Multiplex communications – Channel assignment techniques – Polling
Reexamination Certificate
1999-04-26
2003-02-11
Kizou, Hassan (Department: 2662)
Multiplex communications
Channel assignment techniques
Polling
C370S232000, C370S235000, C370S395720, C370S429000, 72
Reexamination Certificate
active
06519264
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to communications networks and more particularly, to a method and apparatus for monitoring the rate of one or more connections of message, cell or frame traffic in such networks.
BACKGROUND OF THE INVENTION
In many instances, it is necessary or desirable to measure the rate of transmission of a message stream at a specific measurement point in a communications network. Rates may be measured on the aggregate of several connections or at the level of individual connections. These rate measurements may in turn be used to determine the activity state of a connection, for instance for bandwidth allocation or feedback control purposes.
In the Asynchronous Transfer Mode (ATM) networking protocol, multiple classes of communications service have been standardized. The ATM Forum Traffic Management Working Group has defined five service categories for cell transmission which are distinguished by parameter sets used to describe source behaviour and quality of service (QoS) guarantees. The service categories are identified as constant bit rate (CBR), real-time variable bit rate (rtVBR), non-real-time variable bit rate (nrtVBR), available bit rate (ABR) and unspecified bit rate (UBR). Traffic management techniques such as flow control are used in communications networks to protect a network from congestion and to achieve network performance or utilization objectives. Flow control in the ABR service category is achieved by arranging for each source node of a network to send special resource management (RM) cells through the network. Each network entity or element, for instance a switch or a node in the network, may indicate its congestion status by writing into the RM cell. The RM cell is then forwarded on to the next network element in the data path. At the destination network element, the RM cell is turned back towards the source. The network entities in the backward data path may mark congestion information into the RM cell, which is ultimately received by the source. The source may then adjust its sending rate in response to the information contained in the received RM cell.
Various mechanisms can be used in order to achieve flow control in a network. These mechanisms can be classified broadly depending on the congestion monitoring criteria used and the feedback mechanism employed. The feedback mechanisms are either binary in nature or may calculate an explicit rate of transmission. In binary flow control methods, a source of message transmission may receive feedback information to either increase its rate of transmission or decrease its rate of transmission depending on the instantaneous traffic flows at each contention point in the network. In explicit rate (ER) feedback schemes, an actual explicit rate of transmission for the connection will be determined by the network element or node and this information will be sent to the source of message transmission. This explicit rate of transmission is calculated at periodic intervals which may not necessarily be equal to one cell time. Examples of ER mechanisms known to those skilled in this art are the Enhanced Proportional Rate Control Algorithm (EPRCA) and two congestion avoidance schemes, namely Explicit Rate Indication for Congestion Avoidance (ERICA) and Congestion Avoidance Using Proportional Control (CAPC). While it is generally thought that binary feedback schemes may sometimes suffer from unfairness problems depending on the network topology and on the source and destination behaviour employed, the objective of ER algorithms is to allocate bandwidth at each link in the network so that the link is fully utilized and a fair distribution of bandwidth between connections is achieved.
Typically, basic rate measurements in communications networks are conducted as follows. Upon the arrival of a cell at a measurement point the current time is recorded, for instance by means of a time stamp. The time stamp associated with the arrival of the last preceding cell is then subtracted from the current time. This measure of inter-cell timing is extremely noisy, and is very sensitive to cell delay variation induced by buffering and cell scheduling throughout the network. As explained below, it is usual that some type of signal processing must be performed on the raw timing measurements in order to arrive at an estimate of the transmission rate for a given connection.
Known network architectures will generally apply some form of traffic shaping at the source of message transmission so as to enforce a constant cell inter-departure time. However, as a result of the effect of various queuing delays encountered along the message path, random variations to the intended inter-departure times will likely be introduced. These random variations may be modelled as fixed times to which a noise quantity or jitter has been added. Thus, if m is the intended constant cell inter-departure time and X is the noise quantity associated with a particular rate measurement, the calculation of a per-connection rate thus consists of estimating m from a number of observations or samples of the cell inter-arrival times, as follows:
T
(
1
)=
m+X
(
1
),
T
(
2
)=
m+X
(
2
), (1)
where T is a particular measurement of a cell inter-arrival time.
In the available bit rate (ABR) service category of ATM networking, the mean rate of transmission from a source can be expected to change as a result of dynamic bandwidth allocation and feedback control. Therefore, a given source of message transmission may initially transmit at a mean rate associated with an inter-departure time m, but may be instructed during message transmission to transmit at a higher rate associated with a cell inter-departure time w. In this scenario, the inter-arrival times become:
T
(
1
)=
m+X
(
1
),
T
(
2
)=
m+X
(
2
), . . . ,
T
(
n
)=
m+X
(
n
),
T
(
n
+1)=
w+X
(
n+
1),
T
(
n
+2)=
w+X
(n+2), (2)
where between the n
th
and (n+1)
th
cells a change in rate has occurred. One of the problems encountered in rate measurements is that of formulating a method of measurement which can detect such transitions in the mean rate of transmission.
Heretofore, three principal rate measurement methods which are based upon corresponding filtering techniques have been utilized in the monitoring of transmission rates in communications networks. Generally, the rate R associated with a network connection is defined as the inverse of the inter-arrival time between two cells over a connection, provided the connection produces such cells without any jitter. As mentioned previously, all the connections in an ATM network can experience some jitter when traversing through a series of network nodes. Thus, rate measurement methods have to employ some type of averaging technique to smoothen this jitter. One can employ a low pass filter for such averaging. The three known methods of rate measurement which use a low pass filter can be broadly classified as rate filtering (RF) methods, time-interval filtering (TIF) methods and modified rate filtering (MRF) methods.
In typical rate filtering methods, a low pass filter is used to filter the inverse of cell inter-arrival times. The connection rate according to these methods is determined as follows:
Rate
(
k
)
=
(
1
-
α
)
·
Rate
(
k
-
1
)
+
α
·
(
1
Δ
t
)
(
3
)
where:
Rate(k) is the rate of the connection being monitored at the k
th
cell arrival point;
&Dgr;t is the inter-arrival time between the k
th
and k−1
th
cells; and
&agr; is the filter gain factor.
One technique of rate filtering is the finite impulse response (FIR) filter. In this technique, a weighted average is maintained over a constant number of most recent timing sample points. In general terms, the FIR filter as applied to input rate monitoring would assume the following form:
R
n
=&bgr;·R
n−1
+(1−&bgr;)/&Dgr;t (4)
where:
R
n
is the
Carr David W.
Lee Denny L. S.
Sterne Jason T.
Alcatel Canada Inc.
Blake Cassels & Graydon LLP
Kizou Hassan
Macchione Alfred A.
Odland David
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