Model-based admission control adjustment in data networks

Details Model-based admission control adjustment in data networks Model-based admission control adjustment in data networks

1999-09-28

2003-09-23

Chin, Wellington (Department: 2738)

Multiplex communications

Pathfinding or routing

Switching a message which includes an address header

C370S230000

Reexamination Certificate

active

06625155

ABSTRACT:

RELATED APPLICATIONS
The present invention is related to the following concurrently filed applications: Admission Control Adjustment in Data Networks Using Maximum Cell Count, by Z. Dziong and W. Lau; and Learning-Based Admission Control Adjustment in Data Networks, by Z. Dziong and M. Ji. Each of these concurrently filed applications is assigned to the assignee of the present invention, and each is hereby incorporated by reference into the present application.
1. Field of the Invention
The present invention relates to high-speed data networks, such as Asynchronous Transfer Mode (ATM) networks. More particularly, the present invention relates to Admission Control for bandwidth management and congestion control in such networks. Still more particularly, the present invention relates to the use of Connection Admission Control (CAC) adjustments in ATM networks using network measurement data to further control and tune an analytical CAC system.
2. Background of the Invention
In broadband integrated services networks, e.g., those using asynchronous transfer mode (ATM) systems and techniques, information is packetized in fixed length “cells” for statistical multiplexing with other traffic for transmission over high-bit-rate channels. Such networks are connection oriented, so a connection must be established before transmission begins. Moreover, these connections are usually subject to contracts between a network operator and users of the network. To ensure quality of service (QoS) consistent with these contracts, connection admission control (CAC) techniques are typically employed in management of such networks. Generally, CAC algorithms determine whether a new virtual channel connection should be admitted to the network based on network status—such as available resources, cell loss performance—and contract parameters (e.g., mean traffic rate and peak traffic rate). See generally, Dziong, Z.,
ATM Network Resource Management,
McGraw-Hill, 1997.
Because of the complex variety of connection types and services, and consequent difficulty in ascertaining complete and current information regarding the actual state of ATM networks, and because of possible adverse consequences of failing to honor QoS guarantees in customer contracts, many network operators have chosen to use CAC algorithms that are quite conservative. Most CAC algorithms are designed for worst-case source behavior. Moreover, analytical models applied in these algorithms are also conservative—to account for the difficulty in achieving exact modeling of the connection aggregate process. Such conservative approaches in many cases tend to offset statistical multiplexing gains and other system efficiencies available in ATM networks.
Some have proposed using actual network measurements (such as traffic level and cell-loss characteristics in light of corresponding QoS constraints) to adjust CAC mechanisms in an attempt to more fully use network resources. See, for example, Bensaou, B.; Lam, S. T. C.; Chu, H. and Tsang, D. H. K., “Estimation of the Cell Loss Ratio in ATM Networks with a Fuzzy System and Application to Measurement-Based Call Admission Control,”
IEEE/ACM Transactions on Networking,
VOL. 5, NO. 4 (August 1997), pp. 572-584; Gibbens, R. J., Kelly, F. P., and Key, P. B., “A decision-theoretic approach to call admission control in ATM networks,”
IEEE Journal on Selected Areas in Communication,
13(6):1101-1114 (1995); and Saito, H. “Dynamic call admission control in ATM networks,
IEEE Journal on selected Areas in Communication,
9(7):982-989 (1991).
Thus far however, attempts to use network operating measurements have proven difficult in network administration, especially in respect of their incorporation in CAC processes. A particular difficulty arises in some prior art CAC processes in efficiently treating operations in networks exhibiting a wide variety of traffic types with a concomitant variety of QoS constraints. High bandwidth efficiencies through CAC tuning have not been readily available without high precision measurements.
SUMMARY OF THE INVENTION
The present invention overcomes limitations of prior art CAC algorithms and achieves a technical advance, as described in connection with illustrative embodiments presented below.
In accordance with one aspect of the present invention, the concept of aggregate effective bandwidth, AEBW, is used to provide a useful approximation to required bandwidth for given levels and classes of network traffic. AEBW is used in deriving an allowed level of overbooking—expressed in terms of an overbooking gain, &agr;
t
.
In accordance with another aspect of the present invention, a model-based system and method for CAC model tuning estimates selected parameters of the aggregate cell process for the current state of connections using measurements made at individual ATM switches. These estimates are then used to evaluate currently allowed over-booking gain, which is used in the next CAC decision.
In an illustrative embodiment, estimates of mean and variance of the aggregate cell rate process, M
k
, V
k
for the current state k are used to estimate allowed overbooking gain, given by
α
k
t
=
f
AEBW
⁡
(
M
k
d
,
V
k
d
)
f
AEBW
⁡
(
M
^
k
,
V
^
k
)
-
1
,
(
1
)
where M
k
d
, V
k
d
and {circumflex over (M)}
k
, {circumflex over (V)}
k
are declared and estimated values for connection state k, respectively. The function ƒ
AEBW
is an analytical model for effective bandwidth evaluation.
In estimating aggregate cell rate mean and variance, M, V it proves useful to employ cell count samples over 100 ms (or a shorter time interval, where convenient). Then well-known Kalman filtering techniques are advantageously applied to optimally estimate M and V based on measurements and declarations. Because the Kalman filter also assesses error in the estimate, it proves convenient to design safety margin in calculation of the over-booking gain.
It also proves useful in some cases to include longer-term statistics in the mean and variance calculation and to modulate the aggressiveness of the overbooking based on actual system performance measures (e.g. cell losses). In some cases, a learning model can be employed to further or alternatively modulate this aggressiveness.


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