Optimal Routing Designs in Self-healing Communications Networks

Self-healing communication networks that allow re-routing of demands through switching processes at designated nodes are studied. It is shown how network utilization, demand throughput and reliability of such networks can be studied simultaneously to achieve an optimal design for all three. This is done through a max–min–max multi-commodity network flow formulation of the routing problem in which it is ensured that maximum network throughput is achieved with minimum loss of demands that are blocked due to single switching node failures. It is shown that a node-path linear programming approximation to the multi-commodity network flow formulation solves the problem for medium and large network sizes in moderate computational times.     

Studies of random demands on network costs

When network demands are uncertain, a planner might design a network based on some nominal set of point‐to‐point demands, and later be faced with a different set of offered demands. To accommodate the offered demands, modification of the network may be required. Given this scenario, it seems natural to question how these modification costs might affect the overall cost. To address such questions, we study the effects of random demands on network costs. In this study, we design a network based on nominal demands, generate random demands based on the nominal demands, and then modify the designed network to carry the random demands. We generate the offered demands randomly from four different distributions. For each demand distribution we perform 300 simulations. This paper describes our observations. This revised version was published online in June 2006 with corrections to the Cover Date.     

Scheduling bursts in time-domain wavelength interleaved networks

A time-domain wavelength interleaved network (TWIN) (Widjaja, I. et al., IEEE Commun. Mag., vol.41, 2003) is an optical network with an ultrafast tunable laser and a fixed receiver at each node. We consider the problem of scheduling bursts of data in a TWIN. Due to the high data rates employed on the optical links, the burst transmissions typically last for very short times compared with the round trip propagation times between source-destination pairs. A good schedule should ensure that: 1) there are no transmit/receive conflicts; 2) propagation delays are observed; 3) throughput is maximized (schedule length is minimized). We formulate the scheduling problem with periodic demand as a generalization of the well-known crossbar switch scheduling. We prove that even in the presence of propagation delays, there exist a class of computationally viable scheduling algorithms which asymptotically achieve the maximum throughput obtainable without propagation delays. We also show that any schedule can be rearranged to achieve a factor-two approximation of the maximum throughput even without asymptotic limits. However, the delay/throughput performance of these schedules is limited in practice. We consequently propose a scheduling algorithm that exhibits near optimal (on average within /spl sim/7% of optimum) delay/throughput performance in realistic network examples.     

Prediction of failure in tube hydroforming process using a damage model

In tube hydroforming process (THP), two types of loading, internal pressure and axial feeding and in particular the combination of them, are needed to feed the material into the cavities of the die to form the workpiece into the desired shape. If the variation of pressure versus axial feeding is not determined properly, the workpiece may be buckled, wrinkled or burst during THP. The appropriate variation is normally determined by experiment which is expensive and time-consuming. In this work, numerical simulation using Johnson-Cook models for predicting the elasto-plastic response and the failure of the material are employed to obtain the best combination of internal pressure and axial feeding. The numerical simulations are examined by a number of experiments conducted in the present investigation. The results show very close agreement between the numerical simulations and the experiments, suggesting that the numerical simulations using Johnson-Cook material and failure models provide a valuable tool to examine the different parameters involved in THP.     

An optimization problem related to balancing loads on SONET rings

We provide a model and a set of solution techniques for an important problem arising in the design of survivable telecommunication networks utilizing fiber-optics-based technologies. The emergence of a synchronous standard for optical signaling called SONET allows for an economic implementation of ring designs that provides protection for high capacity services. An objective is to choose a loading of the demands onto a ring design that minimizes associated equipment and facility costs while providing capacity for alternative routing should some link or node fail. After the computational complexity of the problem has been determined, three approximation heuristics, including a mathematical programming dual-ascent solution technique, are described and compared. The heuristics are being successfully applied to actual network design problems arising in Bell operating companies and other telecommunication providers.     

Data mining with a simulated annealing based fuzzy classification system

In this paper, the use of simulated annealing (SA) metaheuristic for constructing a fuzzy classification system is presented. In several previous investigations, the capability of fuzzy systems to solve different kinds of problems has been demonstrated. Simulated annealing based fuzzy classification system (SAFCS), hybridizes the learning capability of SA metaheuristic with the approximate reasoning method of fuzzy systems. The objective of this paper is to illustrate the ability of SA to develop an accurate fuzzy classifier. The use of SA in classification is an attempt to effectively explore and exploit the large search space usually associated with classification problems, and find the optimum set of fuzzy if–then rules. The SAFCS would be capable to extract accurate fuzzy classification rules from input data sets, and applies them to classify new data instances in different predefined groups or classes. Experiments are performed with eight UCI data sets. The results indicate that the proposed SAFCS achieves competitive results in comparison with several well-known classification algorithms.     

A parallel genetic local search algorithm for intrusion detection in computer networks

The security of networked computers plays a strategic role in modern computer systems. This task is so complicated because the determination of normal and abnormal behaviors in computer networks is hard, as the boundaries cannot be well defined. One of the difficulties in such a prediction process is the generation of false alarms in many anomaly based intrusion detection systems. However, fuzzy logic is an important solution to reduce the false alarm rate in determining intrusive activities. This paper proposes a parallel genetic local search algorithm (PAGELS) to generate fuzzy rules capable of detecting intrusive behaviors in computer networks. The system uses the Michigan's approach, where each individual represents a fuzzy rule which has the form “if condition then prediction.” In the presented algorithm the global population is divided into some subpopulations, each assigned to a distinct processor. Each subpopulation consists of the same class fuzzy rules. These rules evolve independently in the proposed parallel manner. Experimental results show that the presented algorithm produces fuzzy rules, which can be used to construct a reliable intrusion detection system.     

Water—ethanol—gasoline blends as spark ignition engine fuels

The miscibility characteristics of hydrated ethanol with gasoline is investigated as a means of reducing the cost of ethanol/gasoline blends for use as a spark ignition engine fuel. For a given percentage of water in the ethanol, the experimental data shows that a limited volume of gasoline can be added to form a stable mixture. Engine experiments indicate that, at normal ambient temperatures, a water/ethanol/gasoline mixture containing up to 6 vol% of water in the ethanol constitutes a desirable motor fuel with power characteristics similar to those of the base gasoline. As a means of reducing the smog causing components of the exhaust gases, such as the oxides of nitrogen and the unburnt hydrocarbons, the water/ethanol/gasoline mixture is superior to the base gasoline.     

A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis

Classification systems have been widely utilized in medical domain to explore patient’s data and extract a predictive model. This model helps physicians to improve their prognosis, diagnosis or treatment planning procedures. The aim of this paper is to use an Ant Colony-based classification system to extract a set of fuzzy rules for diagnosis of diabetes disease, named FCS-ANTMINER. We will review some recent methods and describe a new and efficient approach that leads us to considerable results for diabetes disease classification problem. FCS-ANTMINER has new characteristics that make it different from the existing methods that have utilized the Ant Colony Optimization (ACO) for classification tasks. The obtained classification accuracy is 84.24% which reveals that FCS-ANTMINER outperforms several famous and recent methods in classification accuracy for diabetes disease diagnosis.