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ISDN SYSTEMS ELSEVIER Computer Networks and ISDN Systems 28 (1996) 627-633

Dimensioning network resources for IN services

James Yan * BNR, P.O. Box 3511, Station C, Ottawa, Canada KlY 4H7

Abstract

As Intelligent Network (IN) services are deployed globally, there is a need to ensure that IN network resources are cost-effectively dimensioned. Within the framework of a constrained optimization problem, this paper highlights and discusses the issues that must be considered in formulating the IN network dimensioning problem. The discussion will focus on those new aspects which distinguish the IN network dimensioning problem from the traditional dimensioning problem of circuit-switched telephone voice networks.

Keywords: Intelligent networks; Dimensioning; Network design; Network performance; Network performance modeling

1. Introduction

Globally, the public telephone networks are undergoing a major shift as they evolve towards the Intelligent Network (IN) and offer new services based on this new architecture. Telecom operators in Europe, North America and Japan have now implemented aspects of IN and are offering IN-based commercial services such as Freephone, virtual private networks (VPN) and calling card services [l-3].

Fig. 1 shows the physical architecture of IN. IN services can be accessed either directly at the Service Switching Point (SSP) or through an End Office. Service logic, essentially the combined software/hardware implementing the service, can be resident in either the Service Control Point (SCP) or the Adjunct (ADJ). The SSP communi-

* E-mail: [email protected].

cates with the SCP via the Signal Transfer Point (STP) signaling network using the SS7 signaling protocol.

The connection between the SSP and the ADJ is typically a direct high-speed communication link. The SCP is a centralized database well suited for services that involve a geographically dis- persed subscriber community and which need network-wide access, usage status and routing information. Because of its high-speed interface to the SSP, the ADJ may be more appropriate for providing quick responses to user actions as well as for service logic local to the SSP.

Both the Service Node (SN) and the Intelli- gent Peripheral (IP) are systems for providing and managing resources required for user interactions. These resources may be for: voice synthesis, announcements, voice recognition, and digit collection. Besides supporting user interactions, the SN can also realize some service logic. Typi- cally, the SN is used for specific services rather

0169-7552/96/$15.00 0 1996 Elsevier Science B.V. All rights reserved SSDZ 0169-7552(95)00069-O

628 J. Yan /Computer Network and ISDN Systems 28 (19%) 627433

than for the whole range of IN services offered by the SCP or the ADJ. Not shown in Fig. 1, but an important part of IN, is the Service Creation Environment (SCE), a modular programming fa- cility, and the reIated Service Management Sys- tem (SMS) used to create and deploy new IN services.

The IN dimensioning problem is essentially to determine how many, where, when, and how to deploy the SSPs, STPs, SCPs, SNs and IPs in the networks. Because IN is a new paradigm for delivering telecommunications services, the dimensioning problem associated with IN services needs to take into account the new aspects of how IN services will use network resources.

Why is it important to solve the IN network dimensioning problem? Network operators worldwide are aggressively introducing network services based on IN. Solving the dimensioning problem will determine how much of each type of network resources would be required. Such knowledge is useful in two ways: - Knowing the resource requirements early in

the service planning stage will influence the way services are designed and will be of signifi- cant use to service designers in making trade- offs on how to implement the services.

- The solution to the problem will reduce the cost of network deployment. Recognizing the importance of the IN network

dimensioning problem, EURESCOM now has an active project on IN dimensioning [4].

The purpose of this paper is to identify and discuss these aspects unique to IN services. To discuss the new requirements in formulating the IN network dimensioning problem, the framework of a constrained optimization problem is used to structure the discussion.

2. Problem definition

The goal of network dimensioning is to minimize, over a planning period, the costs of the network resources in a given network architecture. The network dimensioning problem can be defined as a constrained optimization problem. Specifically, for an expected traffic demand T on network resources, we wish to minimize the resource costs C over a set D of design options. Formally, the problem can be stated as:For expected traffic T, minimize Resource Costs Ouer design set D (1) such that

Network performance P 2 Target Pti,, (la) Resource utilization R I Capacity R,, . (lb)

In achieving the minimum costs, at least two sets of constraints must be satisfied. The first is

Fl SCP

SSP I Servlw Switching Polnt

SN = ServlwNode

IP - Intelligent Peripheral

ADJ = Adjunct STP = Signal Transfer Point

SCP I Service Control Point

Fig. 1. IN network architecture.

J. Yan /Computer Networks and ISDN Systems 28 (1996) 627-633 629

that the performance of the network P is at least as good as the target minimum performance level. The second is that the utilization of each network resource is within the resource’s capacity limit. Casting this loosely stated problem into specific terms will require doing the following: . Characterize the traffic T of the planned IN

services. . Establish the minimum performance targets

pmin- . Develop the network resource capacity models

for calculating the utilization R. * Formulate and model the network design alter-

natives. . Select the appropriate realistic cost functions

The unique IN aspects of each of these will be discussed in the subsequent sections.

3. Traffic characterization

Traffic characterization has to be sufficiently detailed to quantify the traffic demands on the various IN network resources as well as to de- scribe the community of interest among the users. The traffic characterization involves mapping the

forecast service demands into resource demands. The impact of user behavior has to be incorpo- rated. The biggest challenge under traffic characterization is the mapping of forecast service demands to resource demands. Typical examples of IN services are Freephone, virtual private network, personal communication services. To characterize the service demands, there is a need to have a service model. A service model gives the description of what the service features are, an understanding of the network architecture, how these features are provided in the network, how some of the features will generate demands on the network resources. The service model should include information such as: * how the network resources will be used, * the features of the service and their penetra-

tion, . the geographical coverage of the service, * the expected number of subscribers, * the growth rate, etc.

To illustrate the service model, consider the Personal Number service. This service is intended to use a single directory number to provide personal mobility of subscribers within the wireline network. The service will use the network as follows (see Fig. 21:

Fl SCP

Fig. 2. Simplified message flow in the Personal Number service.

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1. Any subscriber A calls the Personal Number service subscriber B whose home location is Switch 3.

2. The SSP serving subscriber B sends a query, via the STP signaling network, to the SCP to find out the current location of B.

3. The SCP replies that the current location is Switch 2.

4. The call is routed to Switch 2. Some features of the service are: (1) Follow

Me in which the call is routed to wherever the subscriber is, (2) Schedule in which the destina- tion is changed according to the time of the day and is predetermined, (3) Do Not Disturb in which the call is forwarded to the voice mail box of the subscriber. Feature penetration refers to the percentage of the service subscriber who will use each feature and the frequency of using them. The geographical coverage may be metropolitan in some cities only, national or international. Part of the service model is to project the initial expected number of subscribers and the growth pattern over the time period of interest.

Characterizing the service, however, is not sufficient for dimensioning. There is still the need to translate the service model into traffic parameters which are meaningful for dimensioning the network resources. Examples of these parameters are: * call attempt rate, * signaling messages, - SCP query rate, - non-call associated processing load, * community of interest (CO0 matrix.

The importance of these parameters depends on the resources being dimensioned. If, for in- stance, the goal is to determine the minimum number and location of SCPs, then the SCP query traffic and the CO1 matrix are of great interest.

IN-based services will introduce non-call associated traffic demands. These traffic streams will consume network resources but do not require connections set up. A good example of this type of traffic in the Personal Number service is the traffic generated by the subscriber to update the SCP of the subscriber’s new location, to change the times in the Schedule feature, or to turn on or off the Do Not Disturb feature. All these

interactions do not require end-to-end connection set up, but do use real-time processing resource.

4. Performance targets

The next issue to consider is the minimum performance targets which have to be satisfied. This issue has three parts: (1) What performance metrics should be used? (2) What are the end-to- end numerical objective for each metric? (3) How much of the end-to-end targets should be allocated to each network resource?

One of the key impact of IN services on teletraffic engineering [4] is that new service performance metrics need to be defined in order to capture the subscribers’ expectations from the service. For example, in IN-based services, during the time period between receiving the call re- quest message and providing the requested service, the subscriber may have to interact with the network a few times, or several network elements may have to interact with each other. In regular voice telephone calls, this time period is the post-dialing delay. For IN services, the signifi- cance of post-dialing delay as a measure of for gauging subscribers’ waiting time does not apply. A new metric such as Service Completion Time [51 may be more appropriate than the traditional post-dialing delay.

Performance metrics and their numerical objectives are usually set in industry standards fo- rums such as ITU-T. Currently, IN-related performance standards are being actively considered in ITU-T Study Group 2. As IN performance standards are still being formulated, network operators need to set interim performance objectives in order to proceed with deployment planning. In setting these objectives, user expectations need to be factored into these numerical objectives [6].

Establishing service end-to-end objectives is necessary but not sufficient for network dimensioning. There is still the need to allocate the end-to-end targets to the network elements of the architecture being considered. The end-to-end performance targets have to be translated into

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specific objectives which are then allocated to the individual resource. For example, in a Freephone call, the post-~dialing delay is a key metric. The total performance target is a mean of 3.5s. This target has to be allocated to the processing delay in the SSP, the transit delay in the SS7 network and the delay encountered due to query processing in the SCF’.

Another key issue in defining the minimum performance targets is the time period for which the targets apply. The issue is, of course, tightly coupled to the traffic characteristics of the services being considered. For services with traffic reasonably distributed over time, the traditional busy hour concept may still be used. However, for non-homogeneous traffic such as televoting, a much shorter time period may have to be used to capture the performance expected for the service.

5. Capacity and performance models

To ascertain that the performance and capacity constraints are met, the utilization and performance of the network resources (SSP, STP SS7 signaling network, SCP, Adjuncts, etc.) need to be modeled. For each resource type, the models need to represent the architecture of the product being considered. The models must capture the impact of the IN service demands on the product’s utilization and performance.

Generally speaking, one can expect that IN services will require more processing per call attempt, increase the STP SS7 network traffic, and make the SCP as the potential bottleneck as more services will require each IN call to launch queries to the SCP. To capture these expected behavior, the required models include: (1) SSP model relatin,g call traffic to delay and processor utilization, (2:) a model of the STP network link- ing call traffic to message load, network utilization and delay, and (3) SCP model relating call traffic to query traffic, SCP utilization and delay.

From a methodology perspective, the issue here is the level of details which need to be included in .the models. There is obviously a trade-off between accuracy and complexity. One

criterion important for resolving the trade-off is the type of decision which will rely on the results of solving the dimensioning problem. If, for example, the purpose is to know how many SCPs are required, the models of the SSP may not be that detailed. However, if the objective is to determine how to distribute the software of the service logic, then more detailed models have to be used.

6. Network design alternatives

The network design alternatives have to be identified and then modeled in order to quantify how the network resources will be used in each alternative. The design set can be defined at two levels. The first is at the strategy level. At this level, we wish to assess broad approaches such as . whether an overlay should be built or whether

the existing network should be evolved, + whether a centralized approach relying only

SCPs should be used or a hybrid solution in- volving SCPs and adjuncts should be adopted. Depending on the option, a different network

and cost model may be required. The second level focuses on implementation

optimization. This class of dimensioning problems involves the problems of optimal location, clustering, routing, capacity optimization-all adapted to the IN scenarios.

Modeling the network design alternative re- quires integrating the resource capacity models into a network view and a cost model for optimization. The models are used for network design and optimization (synthesis) and for end-to- end performance evaluation (analysis). Typically, analytical methods are used to design the network and estimate its performance. Simulation methods are used for more detailed performance analysis. The network model is one that links the various resource models together in order to estimate the total network performance. As in resource models, a key question is the degree of details. How much of the network dynamics has to be captured? How much analysis? How much simulation?

632 J. Yan /Computer Networks and ISDN Systems 28 (19%) 627-633

7. Cost models

As criteria for optimization, the cost functions have to reflect realistically yet not in too complex a manner the costs of the decisions. The cost functions have to reflect the implementation strategy or options being evaluated. Selecting the cost models involves making some choices. Are we looking at a single one-time decision or a sequence of related decisions over several time periods? Which resources are to be included? Are first costs sufficient or do we need to consider discounted costs? It is important that these choices are made such that they are consistent with the reasons for solving the dimensioning problem.

To illustrate the close relationship between cost models and implementation options, consider the example of a network operator facing two options: (I) a pure one-time SCP solution to satisfy the service demands forecast for the next 5 years, (II) a plan in which only SCP is used in the first two or three years for handling initial traffic and a hybrid SCP/adjunct solution for the subsequent years.

For (I), the cost function may be written as

Total cost = C,, + Cssp + Cstp (2)

where Cscp, Cssp, and C,,, are, respectively, the cost functions of the SCP, SSP and the STP network. The decision that has to be optimized is the number and the location of the SCPs and how the SSPs should be clustered around the SCPS.

For (II), the total cost (ignoring discounting) may be written as

Total cost

= jglr ‘SC* + ‘SSP + ‘Stp] j

+ 5 [Cscp+C.ssp+Cstp+Cadj]j (3) j=n+l

where n, the transition year, can be 2 or 3. The decisions which have to be optimized are: (a) the decision as in (I), (b) the selection of the transition year IZ, (c) the new trade-offs due to the use

of adjuncts. Clearly, (II) is a more complex problem in which (I) is embedded.

From the example, one can also observe that the nature of the decision (hence the cost model) will dictate the complexity of the optimization methods which can be used.

8. Summary

This paper has used the constrained optimization problem framework to identify and discuss areas requiring new or more detailed definitions in order to formulate better the IN network dimensioning problem. Solving this problem is par- ticularly important and useful in two phases of IN service development: in the front-end phase of service design and in the later phase of making deployment decisions. More work on each of the issues discussed is needed if the solutions to the dimensioning problem are to have early impact on the planning and deployment decisions of IN services and networks. Future effort should include new definitions of the problem variables, the development of new resource and network models, and the use of new optimization tech- niques. There is also merit to consider translating the problem definitions and solution algorithms into a flexible dimensioning software tool. Having such a tool would reduce the time and effort in solving the dimensioning problem.

Acknowledgements

The author would like to acknowledge the contributions of Sylvain Archambault, Jim Lam- ont and Doug MacDonald, all of BNR.

References

[l] E. Cancer et al., IN rollout in Europe, IEEE Comm. Mag. (March 1993) 38-47.

[2] S. Suzuki, IN rollout in Japan, IEEE Comm. Msg. (March 1993) 48-55.

[3] P. Russo et al., IN rollout in the United States, IEEE Comm. Mag. (March 1993) 56-63.

J. Yan /Computer Networks and ISDN Systems 28 (.l996) 627-633 633

[4] P.-D. Lansard, IN dimensioning: a key point for real implementation, IN ‘94 Workshop Proc., Heidelberg, Ger- many, 25-26 May 1994.

[5] J. Yan and D. MacDonald, Teletraffic performance in intelligent network services, in: The Fundamental Role of Teletraffic in the Evolution of Telecommunications Net- works, ITC-14, 1994, Vol. la, pp. 357-366.

[6] D. MacDonald and S. Archambault, ” Using customer expectations in planning the intelligent network, in: The Fundamental Role of Teletraffic in the Evolution of Telecommunic,ztions Networks, ITC-14, 1994, Vol. la, pp. 95-104.

James Yan received his B.A.Sc., M.A.Sc, and Ph.D. degrees in electri- cal engineering from the University of British Columbia (Vancouver, B.C., Canada). He joined BNR in 1976 as a member of scientific staff, working initially on private branch exchange performance and later on digital switching systems performance. From 1978-1988, he managed projects on switch performance, private network planning, network design tools devel-

opment, transport products, operations systems planning, Centrex evolution, and corporate networks evolutons planning. From 1988 to 1990, Dr. Yan participated in an exchange program with the Canadian Federal Government, where he was responsible for planning the evolution of the nationwide federal government telecommunications network. Since 1990, he has been the senior manager of BNR’s traffic performance department, which is responsible for the performance analysis of all key Northern Telecom products and new network architectures. Dr. Yan is a member of the Assosiation of Professional Engineers of Ontario.

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