USING MRAN FOR CALL ADMISSION CONTROL OF ATM NETWORKS IN OPNET Mohit Aiyar, N. Sundararajan and P. Saratchandran S1-B4b-06, School of Electrical and Electronic Engineering Nanyang Technological University, Singapore, 639798. Email:
[email protected]
ABSTRACT
integral part of this challenge and is closely related to other aspects of networks such as service models, scheduling disciplines, traffic characterization and QoS specification. Call Admission Control (CAC) has been accepted as a potential solution for supporting diverse, heterogeneous traffic sources demanding different quality of services in Asynchronous Transfer Mode (ATM) networks.
This paper explores the use of OPNET to introduce a neural network Call Admission Control scheme in ATM networks to improve traffic management. The advanced capability of modeling traffic sources in OPNET was employed to depict a realistic ATM network model. The conventional CAC algorithm was explored under various traffic scenarios built using profiles based on varied traffic statistical distributions. The architectural hierarchy in OPNET was then exploited to introduce MRAN - Minimal Resource Allocation Network - a minimal RBF neural network. MRAN was used to define a dynamic bandwidth allocation approach to the CAC algorithm. The performance of the network was scrutinized under the same traffic conditions. In conclusion, collaborating statistics such as Utilization and Call Blocking Ratio from a set of varied traffic simulations indicate that the MRAN CAC algorithm results in superior network performance and traffic management when compared to the conventional schemes.
OPNET v9.0 provides tools that can be utilized to depict a realistic ATM network model fed with fine tuned traffic sources generating statistically varied data inputs. Scenarios developed on the basis of appropriate traffic modeling can be used to test various OPNET in-built and customized CAC algorithms. OPNET’s hierarchal design architecture makes use of process models and external files to isolate and modularize the CAC function so that it can be customized for further research. In conventional CAC, when a user requests a new VPC or VCC, the user must specify (implicitly or explicitly) the traffic characteristics in both directions for that connection. The decision to restrict a call is based on the availability of network resources sufficient to deliver the call level and cell level QoS. This decision requires information about the connection’s traffic characteristics. To provide QoS guarantees while efficiently utilizing network resources, ATM networks need sufficient information about incoming traffic. The CAC along each node along the end to end path of the connection will use this information to determine the traffic model and decide whether to accept or reject the request for setup. If the CAC at every node along the path decides that there are enough resources to provide QoS guarantees, the request is accepted; otherwise it is rejected. The ATM node accepts the new call only if the QoS of the already
1. INTRODUCTION
Call Admission Control (CAC) is the first line of defense for the network in protecting itself from excessive loads. CAC determines whether a call request for a Virtual Path (VP) connection is accepted or not. The objective of CAC is to restrict the access of new calls so that the cell and call level QoS is maintained within the path network. An additional objective is to maximize network revenue over time. In today’s world of broad band multiplexed varied ISDN traffic, the provision of Quality of Service (QoS) guarantees is an important and challenging issue in the design of ATM networks. CAC is an
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of results under both convention and MRAN enhanced approaches and the resulting conclusion.
connected calls is expected to satisfy their required values even after the new call is connected. Conventional CAC schemes that utilize either capacity estimation or buffer thresholds suffer from some fundamental limitations such as difficulty in obtaining complete statistics on input traffic to a network. As a result, it is not easy to accurately determine the equivalent capacity or effective thresholds or multimedia high-speed networks in various bursty traffic flow conditions. Furthermore, these schemes operate are designed for steady state conditions. A control scheme that dynamically regulates traffic flow according to changing network conditions, however, requires understanding of network dynamics Neural networks, with their property to efficiently adapt and predict, stand out as a very viable and suitable alternative. In [1], the authors have presented a neural network based CAC mechanism for ATM networks with heterogeneous arrivals. Recently, use of artificial neural networks (ANNs) in traffic management of ATM networks is gaining momentum [2-3]. The self-learning capability of the neural network is used to characterize the relationship between input traffic and system performance. Neural network adaptability is used to learn the relation between offered traffic and service quality from observed data. Traffic control using neural networks can adapt to changes in traffic characteristics and to the addition of new communication services. This has been aptly highlighted in [4] – [6].
2. NETWORK DEFINITIONS AND MEASUREMENTS
Figure 1 depicts the network model that was built to represent realistic B-ISDN traffic sources and conventional and MRAN CAC in action. The model was built using OPNET Modeler v9.0.A PL2 (Build 1665).
Figure 1 – Network Model Topology Definition The network model was designed to allow for a central backbone network comprising of three atm8_crosscon_adv switches (Central_Sw1-3). These switches are in turn fed by three other switches (Local_Switch1-3), which are connected to multiple clients or servers. The clients and servers were modeled after the atm_uni_server/client node model. The atm8_crossconn_adv was chosen to represent an ATM switch because it has advanced attributes to perform VP/VC switching. It is capable of switching VCCs among eight VP links. The design considerations inherently permit expansion of the network model to allow for multiple switches to be included in or connecting to the backbone network. The clients or servers connecting to the
This research initiative employs the use of MRAN, a recently developed minimal RBF neural network [7] for an enhanced CAC approach based on dynamic bandwidth allocation. The paper will elucidate upon the use of OPNET in deployment of the network model topology and scenario definition. The paper aims to provide guidance to end users on how to use the OPNET features to define the model and research ATM CAC – leading to alteration of the incumbent conventional algorithm to introduce a neural network based dynamic bandwidth allocation scheme. Further sections will deal with the collation
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scenarios for CAC investigation. The results illustrated in this paper have used the exponential statistical distribution during the Application definition of Interarrival Time (FTP) and Send/Receive arrival time (Email) and related parameters.
peripheral switches can be multiplied to expand on the traffic inflicted on the network. Application and Profile Configuration Traffic incident on the network model was defined using Opnet’s facility to configure Profiles and Applications. OPNET provides global objects for defining profiles and applications. These global objects are portable entities that can be used anywhere within the context of the overall topology. Profiles describe the activity patterns of specific users connected to the network whereas applications that are comprised of usage parameters make up the profile. EMAIL, FTP and VOICE were defined as three Applications in sync with the exact same Profiles – such that there is a one-to-one relationship between the Profile and an Application. The Applications were configured to depict realistic values for Commend Mix, Inter Request Time, File size (FTP), Send/Receive Inter Arrival Time, Send/Receive group size, Email bytes (EMAIL) and Silence/talk spurt length and encode scheme (VOICE). The profiles were defined with realistic and challenging values for Operation Mode, Start Time, Duration and Repeatability. Again, while defining Applications and Profiles, abundant scope was left for expansion of the definition boundaries to generate varied scenarios.
APPLICATION SIM NO.
FTP IRT
EMAIL SR IAT
EMAIL SRGS
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EMAIL RPT
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Statistical distribution = Exponential Characteristics = Varied Simulation Range = 1.1-1.10
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Statistical distribution = Bernoulli Characteristics = Varied Simulation Range = 2.1-2.10
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Table 1 – Results Collection Matrix
Title Sim No. FTP IRT Email SR IAT
Future work includes building complex profiles based on combinations of applications and repeatability patterns. This was not thought necessary for the present investigation.
Email SRGS Voice STSL FTP RPT Email RPT Voice RPT
4. RESULTS COLLECTION MATRIX
An extended version of Table 1 illustrated below was used for results consolidation. The results collection matrix enables documentation of the changes in the statistical distribution at an application and profile configuration level.
Description Simulation Number FTP Inter Request Time Email Send Receive Inter arrival time Email Send Receive Group Size Voice Silence Talk Spurt Length FTP Repeatability Email Repeatability Voice Repeatability
Table 2 – Column legend Each row in the Results Collection Matrix corresponds to one simulation executed. For each simulation, the following statistics were measured
The time and statistical distribution of the incident traffic was defined in the Profile and App configuration. Different statistical distributions for incoming traffic were used as the basis for varied
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ATM Call Blocking Ratio (%) Email Traffic Sent / Received (bytes/sec)
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Voice Traffic Sent / Received (bytes/sec) Email Stn / Local Sw 1 Throughput (bytes/sec)
• Less than or equal to the maximum allocated bandwidth for the CBR category • The sum of SCRs for the VBR category • The sum of MCRs for the ABR category
Simulations (1.1) – (1.10) were executed under incident traffic modeled after varied characteristics of the Exponential statistical distribution. Simulations (2.1) – (2.10) were executed under incident traffic modeled after varied characteristics of the Bernoulli statistical distribution. The set of simulations were repeated under the Conventional and MRAN CAC scheme.
No call admission is applied for the UBR category. All calls belonging to the UBR category are admitted irrespective of their traffic contracts. Call Admission Control is based on the user specified traffic contract – defined in the traffic parameters of the individual nodes. The conformance definition is based on the GCRA algorithm and the traffic contract for the call, which specifies the PCR and the Cell Delay Variation (CDV) tolerance in the case of CBR and UBR traffic.
The results depicted in Section (5) and (6) are from a standard simulation from the Exponential category. The results collection methodology can be extrapolated to include other statistical distributions and combinations therein. Currently, for the simulations executed, work is in progress to calculate a statistical average per distribution, which will allow for performance comparison at ground level
Various scenarios were implemented ranging from light traffic to very heavy traffic scenarios. The results presented hereunder are for a sample heavy traffic scenario.
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The conventional Call Admission Control scheme in OPNET is located in the external file “ams_atm_support_v3.ex.c”. OPNET follows a very structured hierarchical modeling methodology, and the location of the existing conventional CAC algorithm was traced through this layered structure, starting from the network layer to the node layer onwards to the process model and finally to the external file. Traffic control, which includes Call Admission Control (CAC) and Usage Parameter Control (UPC), prevents any calls with unsupportable traffic requirements from establishing a connection and established calls from degrading network service below specified Quality of Service specifications.
ATM.Call Blocking Ratio (%).none
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5. CALL ADMISSION CONTROL – WITHOUT MRAN
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Figure 2 – Call Blocking Ratio – Conventional Scheme Analysis of the data from Figure 2-4 indicate that under heavy traffic conditions, the Call Blocking Ratio is very high resulting in poor throughput and passage of data. This is critical in the context of BISDN scenarios – where data integrity and continuity are essential.
It was determined that the CAC algorithm defined in OPNET guarantees that the sum of the PCRs is:
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Figure 4 – Voice traffic sent/received – Conventional Scheme
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Figure 3 – Email traffic sent/received – Conventional Scheme
Figure 5 – Email Stn – Local Sw1 – Throughput
Voice Traffic Sent (bytes/sec)
The MRAN is a recently developed minimal Radial Basis Function (RBF) Neural Network, which combines the growth criterion of RAN with a pruning strategy to realize a minimum RAN. In the classical approach to radial basis functions (RBF) network implementation, the basis functions are usually chosen as Gaussian and the number of hidden units (i.e., the centers and widths of the Gaussian functions) is fixed a priori based on the properties of the input data. The weights connecting the hidden and output units are estimated by a linear least squares method. The MRAN learning algorithm succeeds the Resource Allocation Network (RAN) and the Resource Allocation Network via Extended Kalman filter (RANEKF) research initiatives. The MRAN overcomes the
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4. MRAN ALGORITHM 40000
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Within ams_atm_support_v3.ex.c, CAC is implemented in the function ams_atm_vp_support_traffic(). The MRAN neural network is used as a feed forward network that employs a sliding window over the input sequence. Since the MRAN is a minimal RBF that incorporates a growth and pruning strategy, it does not need to go through an offline training session. Data rates that are dynamically fed to it train the network. Simultaneously, the concurrent running of the growth criterion and the pruning strategy results in a minimal topology – effectively used for instantaneous data rate prediction. The bandwidth allocation algorithm can be briefly summarized as follows: 1. Note the bandwidth allocation based on traffic class and traffic contract parameters PCR, MCR and SCR. 2. Note current data rate as x(t-1) and x(t) and measure actual bandwidth requirement. 3. Predict bandwidth requirement x(T+t) at time T + t using MRAN 4. Use predicted bandwidth instead of static PCR, MCR and SCR values for bandwidth allocation in function ams_atm_vp_support_traffic ().
problems stated above by incorporating the growth criteria of RAN networks and concurrently running a neuron pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting topology leads to a minimal topology for RAN. The pruning strategy used prunes hidden nodes that do not contribute significantly to the output of the network, or that are too close to each other. This is done by observing the output of each of the hidden nodes for a period of time, and then removing the node that has not been contributing a significant output for that period. In the proposed scheme, the network builds up the hidden neurons from the input data. It does this in an efficient manner to realize a compact RBF network with better approximation accuracy. Furthermore, the proposed MRAN adaptive control scheme provides for adjustments of the centers, widths and also the weights which result in better approximation for the input–output nonlinear functions. 6. CALL ADMISSION CONTROL – WITH MRAN
MRAN was introduced as an isolated function in the external file “ams_atm_support_v3.ex.c”. The variables in the MRAN function were suitably modified in order to incorporate and successfully compile the modified external file The modified external file was stored in the local op_models directory.. This directory was given precedence in the Preferences/Mod_Dir setup. This ensured that the changes in the revised external file would be referenced during simulation execution.
Thus, the function MRAN uses instantaneous data rate as input to predict the data rate for the next incidence. This prediction is used as a substitute for the traffic contract parameters. In this manner, MRAN influences the allocation of bandwidth used during CAC determination thus making it dynamic.The CAC approach implemented as mentioned above is significantly different from the conventional scheme existing in OPNET inasmuch as static values defined in the traffic contract are discarded. Using MRAN, an attempt is made at using predictive values that closely approximate the bandwidth in the face of varied traffic classes modeled on a host of different statistical distributions. Simulation results indicate a sharp decrease in the Call Blocking Ratio under heavy traffic conditions and much better throughput and traffic sent/received ratios.
Upon analysis, it was concluded that the objective of defining an MRAN based CAC system did not necessitate the creation of new models. Suitably altering the external file to include the new MRAN function and call the aforementioned function in the critical path of CAC implementation was thought to be effective in introducing the desired change. As mentioned previously, pre-defined static values for PCR, MCR, SCR and MBS are used to implement conventional CAC. This approach does not take into account the dynamic nature of the incident traffic.
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Figure 9 – Email Stn – Local Sw1 – Throughput – MRAN CAC
Figure 7 – Email traffic sent/received – MRAN CAC
Please note that the results collected above are from a single simulation for a specific traffic distribution. Work is currently in progress to collate results under varied traffic distributions and plotting
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7. CONCLUSION
compare/contrast graphs for the same. For the traffic distribution above, Figure 10 plots a comparison between Call Blocking Ration under the conventional scheme and the MRAN approach.
The results conclusively indicate that under MRAN based Call Admission Control is superior in performance to conventional CAC schemes. The conventional controller also proved to be not as robust as the MRAN scheme under statistically varied traffic scenarios. While the results of this undertaking will be conclusively collated, the future of this project lies in extrapolating the hypothesis to wireless ATM networks. Wireless and ATM are in their infancy. Research has been ongoing to develop a protocol that will run on wireless medium without much delay or error. ATM type wireless networks will play an important role in broadband communication networks of the future. In light of this, the importance of effective traffic management cannot be underestimated. The impact of using MRAN in improving Call Admission Control on wireless ATM networks would need to be further explored.
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Figure 10 – Call Blocking Ratio - Comparison Figure 11 plots a comparison between Email traffic received under MRAN and conventional schemes for the same base lined traffic sent.
8. ACKNOWLEDGEMENTS
The author, Mohit Aiyar, is a full time employee at Citibank N.A, International Card Center, Asia Technology Office, Singapore. This research initiative has been conducted as part-time focused activity at the Nanyang Technological University, Singapore. The ICC-ATO have been very encouraging and have generously lent their support to this research initiative. The author would like to extend a vote of thanks to Citibank N.A for their backing and support.
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9. REFERENCES
Figure 11 – Email Traffic Received – Comparison 1. J M Hah, P L Tien, M C Yuang, "Neural Network Based Call Admission Control in ATM Networks with Heterogeneous Arrival” IEEE 21st Conferece on Local Computer Networks (LCN ’96) 2. Raffaele Bolla, Franco Davoli, Thomas Parisini, and Piergiulio Maryni, "An Adaptive Neural Network Admission Controller for Dynamic Bandwidth Allocation", IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol. 28, No. 4, pp. 592-600, August, 1998.
As can be seen above, there is a distinct reduction in the Call Blocking Ratio and a marked increase in the traffic received statistic under under the MRAN CAC scheme.
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3. Ibrahim Habib, "Applications of Neurocomputing in traffic management of ATM networks", Proceedings of the IEEE, Vol. 84, No. 10, pp. 1430-1441, October, 1996. 4. Cheng, R G, and Chang, C J, "Neural network connection admission control for ATM networks", IEE Proc.-Commun. Vol. 144, No. 2, pp. 93-98, April, 1997. 5. Omatu, S, Khalid, M, and Yusof, R, Neuro-Control and its applications, Springer, London,1996. 6. Kay-Guang Cheng, Chung-Ju Chang, and Li-Fong Lin, "A QoS Provisioning Neural Fuzzy Connection Admission Controller for Multimedia High-Speed Networks", IEEE/ACM Transactions on networking 7. Yingwei, L, Sundararajan, N, and Saratchandran, P, “Performance Evaluation of a sequential minimal radial Basis Function (RBF) Neural Network Learning Algorithm", IEEE transactions on neural network, Vol. 9, No. 2, pp. 308-318, March, 1998.
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