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TelecommunicationsNetworks

Optimization in

This paper explains the role that optimization plays in the economics of today’stelecommunications networks, as well as in the industry’s future. ILOG optimizationenables companies to create agile systems for managing resources and remainingcompetitive under heavy market pressure. Specifically in telecom, we present anoverview of network areas in which ILOG has been directly involved: traffic engineering,adaptive network configuration, circuit design and strategic network planning. Weexplain how the ILOG Optimization Suite fits these applications and delivers significantreturn on investment (ROI) when applied to planning and managing telecommunicationsnetworks.

Technical White Paper

Optimization inTelecommunications

NetworksWhite Paper

© ILOG, October 2002 – Do not duplicate without permission.ILOG, CPLEX and their respective logotypes are registered trademarks.

All other company and product names are trademarks or registered trademarks of their respective holders.The material presented in this document is summary in nature, subject to change,

not contractual and intended for general information only and does not constitute a representation.

Table Of Contents

Executive Summary......................................................................................................................... 3

New Business Objectives for Operators ..................................................................................... 3 The Engineering and the Economics of Telecommunications Networks.................................... 3

Traffic Engineering, Circuit Design and Adaptive Configuration ..................................................... 5

Main Characteristics of Networks................................................................................................ 5 Congestion Management in IP Networks.................................................................................... 5 Load Balancing in Signaling Networks........................................................................................ 7 Maximizing Resource Utilization in Networks with Resource Reservation ................................. 9

Network Planning........................................................................................................................... 13

Planning the Access.................................................................................................................. 13 Planning the Backbone ............................................................................................................. 14

The ILOG Optimization Suite......................................................................................................... 16

Core Engines............................................................................................................................. 16 Vertical Engine Extensions ....................................................................................................... 17 Modeling Tool............................................................................................................................ 18 Performance.............................................................................................................................. 18 Matrix Fit.................................................................................................................................... 19

Conclusion ..................................................................................................................................... 20

Needs for Optimization in Telecommunications........................................................................ 20 ILOG Value Proposition ............................................................................................................ 20

References and Further Reading .................................................................................................. 22

ILOG Technical White Paper 2

Executive Summary New Business Objectives for Operators Until recently, we were still in a period of high growth for bandwidth demand. The stock market was estimating the value of telecommunications operators by focusing on future profit expectations, giving primary importance to the size of their infrastructures and their capacity to accommodate more demand from new customers. At the time, the stock market was anticipating ever-increasing growth for demand based on a positive technology-market feedback loop. New network technologies offered more bandwidth and brought new possibilities for increasingly demanding services. The introduction of these new services raised the expectations of their users, who would soon get used to more bandwidth-intensive high-quality services and, consequently, demand even more bandwidth. At that time, the stock market was giving more importance to the capacity of operators to be able to quickly deploy networks and gain new customers. The operating costs for deploying network infrastructure were secondary. While many operators invested huge amounts of capital in network infrastructures, customer demand, however, followed its own course, falling far short of industry expectations. Similar feedback loops have sustained the growth of other industry sectors for decades. In the computer market, hardware manufacturers, software publishers and end users are driving a feedback loop that helps to explain the consistent growth in the power of computing technologies. The technology-market feedback loop for CPU’s can be summarized as follows:

1. More powerful hardware leads to

2. More powerful software, and in turn

3. Greater expectations from users The situation for telecommunications operators has changed. The telecommunications market has come to realize that anticipating too much future demand while spending enormous amounts of money on infrastructure is a big risk and for many a mistake. Everything points to the “pump” of the technology-market positive feedback loop as going suddenly dry. As a consequence, the stock market now values the operators on the basis of their capacity to generate immediate profits. The operators are under pressure to generate more revenue while decreasing their operating costs. This is where the industry is today and will remain in the immediate future, until growth rates for demand increase significantly, which will take time. In the middle and long terms, however, one can expect the technology-market feedback loop to pump again, bringing high growth rates for demand back to the industry. The Engineering and the Economics of Telecommunications Networks Even though only 30% of the backbone capacity of any communications service provider is typically used, one should not conclude too quickly that the capacity has been overestimated. When engineering a telephone network, for instance, a telecom company needs to base the dimensioning of capacity on peak-hour traffic and ensure that the blocking probability is maintained below an acceptable level. The immediate consequence of this engineering requirement is that a large part of the available capacity goes unused during off-peak hours.


There is clearly a tradeoff between engineering and quality of service (QoS) requirements on one hand and the business objective of maximizing resource utilization on the other. By allowing operators to “do more with less,” i.e., satisfy more customer demand with existing capacity, optimization is an important tool for improving the profit margins of service providers. Traffic engineering and circuit design are two especially important areas in which optimization applications can produce significant business benefits.

Investment in network infrastructure is not the only factor that optimization can help. It can also help in reducing the maintenance costs of the network, in particular those for network management. Adaptive network configuration is an application that integrates optimization with performance and fault management tools, in order to bring new capabilities to the network management platforms. In an open shortest path first (OSPF) IP network, an adaptive network configuration system adjusts the network to changing traffic patterns and network failures by optimally balancing the traffic load. It serves as a powerful solution to congestion management problems. Automating this process represents an opportunity to significantly reduce network management costs. Although operators will try to reduce their investments in infrastructure, this does not mean that they will stop investing in network capacity. The key objective will be to expand networks at the right pace to meet actual demand. Good network planning tools are essential to reach that goal and also special care should be paid to obtaining better demand forecasts. Network planning is about satisfying traffic requirements while minimizing operational costs and respecting engineering constraints. For instance, an engineering constraint can be the maximum number of hops in routing a point-to-point traffic demand or the need to take into account possible failures during planning (concepts of reliability and survivability). The health of the telecommunications market depends on the performance of the industry’s supply chain. Optimization is a tool that operators can use to better adjust their level of network infrastructures (capital assets) to the expected level of service demand. In the short term, this means reducing maintenance costs and investments in infrastructure. The faster the telecommunications industry gets back on track and growing, the better. However, this strategy to overcome the current crisis is not the sole objective of optimization. More generally, optimization helps telecommunications better and more quickly adapt supply to demand. Most equipment vendors have understood that, far from being a threat, optimization is an opportunity to reactivate the supply chain. This explains why equipment vendors are showing a renewed interest in optimization technologies and software tools. When recovering from the crisis and returning to higher growth rates, optimization will be crucial to ensuring that the telecommunications supply chain keeps pumping at the right rate. Thus, the telecommunications market has reached the point where optimization is more needed than ever.


Traffic Engineering, Circuit Design and Adaptive Configuration

Main Characteristics of Networks Like networks for transportation and other utilities, telecommunications networks experience a performance tradeoff between QoS and quantity of service, or throughput. Depending on the network technologies and the protocols used, this tradeoff has important consequences on the way networks are engineered and made more profitable. The following table provides a simplified view of the different types of network switching technologies and their main characteristics in terms of resource utilization. We will see the consequences this has on the possible strategies for better managing network resources.

Switching Resource Management

Resource Reservation

Related Issues

Telephony Circuit switching

Reservation Circuit Blocking probability

IP Packet switching

No reservation

All resources shared

Flow split

Packet reordering

None No QoS guarantee

Congestion

Delays

ATM Cell switching

Reservation Virtual Paths

Virtual Circuits

VP/VC design

MPLS Label switching

Reservation Label Switched Paths

LSP design

Congestion Management in IP Networks In the performance tradeoff between QoS and throughput, the best-effort data networks are designed to provide the most throughput without any type of QoS guarantee. High levels of throughput can be reached because no resources are reserved for specific services. Instead, all the resources are shared among the traffic demands. Additionally, the sent messages are split into packets and can be sent over several distinct paths, thus balancing the traffic over numerous network links. This does not prevent congestion on IP networks. Congestion occurs when too much traffic converges on part of the network that cannot provide enough capacity to carry the traffic. The immediate consequence for the users of the network is a slowdown in data communication. OSPF is one of the most common interior gateway protocols (IGPs) for the Internet. In OSPF IP networks, the traffic routing is determined by link weights. One weight is associated with each link in the network, and the weights are used to compute the shortest path from one point to another. Manufacturers of routers usually recommend setting these weights to a value inversely proportional to the link capacity: in other words, the larger a pipe, the less expensive it is, which means it is more likely to be used by demands since it will appear in more computed shortest paths. The limitation of this approach is that the weights are usually kept


static, and they do not take into account the actual traffic patterns on the network at any given moment of the day. Metric-Based Traffic Control To manage congestion, adjusting the OSPF weight metric can perform some basic traffic control. By manually increasing the weight of a congested link, a network operator can make the link more “expensive” to use, and the effect will be that traffic usually concentrated on this link will be rerouted to alternative paths. This might solve some congestion situations, but it does not constitute a robust solution. Adjusting the OSPF weight of a congested link is a very local strategy. It can solve a congestion problem on one link, but as a consequence it can move the congestion to another place in the network. Even worse, if this occurs, the operator will have no way of predicting where the congestion will reappear. Static OSPF Weights A second problem with using static OSPF weights is that it does not take into account the traffic patterns of a network at any given moment of the day. The consequence is that traffic will always tend to concentrate on some links while leaving other links unused. There is a clear need to match OSPF weights to traffic patterns throughout the day, dynamically adapting them as changes in traffic patterns are identified. The weights will be considered optimally sent when they balance the traffic load over the available network resources.

Fig.1: Cyclic Process to Dynamically Recompute Optimal OSPF Weights

Adaptive Network Configuration This OSPF traffic engineering technique is implemented by INFOSIM in IP Optimizer (IPO). IPO dynamically recomputes weights to redirect traffic, using data on traffic patterns. The product implements the process shown in Figure 1. A complete cycle through the process


typically takes a half-hour. During the cycle, the traffic patterns are determined using a performance management tool. The traffic patterns are then received as input to an OSPF weight computation module based on ILOG CPLEX. The result is a set of OSPF weights that optimally balances the load among the available network resources. These weights are used to automatically reconfigure the routers. Technical Benefits There are several technical benefits to such an automated adaptive configuration system. First, the resource utilization is improved through better load balancing of the traffic. Second, the system significantly reduces congestion problems and allows automatic reconfiguration of the OSPF weights in case of a network failure. Business Benefit On the business side, the benefit of an automated adaptive configuration system is a significant reduction of the network management costs. Load Balancing in Signaling Networks Signaling Transfer Points in SS7 Networks The signaling network is the nervous system of a telecommunications network. It is vital to the operation of the telecommunications network. A breakdown in the signaling network typically causes customer service failures, which result in lost revenue and unsatisfied customers. The objective for the operator is to prevent local overloads and node-link failures from disrupting signaling. Optimal Load Balancing Optimal load balancing is the solution proposed by ATESIO with the optimization models they developed using ILOG CPLEX for mobile operators. The optimization models aim at obtaining optimally load-balanced reconfiguration of the signaling transfer points in an SS7 network. Figures 2 and 3, respectively, show a typical traffic load before and after optimization. The results produced by ILOG CPLEX cope with failures and changes in traffic patterns. In GSM mobile networks, for instance, the need to manage sudden signaling traffic loads has become critical due to the high growth rate of short message services. Fast Computation The models developed by ATESIO are large-integer programs containing several thousand integer variables and constraints. Despite this complexity, computational results show that the ILOG CPLEX mixed-integer optimizer is able to produce optimal solutions in a matter of minutes.

Directly Operational Solutions The objective of the first developed model was to minimize the load differences in a set of internal components, called common channel distributors (CCDs), in the signaling transfer points. It soon became clear that implementing such a solution would be complex. Typically, an optimal solution with regards to load balance can involve more than a hundred changes that have to be implemented manually by a network engineer.

A second requirement to take into account is to limit in some way the number of changes needed to be manually implemented so that solutions are directly operational. This is achieved by reformulating the optimization model. The resulting model minimizes the number of changes while maintaining the maximum load difference below a given percentage.


Computations have since shown that an 85 percent improvement in traffic balance can be achieved with only 5 percent of the initial changes.

Fig.2: Typical traffic load over CCDs before optimization

Technical Benefits Resource utilization is improved with better traffic load balancing. Also, the local overloads and node-link failures are less likely to disrupt signaling. The maintenance activities of the network engineer are also made less complex.

Fig.3: Optimal Load Balancing After Optimization with ILOG CPLEX MIP


Business Benefit On the business side, the benefit is that the probability of unsatisfied customers and a loss of revenue due to customer service failures are largely minimized while the maintenance costs are reduced. Toward Optimized and Automated Network Reconfiguration Currently, the changes calculated by ILOG CPLEX are implemented by a network engineer who manually reconfigures the equipment. Here again, we would see a huge improvement in traffic management if the equipment supported automated reconfiguration. Just as in the case of the automated reconfiguration of OSPF weights, there is potential for bringing new capabilities to the next generation of network management platforms by using optimized and automated network reconfiguration. This represents an opportunity to further reduce the maintenance costs of signaling networks. Maximizing Resource Utilization in Networks with Resource Reservation Traffic Engineering (TE) Network carriers must ensure the reliability of the IP core and cost-effectively keep up with growing traffic volume. It has been shown how this can be implemented using optimization in best-effort IP OSPF networks. In addition, carriers must also prove their ability to integrate best-effort traffic with QoS demands for real-time applications that need a predictable and protected service for such data as voice, video on demand and videoconferencing. This requires an intelligent and optimized mapping of customer traffic flow onto a physical topology over a geographic area, an operation known as traffic engineering. Online Traffic Engineering Online traffic engineering tools have to assign resources to service demands in real time, which means they usually make suboptimal decisions based on fast heuristics. These decisions are about how to route the service demands (typically a path in the case of a point-to-point demand or a tree in the case of a point-to-multipoint, or multicast, demand) while respecting QoS constraints. Online computation typically uses a distributed shortest path algorithm. In MPLS networks, such technology is known as constraint-based routing (CBR). Constraints on additive QoS parameters, like delay or jitter (variation of the delay), can be modeled as linear constraints on the used paths. Also, as service demands are served one at a time, the assignment decisions take into account a single demand and do not maximize network utilization with regards to a global set of demands. Offline Traffic Engineering An offline traffic engineering tool can improve the utilization of network resources by optimally balancing the traffic load over the existing network. Combining online and offline traffic engineering is the best approach for obtaining fast resource assignment on one hand and maximizing the utilization of the network resources on the other. The optimization problems to be solved by an offline traffic engineering tool are known as multicommodity flow problems. More specifically, powerful algorithms are needed to efficiently solve the integer multicommodity flow problem with linear side constraints on the flows. Such traffic engineering tools have been applied successfully in various types of networks:

• Asynchronous Transfer Mode (ATM) networks for the virtual path/virtual circuit (VP/VC) design

• Multi-Protocol Label Switching (MPLS) networks for the labeled switch path (LSP) design

• Frame relay (FR) networks for the permanent virtual circuit (PVC) design


Maximizing the Utilization of Network Resources Optimal load balancing of services can be obtained while respecting the QoS constraints of service demands. This example is from a presentation given by Celso C. Ribeiro. Figure 4 shows the available network resources, i.e., a set of switching nodes and a set of links.

Figures 5 to 7 show the successive routing of three service demands. This corresponds to the typical assignment decisions taken by an online traffic engineering tool based on shortest path computations.

Figures 8 and 9 show how an online optimization tool would be unable to correctly assign resources for a fourth service demand. Figure 8 shows that the available capacity of one of the links has become insufficient to route the incoming demand. Figure 9 shows a possible route for the incoming demand that does not satisfy a QoS constraint, for instance, if routes are required to cross less than four hops. This could be a way to express a maximum delay constraint.

Figure 10 shows the link where the load needs to be better balanced so that the yellow service demand can be routed. Finally, Figure 11 shows the solution proposed by an offline traffic engineering tool after reoptimization of the global set of demands. All service demands can be routed while respecting the QoS constraints on the services. This is achieved by optimally load balancing the services over the available network resources.

Fig.4: The available network resources

Fig.5: Resource assignments for the first service demand


Fig.6: Resource assignments for the first two service demands

Fig.7: Resource assignments for the first three service demands

Fig.8: Demand for a fourth service (in yellow) cannot use saturated

link

Fig.9: Possible path for the fourth service demand (in yellow) does

not respect QoS constraints because the path is too long


Fig.10: Need to reroute the global set of service demands

Fig.11: Solution that optimally balances the load while respecting

QoS constraints

Technical Benefits The main technical benefits of an optimized offline traffic engineering tool are that it maximizes the utilization of the network resources and allows them to serve more service demands with the existing network while respecting the QoS requirements of the respective demands. Business Benefit In terms of business, this represents an opportunity to significantly increase the profitability of QoS services by serving more customer demands with the existing network resources. This also means that the ROI of the network infrastructure will also be improved.


Network Planning Network planning is a key tool for improved investment in network infrastructure. The purpose of network planning is to determine which resources should be installed in order to satisfy traffic requirements while minimizing operational costs and respecting engineering constraints. When looking at the economics of networks, there are opportunities in network infrastructure for large economies of scale. Careful planning can make these opportunities become reality. With respect to network access, these economies can be obtained by carefully deciding where to locate equipment and how to cluster the demands. In backbone optical networks, large savings are possible through the cost structure of fiber-link capacities. Figure 12 presents the phases in a typical network planning process. Equipment location is the first step and consists of deciding where to locate equipment and how to cluster and connect the customers. The second step involves determining the topology and the capacities to be installed in the backbone network. Finally, the switching elements need to be configured so that they can support the traffic requirements. All three steps --equipment location, backbone planning and equipment configuration -- pose challenging optimization problems. The power and robustness of ILOG CPLEX’s optimizers offer key advantages for providing quality solutions to these problems.

Fig.12: Phases of a typical network planning process

Planning the Access Equipment location involves locating equipment in a multilevel network, and connecting and clustering the customers. This problem occurs in various contexts, for instance, the location of GSM equipment or routers in IP networks. We illustrate the problem using a case involving a GSM network. Locating Equipment in a GSM Network In a GSM network, the problem concerns two-level location, following the architecture of GSM networks. The BTS are the “client” nodes that have to be clustered and connected to the network via a base station controller (BSC). All the BSCs in the network have to be connected to mobile switching centers (MSCs). The dotted lines in Figure 14 show possible connections of BTSs to BSCs, and those between BSCs and MSCs. Apart from connection decisions, other decisions govern whether a node location will actually be opened. In Figure 13, this decision about whether to open a location is represented by dotted circles. Additionally, we see that different types of technologies could be used for BSC nodes. (This is represented in the figure by triangles of different colors.) Depending on the chosen technology, the maximum capacity that can be supported by a BSC might be different, thus allowing the number of BTSs to vary in each cluster.


Fig. 13: A 2-Level Facility Location Problem

This problem, also known as the concentrator problem, can be modeled using mixed integer programming, and solved using the ILOG CPLEX MIP optimizer. The objective is to minimize the operational cost, i.e., the sum of all the costs: costs of installing a new MSC or a new MSC plus the cost of connecting nodes together and the cost to upgrade to a newer technology. The business benefits of the application are the reduction of operational costs and the capacity to efficiently plan the evolution of the access network. Planning the Backbone The Cost Structure of Optical Fiber Links The cost structure of optical fiber links represents an opportunity for large economies of scale. The reason is that one OC-48 optical fiber link is much cheaper than 48 OC-1 optical fiber links. Dealing with Network Failures during Planning Modern optical fiber-based networks can transport much higher volumes of traffic than traditional copper-based networks. However, there is a tradeoff between concentrating a lot of traffic on some backbone links and the need to make the network survivable. Survivability is the ability of a network to perform according to a specification after it has been damaged. While in copper-based networks, failures have an impact on a limited number of services, in modern optical networks the impact of a single link or node failure can disrupt a huge number of services and greatly inconvenience customers. It is therefore vital to take into account possible failures during planning. Dealing with Demand Uncertainty In order to expand a network at the right pace and in the right direction, optimization models for strategic planning usually take into account multiple periods with budget constraints. Also,


various “what-if” scenarios can be considered in order to provide decision support in the case of an uncertain demand forecast. Optimization Technologies Altogether, linear programming, mixed integer programming and local search are essential optimization technologies for efficiently solving the backbone planning problem. ATESIO, for instance, built a planning tool for backbones based on ILOG CPLEX. Benefits The principal business benefit of backbone planning is the ability to reduce network infrastructure costs while satisfying traffic requirements and respecting vital engineering constraints, like survivability.


The ILOG Optimization Suite The ILOG Optimization Suite is applied to long-term planning, scheduling, routing and configuration problems, as well as to other resource allocation problems. The ILOG optimization product line – ILOG CPLEX, ILOG Solver, ILOG JSolver, ILOG Scheduler, ILOG Dispatcher, ILOG Configurator, ILOG JConfigurator and ILOG OPL Studio – can be fully integrated and allows the management of resource optimization problems from long-term capacity planning to short-term scheduling. ILOG optimization software provides core engines for applying constraint programming and mathematical programming to a wide variety of problems. Additionally, the ILOG Optimization Suite enables the user to apply optimization through the use of vertical engine extensions for constraint-based scheduling, technician dispatching, vehicle routing, and product and service configuration. Furthermore, the ILOG Optimization Suite product line provides a complete modeling environment to support both constraint programming and mathematical programming applications, enabling the user to develop and deploy high-level optimization applications without a detailed knowledge of computer programming. Core Engines ILOG CPLEX

ILOG CPLEX provides powerful C and C++ libraries of fundamental algorithms for operations research and mathematical programming professionals. These libraries include ILOG’s simplex, barrier and mixed integer optimizers for linear, integer and quadratic programming. ILOG CPLEX also provides easy-to-use C++ modeling objects that allow the expression of linear and integer programs in a simplified form directly related to their algebraic models. ILOG CPLEX can be used as a standalone library or with ILOG Solver for powerful hybrid optimization. Since it is callable from C, C++, Java, Fortran, VB and other computer languages, ILOG CPLEX is easily accessible to programmers in a wide range of programming environments. Additionally, solvers are available in parallel form on specific platforms for delivering improved performance in solving difficult problems. ILOG Solver Along with ILOG CPLEX, the ILOG Solver engine forms the foundation of the ILOG Optimization Suite. ILOG Solver is one of the core C++ libraries in the ILOG Optimization Suite and implements the basic engine for constraint-based optimization. It can solve highly combinatorial real-world problems that are impractical to solve with traditional mathematical programming methods. This high-performance constraint-programming engine can be used alone, with ILOG CPLEX or as the foundation for one of its vertical engine extensions. ILOG Solver provides all the functions needed to model and solve a very wide range of resource allocation and decision problems. A parallel version of ILOG Solver is also available on specific platforms. ILOG JSolver ILOG JSolver brings ILOG's leading constraint programming technology to the Java platform. Simply write high-level constraints that describe the desired solution and let the search framework efficiently find a solution. With ILOG JSolver, you can build optimization applications that take advantage of the rich architecture of the Java platform. ILOG Concert Technology ILOG Solver and ILOG CPLEX are supplied with ILOG Concert Technology, a set of C++ modeling objects that can be used to represent a wide range of optimization problems. This


easy-to-use, common framework for representing optimization problems reduces development time and greatly facilitates the comparison and integration of constraint programming and mathematical programming technologies. Concert Technology serves as the foundation for the C++ object-oriented approach to solving optimization problems.

Fig. 14: The ILOG Optimization Suite Vertical Engines

Vertical Engine Extensions The vertical engine extensions of ILOG Solver allow developers to take advantage of the power of constraint programming with extensions for specific problem domains. These extensions allow complete access to the full power of ILOG Solver, with additional facilities provided for modeling problems specific to the domains and addressing them with specialized algorithms. ILOG Scheduler ILOG Scheduler contains specialized modeling and algorithmic enhancements for scheduling applications, including constraint-based scheduling algorithms for handling the time scheduling of resource-constrained activities. ILOG Scheduler is specifically adapted to short-term scheduling applications and is widely used in production, workforce and maintenance scheduling. ILOG Dispatcher ILOG Dispatcher is designed for building vehicle routing and personnel dispatching applications, including the assignment of technicians to customer locations for after-sale service. Dispatcher assists in routing vehicle fleets or dispatching technicians, taking into account various business constraints such as customer’s logistical requirements, visit time windows, fleet capacity, driver timetables, and work-hour regulations. ILOG Configurator


ILOG Configurator is a C++ library that provides faster, easier development of high-performance configuration engines and applications. In the context of e-commerce and CRM, ILOG Configurator’s innovative technology can better guide buyers through the selling process, and propose the products and services that best satisfy the customers’ needs. Coupled with CAD/CAM, ILOG Configurator can also be helpful in designing complex products such as computers and automobiles. ILOG JConfigurator ILOG JConfigurator provides specialized modeling and algorithmic enhancements for building configuration engines and applications on the Java platform. It configures solutions to problems according to business rules, wishes and preferences expressed by a user either through a Business Object Model (BOM) and Business Configuration Language (BCL) or its Java application programming interface (API). Its interactive graphical editor, ILOG Builder, enables even nonprogrammers to state business rules directly in a nearly natural language adapted to the problem. Web-enabled by means of Web Connector, ILOG JConfigurator supports transactional publishing and editing services to implement interactive configuration services. Using Enterprise Java Bean (EJB) wrappers, these services can be deployed as stateful or stateless configuration servers. ILOG JConfigurator also supports the Web services protocol (SOAP), enabling users to take advantage of this emerging market. Modeling Tool ILOG OPL Studio ILOG OPL Studio is a complete interactive modeling environment for the rapid development of optimization models and efficient deployment of optimization applications. The powerful and elegant Optimization Programming Language (OPL) expands the audience for the ILOG Optimization Suite to include nonprogrammers. With the OPL language, problems can be represented intuitively and concisely, thus simplifying the user’s ability to learn and operate the suite. From the OPL language, users have access to the powerful algorithms of ILOG CPLEX, ILOG Solver and ILOG Scheduler. After developing a model with OPL, the OPL Component Libraries can be used to integrate the model into an application developed in C++, Visual Basic or Java. Performance Algorithmic innovations pioneered by ILOG in the past 10 years have improved solution times by more than a factor of 350. Together with advancements in computer hardware, this dramatic improvement in software and algorithms means that problems considered unapproachable only a few years ago are now readily solvable – many in real time.


Fig. 15: Performance Evolution of ILOG CPLEX LP over One Decade

Matrix Fit The following matrix shows which ILOG engine can be applied to which type of application in telecommunications.

Applications \ Products ILOG CPLEX

ILOG Solver

ILOG Configurator

ILOG Dispatcher

ILOG Scheduler

OSPF TE

Circuit Design, Offline TE

Adaptive Configuration

Network Planning

Sales & Hardware Configuration

Field Technician Dispatching

Maintenance Scheduling


Conclusion Need for Optimization in Telecommunications In the short term, there is a lot of pressure on telecommunications operators to prove they can generate respectable profit margins. We have seen how two optimization applications -- traffic engineering and adaptive network configuration -- are crucial to “do more with less” and help improve the profitability of telecommunications services. These applications have been successfully implemented using the ILOG Optimization Suite. Another area where optimization helps achieve better investment decisions is network planning. In order to deploy networks at the right pace and better meet actual customer demand, it is important to make optimization decisions concerning equipment location and capacity. The ILOG Optimization Suite is ideal for building efficient software solutions for network planning. There are other areas where optimization represents an opportunity to improve the performance of operations. In CRM, sales configuration tools reduce the cost of sales while improving the selling process by configuring telecommunications products and services to better meet customer requirements while respecting constraints. In network creation and maintenance, field technician dispatching and maintenance scheduling are two application areas that reduce maintenance costs while improving customer satisfaction. More generally, optimization helps to improve and quicken adaptation of supply to demand. ILOG Value Proposition ILOG Optimization Suite The ILOG Optimization Suite is a unique set of optimization engines for solving an impressive range of optimization problems. ILOG CPLEX is for linear and integer programming, and ILOG Solver for constraint programming and constraint-based local search. Algorithmic innovations pioneered by ILOG in the past 10 years have steadily improved the performance of these engines, enabling them to solve many more problems in much less time. Professional Services ILOG Professional Services provides unmatched expertise and best practices in applying ILOG optimization to telecommunications network problem modeling and application development. ILOG consultants work side by side with customers to guarantee their success with ILOG software. They provide industry-specific knowledge and training, accelerating the development and deployment of applications.

• Faster time to market: Be it an application or a software module, ILOG consultants

can help you get your project to market on time. Experts in designing and implementing with ILOG components, they apply their experience in your industry to get you online fast.

• Maximum risk reduction: ILOG consultants reduce project risks through better problem formulation, architecture and implementation. They know the pitfalls and have proven methods for avoiding them, better addressing the technical and functional constraints of the application.


• Highest quality: ILOG consultants ensure the high quality of applications in terms of functionality, changeability, portability and efficiency. They help find the optimal fit between the end user’s requirements and their state-of-the-art technology, and design with future upgrades in mind. Portability is guaranteed to match both today’s and tomorrow’s platforms and technologies, while efficiency is ensured through the high performance of ILOG products.

• Greater savings: All these activities are conducted with an eye on the bottom line to ensure the customer gets the best solution with the greatest return on investment.


References and Further Reading ILOG Optimization Suite “ILOG Optimization Suite White Paper – Delivering a Competitive Advantage,” download from http://www.ilog.com

“MIP: Theory and Practice – Closing the Gap,” Robert E. Bixby, Mary Fenelon, Zonghao Gu, Ed Rothberg, Roland Wunderling. Download from http://www.ilog.com/products/cplex/tech_papers.cfm

Traffic Engineering “Traffic Engineering Solutions for Core Networks.” A paper in the Backbone Network and Business Solution Series. Alcatel. Download from http://www.cid.alcatel.com/industry_analysts/secure/pdf/traffic_eng.pdf

“Optimizing Routing Software for Reliable Internet Growth.” White Paper. Chuck Semeria and John W. Stewart. Juniper, Download from http://www.juniper.net/techcenter/techpapers/

Optimization in Signaling Networks “Re-Optimization of Signaling Transfer Points,” Arie M.C.A. Koster. May 2000. Download from http://www.zib.de

“Load Balancing in Signaling Transfer Points using CPLEX,” Andreas Eisenblätter, Jean-Christophe Jardinier, Arie Koster, Randolf Wallbaum, Roland Wessäly. To be presented at the 6th INFORMS Telecommunications Conference 2002.

More information on SS7 signaling networks can be found at http://www.pt.com and at http://www.ss7.com

The Economics of Networks “A taxonomy of communications demand,” Steven G. Lanning, Shawn R. O’Donnell, W. Russell Neuman, September 1999

“The history of communications and its implications for the Internet,” Andrew Odlyzko, June 2000

“The economics of networks,” Nicholas Economides, September 1995


http://www.zib.de/

http://www.pt.com/

http://www.ss7.com/

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