Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

Web Service Composition Systems for the Web Service Challenge - A Detailed Review

This report gives a detailed discussion on the system, algorithms, and techniques that we have applied in order to solve the Web Service Challenges (WSC) of the years 2006 and 2007. These international contests are focused on semantic web service composition. In each challenge of the contests, a repository of web services is given. The input and output parameters of the services in the repository are annotated with semantic concepts. A query to a semantic composition engine contains a set of available input concepts and a set of wanted output concepts. In order to employ an offered service for a requested role, the concepts of the input parameters of the offered operations must be more general than requested (contravariance). In contrast, the concepts of the output parameters of the offered service must be more specific than requested (covariance). The engine should respond to a query by providing a valid composition as fast as possible. We discuss three different methods for web service composition: an uninformed search in form of an IDDFS algorithm, a greedy informed search based on heuristic functions, and a multi-objective genetic algorithm.

Finding short patterns to classify text documents

Many classification methods have been proposed to find patterns in text documents. However, according to Occam's razor principle, "the explanation of any phenomenon should make as few assumptions as possible", short patterns usually have more explainable and meaningful for classifying text documents. In this paper, we propose a depth-first pattern generation algorithm, which can find out short patterns from text document more effectively, comparing with breadth-first algorithm

Simulation and visualization of fire using extended Lindenmayer systems

This research introduces a method of using Lindenmayer Systems to model the spreading and behavior of fire inside a factory building. The research investigates the use of L-System propagated fires for determining factors such as where the fire is most likely to spread first and how fast. It also looks at an alternative way of storing the Lindenmayer System, not in the form of a string but rather as a graph. A variation on the building and traversal process is also investigated, in which the L-System is traversed depth first instead of breadth first. Results of fire propagation are presented and we conclude that L-Systems are a suitable tool for fire propagation.

Retrieval of episodic versus generic information : does the order of recall affect the amount and accuracy of details reported by children about repeated events?

Children (N = 157) 4 to 8 years old participated 1 time (single) or 4 times (repeated) in an interactive event. Across each condition, half were questioned a week later about the only or a specific occurrence of the event (depth first) and then about what usually happens. Half were prompted in the reverse order (breadth first). Children with repeated experience who first were asked about what usually happens reported more eventrelated information overall than those asked about an occurrence first. All children used episodic language when describing an occurrence; however, children with repeated-event experience used episodic language less often when describing what usually happens than did those with a single experience. Accuracy rates did not differ between conditions. Implications for theories of repeated-event memory are discussed.

Beam Search Algorithms for Minimizing Tool Switches on a Flexible Manufacturing System

In the minimization of tool switches problem we seek a sequence to process a set of jobs so that the number of tool switches required is minimized. In this work different variations of a heuristic based on partial ordered job sequences are implemented and evaluated. All variations adopt a depth first strategy of the enumeration tree. The computational test results indicate that good results can be obtained by a variation which keeps the best three branches at each node of the enumeration tree, and randomly choose, among all active nodes, the next node to branch when backtracking.

Uma investigação de algoritmos exatos e metaheurísticos aplicados ao nonograma

Nonogram is a logical puzzle whose associated decision problem is NP-complete. It has applications in pattern recognition problems and data compression, among others. The puzzle consists in determining an assignment of colors to pixels distributed in a N  M matrix that satisfies line and column constraints. A Nonogram is encoded by a vector whose elements specify the number of pixels in each row and column of a figure without specifying their coordinates. This work presents exact and heuristic approaches to solve Nonograms. The depth first search was one of the chosen exact approaches because it is a typical example of brute search algorithm that is easy to implement. Another implemented exact approach was based on the Las Vegas algorithm, so that we intend to investigate whether the randomness introduce by the Las Vegas-based algorithm would be an advantage over the depth first search. The Nonogram is also transformed into a Constraint Satisfaction Problem. Three heuristics approaches are proposed: a Tabu Search and two memetic algorithms. A new function to calculate the objective function is proposed. The approaches are applied on 234 instances, the size of the instances ranging from 5 x 5 to 100 x 100 size, and including logical and random Nonograms

Content Discovery in Wireless Information-Centric Networks

Information-centric networking (ICN) enables communication in isolated islands, where fixed infrastructure is not available, but also supports seamless communication if the infrastructure is up and running again. In disaster scenarios, when a fixed infrastructure is broken, content discovery algorit hms are required to learn what content is locally available. For example, if preferred content is not available, users may also be satisfied with second best options. In this paper, we describe a new content discovery algorithm and compare it to existing Depth-first and Breadth-first traversal algorithms. Evaluations in mobile scenarios with up to 100 nodes show that it results in better performance, i.e., faster discovery time and smaller traffic overhead, than existing algorithms.

Efficient local unfolding with ancestor stacks

The most successful unfolding rules used nowadays in the partial evaluation of logic programs are based on well quasi orders (wqo) applied over (covering) ancestors, i.e., a subsequence of the atoms selected during a derivation. Ancestor (sub)sequences are used to increase the specialization power of unfolding while still guaranteeing termination and also to reduce the number of atoms for which the wqo has to be checked. Unfortunately, maintaining the structure of the ancestor relation during unfolding introduces significant overhead. We propose an efficient, practical local unfolding rule based on the notion of covering ancestors which can be used in combination with a wqo and allows a stack-based implementation without losing any opportunities for specialization. Using our technique, certain non-leftmost unfoldings are allowed as long as local unfolding is performed, i.e., we cover depth-first strategies. To deal with practical programs, we propose assertion-based techniques which allow our approach to treat programs that include (Prolog) built-ins and external predicates in a very extensible manner, for the case of leftmost unfolding. Finally, we report on our mplementation of these techniques embedded in a practical partial evaluator, which shows that our techniques, in addition to dealing with practical programs, are also significantly more efficient in time and somewhat more efficient in memory than traditional tree-based implementations. To appear in Theory and Practice of Logic Programming (TPLP).

Constructs and evaluation strategies for intelligent speculative parallelism - armageddon revisited

This report addresses speculative parallelism (the assignment of spare processing resources to tasks which are not known to be strictly required for the successful completion of a computation) at the user and application level. At this level, the execution of a program is seen as a (dynamic) tree —a graph, in general. A solution for a problem is a traversal of this graph from the initial state to a node known to be the answer. Speculative parallelism then represents the assignment of resources to múltiple branches of this graph even if they are not positively known to be on the path to a solution. In highly non-deterministic programs the branching factor can be very high and a naive assignment will very soon use up all the resources. This report presents work assignment strategies other than the usual depth-first and breadth-first. Instead, best-first strategies are used. Since their definition is application-dependent, the application language contains primitives that allow the user (or application programmer) to a) indícate when intelligent OR-parallelism should be used; b) provide the functions that define "best," and c) indícate when to use them. An abstract architecture enables those primitives to perform the search in a "speculative" way, using several processors, synchronizing them, killing the siblings of the path leading to the answer, etc. The user is freed from worrying about these interactions. Several search strategies are proposed and their implementation issues are addressed. "Armageddon," a global pruning method, is introduced, together with both a software and a hardware implementation for it. The concepts exposed are applicable to áreas of Artificial Intelligence such as extensive expert systems, planning, game playing, and in general to large search problems. The proposed strategies, although showing promise, have not been evaluated by simulation or experimentation.

Manejo de memoria en programación lógica con tabulación

Los lenguajes de programación son el idioma que los programadores usamos para comunicar a los computadores qué queremos que hagan. Desde el lenguaje ensamblador, que traduce una a una las instrucciones que interpreta un computador hasta lenguajes de alto nivel, se ha buscado desarrollar lenguajes más cercanos a la forma de pensar y expresarse de los humanos. Los lenguajes de programación lógicos como Prolog utilizan a su vez el lenguaje de la lógica de 1er orden de modo que el programador puede expresar las premisas del problema que se quiere resolver sin preocuparse del cómo se va a resolver dicho problema. La resolución del problema se equipara a encontrar una deducción del objetivo a alcanzar a partir de las premisas y equivale a lo que entendemos por la ejecución de un programa. Ciao es una implementación de Prolog (http://www.ciao-lang.org) y utiliza el método de resolución SLD, que realiza el recorrido de los árboles de decisión en profundidad(depth-first) lo que puede derivar en la ejecución de una rama de busqueda infinita (en un bucle infinito) sin llegar a dar respuestas. Ciao, al ser un sistema modular, permite la utilización de extensiones para implementar estrategias de resolución alternativas como la tabulación (OLDT). La tabulación es un método alternativo que se basa en memorizar las llamadas realizadas y sus respuestas para no repetir llamadas y poder usar las respuestas sin recomputar las llamadas. Algunos programas que con SLD entran en un bucle infinito, gracias a la tabulación dán todas las respuestas y termina. El modulo tabling es una implementación de tabulación mediante el algoritmo CHAT. Esta implementación es una versión beta que no tiene implementado un manejador de memoria. Entendemos que la gestión de memoria en el módulo de tabling tiene gran importancia, dado que la resolución con tabulación permite reducir el tiempo de computación (al no repetir llamadas), aumentando los requerimientos de memoria (para guardar las llamadas y las respuestas). Por lo tanto, el objetivo de este trabajo es implementar un mecanismo de gestión de la memoria en Ciao con el módulo tabling cargado. Para ello se ha realizado la implementación de: Un mecanismo de captura de errores que: detecta cuando el computador se queda sin memoria y activa la reinicialización del sitema. Un procedimiento que ajusta los punteros del modulo de tabling que apuntan a la WAM tras un proceso de realojo de algunas de las áreas de memoria de la WAM. Un gestor de memoria del modulo de tabling que detecta c realizar una ampliación de las áreas de memoria del modulo de tabling, realiza la solicitud de más memoria y realiza el ajuste de los punteros. Para ayudar al lector no familiarizado con este tema, describimos los datos que Ciao y el módulo de tabling alojan en las áreas de memoria dinámicas que queremos gestionar. Los casos de pruebas desarrollados para evaluar la implementación del gestor de memoria, ponen de manifiesto que: Disponer de un gestor de memoria dinámica permite la ejecución de programas en un mayor número de casos. La política de gestión de memoria incide en la velocidad de ejecución de los programas. ---ABSTRACT---Programming languages are the language that programmers use in order to communicate to computers what we want them to do. Starting from the assembly language, which translates one by one the instructions to the computer, and arriving to highly complex languages, programmers have tried to develop programming languages that resemble more closely the way of thinking and communicating of human beings. Logical programming languages, such as Prolog, use the language of logic of the first order so that programmers can express the premise of the problem that they want to solve without having to solve the problem itself. The solution to the problem is equal to finding a deduction of the objective to reach starting from the premises and corresponds to what is usually meant as the execution of a program. Ciao is an implementation of Prolog (http://www.ciao-lang.org) and uses the method of resolution SLD that carries out the path of the decision trees in depth (depth-frist). This can cause the execution of an infinite searching branch (an infinite loop) without getting to an answer. Since Ciao is a modular system, it allows the use of extensions to implement alternative resolution strategies, such as tabulation (OLDT). Tabulation is an alternative method that is based on the memorization of executions and their answers, in order to avoid the repetition of executions and to be able to use the answers without reexecutions. Some programs that get into an infinite loop with SLD are able to give all the answers and to finish thanks to tabulation. The tabling package is an implementation of tabulation through the algorithm CHAT. This implementation is a beta version which does not present a memory handler. The management of memory in the tabling package is highly important, since the solution with tabulation allows to reduce the system time (because it does not repeat executions) and increases the memory requirements (in order to save executions and answers). Therefore, the objective of this work is to implement a memory management mechanism in Ciao with the tabling package loaded. To achieve this goal, the following implementation were made: An error detection system that reveals when the computer is left without memory and activate the reinizialitation of the system. A procedure that adjusts the pointers of the tabling package which points to the WAM after a process of realloc of some of the WAM memory stacks. A memory manager of the tabling package that detects when it is necessary to expand the memory stacks of the tabling package, requests more memory, and adjusts the pointers. In order to help the readers who are not familiar with this topic, we described the data which Ciao and the tabling package host in the dynamic memory stacks that we want to manage. The test cases developed to evaluate the implementation of the memory manager show that: A manager for the dynamic memory allows the execution of programs in a larger number of cases. Memory management policy influences the program execution speed.

Host and network optimizations for performance enhancement and energy efficiency in data center networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.

Host and Network Optimizations for Performance Enhancement and Energy Efficiency in Data Center Networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.