number of paths thus becomes a problem.  The variation of throughput
   with the number of paths has been illustrated in [RG10] [GWH11]

   Along with consideration of congestion, we also need to ensure a
   level of fairness in network resource allocation.  Factoring fairness
   into the protocol is important in order to prevent some user's flows
   from suffering due to bottlenecks in some links.  Based on
   mathematical optimization formulations, we consider route



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   determination methods that ensure fairness where all users can
   achieve at least a minimum percentage of their demand.  We introduce
   an algorithm that uses additive increase and multiplicative decrease
   for flow control and we have experiments to illustrate its stability
   and convergence.  The algorithm may be considered as a generalization
   of TCP.

   We have performed an extensive set of simulations using the NS-3
   simulation environment.  In our implementation we drive the
   multiplicative decrease part of the algorithm using queue occupancy
   data at each outgoing network link, with that data provided by an
   enhanced routing protocol.  For a more in-depth evaluation of the
   algorithm's performance we simulated not only the fairness algorithm
   but also a version of the same without the fairness component.  We
   also performed and compared simulations using standard TCP and TCP
   with ECMP enabled.

2.  Joint Routing and Congestion Control: Preliminaries

   The Joint Routing and Congestion Control utilizes the Link State of
   the network.  The algorithm utilizes a price variable that models
   congestion at each link and a variable that models the fairness
   coefficient.  The fairness coefficient is used to establish the same
   percentage of traffic is being routed for multiple source-sink pairs.

2.1.  The Price function

   Each edge of the network has a price function associated with it,
   referred to as P(e).  The price function measures the congestion on
   the link.  The price function lies in the interval [0,1] and is 0 if
   the edge occupancy is low and 1 if the edge occupancy is high.

   In theory the edge occupancy is given by f(e)/C(e) where f(e) is the
   amount of traffic on the link and C(e) is the capacity of the link.
   In practice, the edge occupancy is measured by the congestion in the
   queue serving the link.

   The price function increases as the congestion grows.  This
   function's values will be referred to by a price variable on link e
   which is denoted by PBQ(e)

2.2.  The Fairness function

   The price function is complemented by an "increase" function, i.e. a
   variable that regulates the amount of traffic changes based on the
   fraction of traffic of the commodity that has been routed.  This
   function, th values represented by the variable PBF(s-t), is used to
   model the fairness.  This variable is related to the source-



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   destination pair, denoted by s-t, whose requirements are being
   satisfied.

   The variable PBF(s-t) starts with an initial value and goes down to
   zero as the requirement of s-t is being increasingly met.  The
   increase in PBF is dictated by a fairness co-efficient, Gamma.

   The formula for PBF(s-t) is 1- y(s-t)/[Gamma *d(s-t)] where Gamma is
   the fairness co-efficient, d(s-t) is the demand and y(s-t) is the
   amount of requirement that is being met.

3.  Joint Routing and Congestion Control: Algorithm

   We present the details of the algorithms below

3.1.  Preliminaries

   Let T be the time interval used to increment or modify routing.

   We use two coefficients for each path Pi

   1.  Additive increase coefficient: A positive value ai by which we
       increment the flow on a path at each iteration xi(t) = xi(t-T ) +
       ai

   2.  Multiplicative decrease coefficient: The coefficient bi that we
       apply to decrement flows: xi(t) = (1-bi)xi(t-T ) where x, t, T
       are the same as above.

   We utilize multiple methods for calculating bi:

   METHOD 1: bi may be computed as follows:

   1.  bi =0 if no edge on the path Pi is congested.

   2.  bi =0.5 if one edge on the path Pi is congested.

   3.  bi= 1.0 if more than one edge on the path Pi is congested

   METHOD 2: bi may be computed as follows:

   1.  bi = 1-1/2^c where c is the number of congested edges.

3.2.  The Basic Multi-path Algorithm

   We propose two routing mechanisms.  The first, presented here,is a
   basic mechanism that is primarily based on multiplicative decrease
   and additive increase



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   In the first routing method, for each commodity c, let P(c) be the
   set of paths being used.  If any of the paths Pi is congested, the
   flow on the path is reduced using the multiplier (1-bi).  Next, the
   shortest path is found to push additional flow requirements if they
   exist.  An additive flow of value ai, which is chosen to be a
   constant independent of i, is pushed on that path.

    At the end of each time interval T
           FOR each  commodity c:
           Calculate commodity flow
                   FOR each flow path, i, of commodity c
                           Calculate bi
                           IF {demand is met and bi = 0}
                                   No change in path flow
                           ELSE
                                   Apply coefficient bi to
                                           decrease path flow
                           ENDIF
                   ENDFOR
                   IF {demand not met}
                           Find shortest path for commodity c
                           IF{shortest Path is new}
                                   Add new path to list
                           ENDIF
                           Increment shortest path flow by a
                   ENDIF
           ENDFOR

   If the number of paths used is excessive then no new paths need be
   generated.

3.3.  The Multi-path Algorithm with Fairness

   In order to ensure that different source-sink pairs are treated
   fairly, the coefficient bi for path Pi is chosen as PBQ - PBF with
   two components: a congestion component PBQ and a fairness component
   PBF .

   1.  PBQ is calculated as bi before;

   2.  PBF is calculated using the formula PBF(s-t) =
       1-Total_current_FLOW(S-t)/(Gamma* Demand(s-t))

   where Gamma is the fairness parameter and DEMAND(s-t) is the demand.







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   In the second routing method, again, for each commodity c, let P(c)
   be the set of paths being used.  If any of the paths is congested,
   the flow on the path Pi is reduced using the multiplier (1-bi).  The
   key difference is in the computation of bi. bi is not uniform across
   various user requests but is dependent on the fraction of flow of
   that commodity that is already being serviced by the network.  Having
   reduced congestion, if it exists, a shortest path is found to push
   additional flow requirements if they exist.  Again an additive flow
   of value ai, which in our current implementation is chosen to be a
   constant independent of i, is pushed on that path.


           At the end of each time interval T
           FOR {each  commodity}
                   Calculate commodity c flow
                   FOR {each path i}
                           Calculate PBQ and PBF
                           IF {demand is met}
                                   IF {NO Congestion}
                                            No change in path flow
                                   ELSE
                                           bi = max (0, PBQ-PBF)
                                           flow on path i,
                                           xi(t) = (1-bi)*xi(t-T))
                                   ENDIF
                           ELSE
                                   IF {NO Congestion}
                                           bi = -PBF
                                   ELSE
                                           bi = max (0, PBQ-PBF)
                                   ENDIF
                                   xi(t) = a + (1-bi)*xi(t-T))
                           ENDIF
                   ENDFOR
                   Recalculate Commodity flow
                   IF {flow change in any path AND demand not met}
                           Find shortest path
                           IF {shortest path is new}
                                    Add new path to list
                           ENDIF
                           Increment shortest path flow by a
                   ENDIF
           ENDFOR


   If the number of paths become excessive then they can be curtailed.
   At that stage no additional flow is pushed until congestion is
   relieved.



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4.  Conclusion

   We implemented [1] a discrete time version of the two algorithms
   using the NS-3 simulation environment.  We modeled the network
   topology on the network of a large service provider, with link
   capacities proportional to capacities in the actual physical network.
   In our implementation we used, for routing, a combination of link-
   state routing protocol and source routing.  For the link-state part
   we augmented the NS-3 implementation of the OSPF routing protocol by
   adding link queue occupancy to the data exchanged by nodes in Link
   State Advertisement (LSA) messages, a minimal increase in LSA data.
   That allows for more sophisticated monitoring of network status: if
   the queue occupancy for one of more links of a path exceeds a given
   threshold we conclude that the path is experiencing congestion and
   that the multiplicative decrease has to be applied to adjust the
   allocation of flow to the paths.  The additive increase is applied at
   each iteration, if demand is not met, to augment the sending rate.
   The source node uses OSPF to find the shortest path to the
   destination and, based on available network data, builds a source-
   routing vector that is inserted in the packet and used by
   intermediate nodes to route the packet to the destination.  To
   implement the source-routing function we augmented the NS-3 Nix-
   Vector protocol that builds the source-routing vector from the list
   of nodes to be traversed, list that is obtained from OSPF.  The main
   process is iterative as we refresh LSA at a fixed interval: for our
   simulations we experimented with updating LSAs every 50 ms and 500
   ms.

   In conclusion we found that our algorithm with fairness provides
   throughput improvement over both regular TCP and TCP with ECMP.  In
   addition, its ability to discover additional path dynamically
   eliminates the need to set a preselected set of paths, allowing the
   spreading of the traffic load amongst a wider but still reasonable
   set of paths.  The results may be found at
   www.cs.iit.edu/~kapoor/papers/reducerate.pdf .

5.  References

5.1.  References

   [M75]      Maxemchuk, N., "Dispersity Routing", IEEE ICC, 1975.

   [CRS99]    Cidon, I., Rom, R., and Y. Shavitt, "Analysis of multi-
              path routing", IEEE/ACM Trans on Networking pages 885-896,
              1999.






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   [ST92]     Suzuki, H. and F. Tobagi, "Fast bandwidth reservation with
              multiline and multipath routing in atm networks",
              Proceedings of IEEE Infocom pages 2233-2240, 1992.

   [DSAK11]   Devetak, F., Shin, J., Anjali, T., and S. Kapoor,
              "Minimizing path delay in multipath networks", IEEE ICC,
              2011.

   [SKDA13]   Devetak, F., Anjali, T., Shin, J., and S. Kapoor,
              "Concurrent multipath routing over bounded paths:
              Minimizing delay variance", Globecom 2013 , 2013.

   [PU12]     Popovic, M., Upadhyay, U., Le Boudec, J., Khalili, R., and
              N. Gast, "Mptcp is not pareto-optimal: performance issues
              and a possible solution", Proceedings of the 8th
              international conference on Emerging networking
              experiments and technologies , 2012.

   [RG10]     Barre, S., Greenhalgh, A., Wischik, D., Handley, M.,
              Raiciu, C., and C. Pluntke, "Data center networking with
              multipath tcp.", 9th ACM SIGCOMM , 2010.

   [GWH11]    Greenhalgh, A., Wischik, D., Handley, M., and C. Raiciu,
              "Design, implementation and evaluation of congestion
              control for multipath tcp.", 8th USENIX conference on
              Networked systems design and implementation , 2011.

   [RH10]     Raiciu, C., Handley, M., Barre, S., and O. Bonaventure,
              "Experimenting with multipath tcp.", Proceedings of the
              ACM SIGCOMM 2010 conference , 2010.

   [AP13]     Amer, P. and F. Pang, "Non-renegable selective
              acknowledgments (nr-sacks) for mptcp.", Proceedings of the
              2013 27th International Conference on Advanced Information
              Networking and Applications Workshops , 2013.

   [M05]      Internet Engineering Task Force, , "Coupled congestion
              control for multipath transport protocols", RFC 6356 ,
              2011.

   [M06]      Internet Engineering Task Force, , "Multipath tcp (mptcp)
              application interface considerations", RFC 6897 , 2013.

   [M07]      Internet Engineering Task Force, , "Tcp extensions for
              multipath operation with multiple addresses", RFC 6824 ,
              2013.





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   [M08]      Internet Engineering Task Force, , "Architectural
              guidelines for multipath tcp development", RFC 6182 ,
              2011.

5.2.  URIs

   [1] http:www.cs.iit.edu/~kapoor/papers/reducerate.pdf

Authors' Addresses

   Fabrizio Devetak
   Illinois Institute of Technology
   10W 31 Street
   Stuart Building
   Chicago, IL  60565
   US


   Sanjiv Kapoor
   Illinois Institute of Technology
   10W 31 Street
   Stuart Building
   Chicago, IL  60565
   US

   Phone: +1 312 567 5329
   Email: kapoor@iit.edu
   URI:   http:www.cs.iit.edu/~kapoor























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