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Full text of "USPTO Patents Application 09750903"

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1 25991 D Kirkby 


This invention relates to methods and apparatus for controlling traffic flow in a 
communications network so as to reduce congestion. 


Increasing volumes of communications traffic are now being carried on packet 
networks, and in particular on Internet Protocol (IP) networks. Such networks 
comprise on any nodes or routers interconnected by links so as to define a mesh. 
A recent introduction has been the concept of network having an optical core in 
which traffic is carried on switched optical fibre paths between routers. The core is 
accessed by an edge network Typically in the design of high capacity IP networks, 
routers are classified as either core routers or edge routers. Edge routers carry out 
all the network ingress and egress functions in particular controlling the incoming 
traffic streams across the network. Core routers act as transit routers forwarding 
network traffic from one network node to another. 

In such a network, the user data is assembled into packets and each provided with 
a header identifying the destination of the packet and optionally, including routing 
information. The header may further contain information relating to the router of 
the packet contents and identifying a priority class for the packet. For example, 
packets containing high quality of service real time traffic, and is voice, will be 
accorded the highest priority, while packets containing 'best efforts' data may be 
accorded a low priority. 

A particular problem that has been experienced with certain types of traffic, 
particularly data traffic and real-time video traffic, in its inherently bursty nature. 
Further, this burstiness occurs on a timescale that is shorter than feasible network 
control loop timescales, and thus can lead to congestion when traffic is heavy. 

When congestion occurs, ordinary data traffic which is not critically time sensitive 
can be briefly buffered in the routers which are experiencing congestion. Urgent 
data traffic and real time interactive services such as voice and video cannot be 

In order to maximise the overall network utilisation, it is desirable to perform 
statistical multiplexing of traffic traversing the network while providing a prior 
allocation of resources and protection particularly for the delay sensitive traffic . 
Existing control and feedback mechanisms Such as described in patents... are 
however inadequate to respond to this bursty traffic at a sufficiently rapid rateto 
provide this resource allocation and protection. In the conventional approach to this 
problem, the high speed statistical variations in traffic flow are simply allowed for by 
setting large margins in the setting of control levels for determining feedback price. 
Proposals for 'pricing' ingress flows at the edge of the network for admission 
control purposes have involved for instance measuring the 'effective bandwidth' of 
the flow. (Ref) Effective bandwidth is a measure of the bandwidth that needs to be 
reserved to give a desired packet loss ofr delay rate on a statistically varying flow. 
Unfortunately effective bandwidths do not add linearly on aggregation so are 
difficult to use in a congestion price feedback control scheme. 


An object of the invention is to minimize or to overcome these disadvantages. 

According to a first aspect of the invention there is provided a method of controlling 
traffic flow in a communications packet network, the method comprising 
determining for flows within the network a mean utilisation requirement and a 
measure of a variance from that mean, and determining from said mean and 
variance and bandwidth pricing so as to control the admission of said flows to the 

Advantageously, control limits are set by the network operator for mean and 
variance, and the pricing is increased as one or both of these limits is approached 
so as to provide feedback control of admission to the network. 


The control method may be embodied as software in machine readable form on a 
storage medium. 

According to another aspect of the invention, there is provided a method of 
controlling admission of traffic flows to a communications network, the method 
comprising sampling the traffic flows each at an ingress, and sampling an 
aggregate flow of said flows at some or all of the resources used by the aggregate 
5 flow, determining from said sampling a mean bandwidth requirement for each traffic 

flow and a measure of the variance from that mean, determining from said mean 
and variance measurements first and second prices for the mean and variance 
components of the controlled traffic flows that are admitted to the network, and 
determining from said first and second prices an admission cost for each said flow 
10 so as to regulate the admission of that flow. 

According to a further aspect of the invention, there is provided an admission 
control arrangement for a communications network, the arrangement comprising 
sampling means for sampling a traffic flow, means for determining from said 
15 sampling means a measure of mean bandwidth requirement and of a variance from 

that mean, and price computation means for determining from said mean and 
variance a cost or price for bandwidth case so as to provide ingress price control 
for admission of the traffic flow to the network. 

The admission control arrangement may be incorporated in a network manager, 
e.g. in software form. 

In our arrangement and method, overall resource utilisation is optimised by the use 
of congestion price based feedback that separates out the components of price 
that affect the mean traffic flow from the standard deviation of the flow. It is well 
known that on aggregation, standard deviations add according to a square route 
law and means add linearly. In a mixed traffic flow type network, a network flow 
optimisation process that separates these components can provide improved 
overall resource utilisation. A further advantage of this arrangement and method is 
that that it enables a fairer allocation of 'network -price' to any given ingress flow. 
Rather than simply allocating the network charges on the basis of mean ingress 
flow, the charges are based on a measure of mean and standard deviation. This 


discourages users from abusing the network by sending excessively bursty traffic, 
unless it is genuinely important to the user It should be noted that the terms prices 
and charges . as employed herein refer to an internal network currency used for 
admission control purposes and is generally separate from any real billing. 


Embodiments of the invention will now be described with reference to the 

accompanying drawings in which:- 

Figure 1 is a selective design of an exemplary network; 

Figure 2 illustrates the aggregation of traffic flows at a node in the network of 
5 Figure 1 ; 

Figure 2a shows the construction of an ingress controller; 

Figure 3a and 3b illustrate idealised and practical bandwidth demands for a traffic 

10 Figure 4 illustrates the variance of the traffic flow of Figure 3; 

Figure 5a illustrates the measured mean and standard deviation of a traffic 
aggregate; and 

Figure 5b illustrates the primary flow control level and its division into separate 
allocations for variance and mean.. 



Referring first to Figure 1 , this shows an exemplary network in schematic form. As 
shown in Figure 1 , the network comprises a number of nodes 1 1 interconnected by 
links 12. As shown in Figure 1, the network comprises a core region 13 accessed 

20 via an edge region 14. The links 12 are usually optical fibre, particularly in the core 
region. Advantageously the network of Figure 1 is an Internet Protocol (IP) or 
MPLS (multi protocol label switched) network in which traffic is transported in 
packet form. An objective of the resource control system is to control the ingress 
of IP and MPLS traffic in such a way that different traffic classes are treated 

25 optimally. In particular, delay sensitive classes of traffic must see minimal 
congestion inside any router or on entering any of the packet buffers at he 
entrance to each optical link. 


Referring now to Figure 2, this shows in schematic form a an edge node 1 1 and a 
core network node 11a. At the edge node 1 1, a number of input traffic flows are 
aggregated on to a traffic path on link 12. At the core node 11a two transit traffic 
flows are shown merging. Prior to entering the packet buffer 17 at the entrance to 
5 transmission link 12, the traffic is sampled by the aggregate sampling circuit 21 . At 

the entrance to the next link on the traffic path 12a a similar measurement 
arrangement exists 17a,21a. The traffic flows are sampled by sampling circuits 21 
and 22 to determine both the mean bandwidth usage x,- and the standard deviation 
cti from that mean for each aggregated flow, across the network. The mean and 

1 0 standard deviation measurement are processed by a network admission controller 

to determine a pair of prices for using that particular resource. This price pair 
defines separate prices for the mean component of traffic flow and the deviation (or 
variance) component. A separate ingress controller 23 (Figure 2a) in the edge 
router samples 24 and measures the mean and deviation of individual edge to 

15 edge flows entering the network. The ingress controller also continuously monitors 

the sum of the resource price pairs for the edge to edge path it is using, (note there 
is one ingress controller per edge to edge path, the explicit edge to edge path 
being defined for instance by MPLS labels attached to each packet The user (or a 
software object using pre-agreed ingress control rules) can then either accept this 

20 price or modify this mean bandwidth or standard deviation bandwidth requirements 

to obtain his optimum quality of service vs price. To modify the ingress traffic flow 
the ingress controller could use the traffic shaper 25 or for example signal back to 
the original traffic source (not shown). The traffic shaper controls scheduler 26. 
This price feedback mechanism provides a self-regulating mechanism on the 

25 bandwidth demands imposed on the network. 

The ingress control 23 controls traffic on every end to end path through the 
network. The paths may be MPLS paths. 

Referring now to Figures 3a and 3b, these illustrate respectively idealised and 
practical bandwidth requirements for a traffic flow. Figure 3a shows a slowly 
30 varying mean bandwidth demand, while figure 3b shows a typical rapid short term 

variation superimposed on the mean. 

The short term variations, which represent the deviations of the traffic flow from the 
mean, are illustrated in Figure 4. These can be statistically analysed in real time to 


give a measure of mean and standard deviation. This analysis also gives the 
variance which is the standard deviation squared. A variety of algorithms could be 
used for this purpose. The exponentially weighted time averaged mean is one of 
the simplest, whilst the sampled data can and the mean together can be used to 
5 give the time averaged variance. The rest of this description makes the assumption 

that the sampling periods and rate and time averaging parameters used for ingress 
control purposes are sufficiently similar to those used by the aggregate flow meters 
that insignificant errors are caused. Typically the time averaging time constant is 
greater than the feedback time constants of the DRC control system. Typically 

10 100ms to 10 seconds. The variance sampling period is typically sufficiently short 

that the shortest bursts you are interested in controlling are captured. This implies 
a sampling time period does not need to be much shorter than the specified worst 
case permitted delay variation for the traffic being controlled. 1-1 0ms might be 
appropriate for typical delay sensitive traffic. (Longer sampling periods could be 

15 tolerated if margins were increased. A measure of the significance of these rapid 

short term variations is given by the standard deviation (a) or by the corresponding 
variance a 2 . 

In the arrangement of Figure 2a in which a number flows are aggregated on a 
common path. Assuming that traffic deviations are uncorrelated in time, the 
20 standard deviation a A of the aggregate flow is given by the expression 

The aggregated mean traffic of course adds linearly so the aggregated mean flow 
is given by the expression 

25 Referring now to Figure 5a, this illustrates a bandwidth plan from which pricing 

information is determined to provide feedback control for admission to the network. 
In figure 5a, the network operator sets a peak bandwidth maximum or control level 
Xc which, in the ordinary cause of events should not be exceeded, even 
momentarily by the peak bursts of the aggregated traffic. A typical flow has a 

30 mean bandwidth Xa well below this maximum level. Further, to minimise the risk of 


congestion, this mean x A should be at least one and preferably *k' standard 
deviations below this control level Xc. To ensure that the probability of momentary 
congestion is sufficiently small for all practical purposes k should typically lie in the 
range 3 to 6. 

5 In a preferred embodiment separate price calculations are performed from the 

mean x A and standard deviation cr A demands on the network as follows 

The network operator subdivides the control level bandwidth into two sub 
allocations, xcwithe allocation for the mean flow, and Xcothe nominal allocation for 
the components of flow that deviate from the mean. These two allocations are 
10 related to the control level by the equation Xc = Xcm + Xcd ,as illustrated in Figure 

5b. . We determine the mean pricing P x as a function of the mean demand and 
the control level for mean traffic. This price will typically be quoted by the resource 
in terms of a price per unit bandwidth. 

i.e. P x = F (xcm , Xa) 

15 In a discrete system, in which price is updated at regular intervals , At, an example 

function might be. 

Px (t+1) = |Px (t) +pA t (Xcm - Xa)| + 

This equation indicates that the new price at the end of the current time interval 
equals the current price plus a gain factor times the time interval and the difference 

20 between the control level for mean traffic and the current mean flow. The outer 

brackets with subscript + sign signifies that the solution is constrained to be 
positive. The parameter pis the feedback gain. This is an example of an 
integrating control feedback system, as the error signal is integrated over time to 
produce change in price. Other variants include differential and proportional 

25 feedback. 

Similarly, we define the variance pricing P v in terms of the the measured standard 
deviation a A and the control level xcd, This price will typically have units of price per 
unit time per unit bandwidth variance, i.e.. 

Pv = F (xcd , o A ) 


In a discrete system an example function might be 


Pv (t+1) = |Pv (t) +oA t (^co -k 2 ^^ 

This equation indicates that the new variance price at the end of the current time 
interval equals the current price plus a gain factor times the time interval and the 
difference between the square of control level for deviation and the square of k 
5 times the measured aggregate variance. The outer brackets with subscript + sign 

signifies that the solution is constrained to be positive. The parameter a is the 
feedback gain. This is an example of an integrating control feedback system, as 
the error signal is integrated over time to produce change in price. Other variants 
include differential and proportional feedback. 


In both these examples the price increases rapidly as the argument of the inner 
bracket (the error signal) goes positive. 

A more sophisticated pricing may be obtained by introducing a second order term 
which takes into account the first and/or second differentials of the traffic flow 
15 means and deviations. . In that case the pricing functions become 

P m = F((x CM ,x A ) I ^-JandPv^,,^, ^JLl) 

dt d t at at 

The price that is fed back to the user is in the form of the two separate prices P m 
and P v - These prices can be quoted in terms of a price per unit bandwidth and a 
price per unit variance. The total 'price' charged to the user is then determined by 

20 measurements on the traffic leaving the user of the mean and variance of his 

traffic. These are multiplied by the prices per unit and added together to give the 
total price to the userT. This enables the network (and optionally) the user to see 
a much fairer estimate of the network 'cost' to transport that particular flow. This 
information can then be used for admission control purposes, for instance for 

25 smoothing the flow to reduce the variance component of the flow or reducing or 

even shutting off the mean flow if the total price T is becoming excessive. It can 
also be used for admission control of inelastic traffic flows where a user would ask 
for admission of a guaranteed flow with certain quoted mean and variance 
characteristics. Once admitted, the ingress controller of such inelastic traffic flows 


is committed to allow the ingress of traffic within the pre agreed limits, irrespective 
of how high the internal congestion price subsequently goes. 

The technique enables finely differentiated services to be provided by control of 
ingress flows at the network edge. Some users may, for instance want a class of 
5 service which highly values the transmission of bursty traffic without delay. These 

users would have a service level agreement that assigned a high WtP (willingness , 
to pay) to the variance component of the traffic. Thus even in high congestion the 
bursts from such users would be allowed into the network whilst other users with a 
low WtP for variance would have their traffic bursts smoothed and hence 
1 0 momentarily delayed. 

A modification of the schemes described above is a system in which the sub- 
allocations of bandwidth for mean and standard deviation control levels x,„ and Xcm 
are not defined by management fiat, but are estimated dynamically from the 
measurements of mean and deviation parameters of the incoming aggregate flows. 
15 The management system still defines the bandwidth peak maximum control level 

xc. The simplest way to determine the standard deviation allocation is simply to 

XcD = Xc — Xa 

That is to say the allocation for standard deviation is simply taken to be the 
20 difference between the current mean traffic level and the peak maximum control 

level. This has the advantage that as the mean level rises towards the peak 
bandwidth control level, the variance price rises in an inverse square manner, 
drastically curtailing new traffic bursts. When the network has only low mean 
traffic, the variance price is extremely low and bursty traffic is hardly discouraged at 
25 all. 

In this case it would be natural to define the control level for the mean flow as 

Xcm = Xc — k.c*A 

That is to say the mean control level is defined as the peak bandwidth control level 
minus K times the currently measured standard deviation. In practice caution may 
30 suggest that other constraints such as a minimum bandwidth margin between the 

peak control level and the mean control level are added. 


For optimum resource usage in a future system in which the ingress controllers 
actively adjusted the shaping of the mean and deviation components of their traffic 
in response to mean and deviation prices and the class of traffic being transmitted, 
then maximum user utility would be obtained when proportional fairness is applied 
5 to the allocation split between mean and deviation traffic. The resource would thus 

adjust the ratio of xcm to Xco to be the same as the current revenue from mean 
traffic and the current revenue the variance component of the traffic. These 
adjustments would have to be carried out slowly in comparison to the DRC 
feedback control time constant or instability couid result. 


The price for traffic traversing a series of resources along a path is optimally found 
by adding the prices at each resource linearly. Thus, the prices per unit bandwidth 
for the mean flows for traffic flowing along a particular path are found by adding the 
prices per unit bandwidth of the means in each resource that the path utilizes. We 
15 have found that, for optimum results, the price per unit variance should be 

determined by adding the price per unit variance of each resource that the path 
utilizes, rather than adding the price per unit standard deviation. 

In a further modification, particularly bursty traffic can incur a further price penalty 
20 by the user of an exponential weighting on the standard deviation measurement. 

This exponential weighting function can be used to weight large positive deviations 
of traffic flow from the mean more strongly than small deviations. This would be 
used as a second order correction to discourage users from transmitting large 
bursts of traffic unexpectedly at very high rates. Note that the use of variance 
25 rather than simple deviation from a mean already penalizes large sudden 

deviations with a square law dependency. 


It will be understood that the above description of a preferred embodiment is given 
by way of example only and that various modifications may be made by those 
skilled in the art without departing from the spirit and scope of the invention.