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.
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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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