With maritime industry carrying out nearly 90% of the volume of global trade, the algorithms and solutions to provide quality of services in maritime transportation are of great importance to both academia and the industry. This research investigates an optimization problem using evolutionary algorithms and big data analytics to address an important challenge in maritime disruption management, and illustrates how it can be engaged with information technologies and Internet of Things. Accordingly, in this thesis, we design, develop and evaluate methods to improve decision support systems (DSSs) in maritime supply chain management. We pursue three research goals in this thesis. First, the Vessel Schedule recovery Problem (VSRP) is reformulated and a bi-objective optimization approach is proposed. We employ bi-objective evolutionary algorithms (MOEAs) to solve optimization problems. An optimal Pareto front provides a valuable trade-off between two objectives (minimizing delay and minimizing financial loss) for a stakeholder in the freight ship company. We evaluate the problem in three domains, namely scalability analysis, vessel steaming policies, and voyage distance analysis, and statistically validate their performance significance. According to the experiments, the problem complexity varies in different scenarios, while NSGAII performs better than other MOEAs in all scenarios. In the second work, a new data-driven VSRP is proposed, which benefits from the available Automatic Identification System (AIS) data. In the new formulation, the trajectory between the port calls is divided and encoded into adjacent geohashed regions. In each geohash, the historical speed profiles are extracted from AIS data. This results in a large-scale optimization problem called G-S-VSRP with three objectives (i.e., minimizing loss, delay, and maximizing compliance) where the compliance objective maximizes the compliance of optimized speeds with the historical data. Assuming that the historical speed profiles are reliable to trust for actual operational speeds based on other ships' experience, maximizing the compliance of optimized speeds with these historical data offers some degree of avoiding risks. Three MOEAs tackled the problem and provided the stakeholder with a Pareto front which reflects the trade-off among the three objectives. Geohash granularity and dimensionality reduction techniques were evaluated and discussed for the model. G-S-VSRPis a large-scale optimization problem and suffers from the curse of dimensionality (i.e. problems are difficult to solve due to exponential growth in the size of the multi-dimensional solution space), however, due to a special characteristic of the problem instance, a large number of function evaluations in MOEAs can still find a good set of solutions. Finally, when the compliance objective in G-S-VSRP is changed to minimization, the regular MOEAs perform poorly due to the curse of dimensionality. We focus on improving the performance of the large-scale G-S-VSRP through a novel distributed multiobjective cooperative coevolution algorithm (DMOCCA). The proposed DMOCCA improves the quality of performance metrics compared to the regular MOEAs (i.e. NSGAII, NSGAIII, and GDE3). Additionally, the DMOCCA results in speedup when running on a cluster.

nÃo hà / Este trabalho foi motivado pela necessidade da exploraÃÃo do potencial do paralelismo distribuÃdo em aplicaÃÃes em OtimizaÃÃo CombinatÃria. Para tanto, propomos uma interface de programaÃÃo distribuÃda, na qual prezamos dois requisitos principais: eficiÃncia e reuso. O primeiro advÃm da necessidade de aplicaÃÃes de CAD exigirem mÃximo desempenho possÃvel. Assim sendo, especificamos esta interface como uma extensÃo da biblioteca MPI, a qual à assumida como eficiente para aplicaÃÃes distribuÃdas. O requisito reuso deve tornar compatÃveis duas caracterÃsticas importantes: assincronismo e operaÃÃes coletivas. O assincronismo deve estar presente na interface, uma vez que as aplicaÃÃes em OtimizaÃÃo CombinatÃria, em sua maioria, possuem uma natureza assÃncrona. OperaÃÃes coletivas sÃo funcionalidades que devem estar disponÃveis na interface, de modo que possam ser utilizadas por aplicaÃÃes em suas execuÃÃes. Tendo em vista atender o requisito reuso, baseamos esta interface nos Modelos de ComputaÃÃo DistribuÃda Dirigidos por Eventos e por Pulsos, pois os mesmos sÃo assÃncronos e permitem a incorporaÃÃo de operaÃÃes coletivas. Implementamos parcialmente a inteface definida neste trabalho. Tendo em vista validar uso desta inteface por aplicaÃÃes em OtimizaÃÃo CombinatÃria, selecionamos duas aplicaÃÃes e as implementamos utilizando a interface. SÃo elas a tÃcnica Branch-and-Bound e o Problema do Conjunto Independente MÃximo (CIM). Fornecemos tambÃm alguns resultados experimentais. / This work was motivated by the need of exploiting the potential of distributed paralelism in combinatorial optimization applications. propose a distributed programming interface, To achieve this goal, we in which we cherish two main requirements: eciency and reuse. The rst stems from the need of HPC (High applications require maximum possible performance. Performance Computing) Therefore, we specify our interface as an extension of the MPI library, which is assumed to be ecient for distributed applications. The reuse requirement must make compatible two important features: asynchronism and collective operations. Asynchronism must be present at our interface, once most of combinatorial optimization applications have an asynchronous nature. Collective operations are features that should be available in the interface, so that they can be used by applications in their execution. In order reach the reuse requirement, we based this interface on the Event- and Pulse-driven Models of Distributed Computing, once they are asynchronous and allow the incorporation of collective operations. We implemented partially the interface dened in this work. In order to validate the use of the inteface by combinatorial optimization applications, we selected two applications and implemented them using our interface. They are the Branch-and-Bound technique and the Maximum Stable Set Problem (MSSP). We also provide some experimental results.

Smart-phones, wearables and mobile devices in general are the sensors of our modern world. Their sensing capabilities offer the means to analyze and interpret our behaviour and surroundings. When it comes to human behaviour, perhaps the most informative feature is our location and mobility habits. Insights from human mobility are useful in a number of everyday practical applications, such as the improvement of transportation and road network infrastructure, ride-sharing services, activity recognition, mobile data pre-fetching, analysis of the social behaviour of humans, etc. In this dissertation, we develop algorithms for processing mobility data. The analysis of mobility data is a non trivial task as it involves managing large quantities of location information, usually spread out spatially and temporally across many tracking sensors. An additional challenge in processing mobility information is to publish the data and the results of its analysis without jeopardizing the privacy of the involved individuals or the quality of the data. We look into a series of problems on processing mobility data from individuals and from a population. Our mission is to design algorithms with provable properties that allow for the fast and reliable extraction of insights. We present efficient solutions - in terms of storage and computation requirements - , with a focus on distributed computation, online processing and privacy preservation.

This dissertation presents algorithms for data structures called distributed hash tables (DHT) or structured overlay networks, which are used to build scalable self-managing distributed systems. The provided algorithms guarantee lookup consistency in the presence of dynamism: they guarantee consistent lookup results in the presence of nodes joining and leaving. Similarly, the algorithms guarantee that routing never fails while nodes join and leave. Previous algorithms for lookup consistency either suffer from starvation, do not work in the presence of failures, or lack proof of correctness. Several group communication algorithms for structured overlay networks are presented. We provide an overlay broadcast algorithm, which unlike previous algorithms avoids redundant messages, reaching all nodes in O(log n) time, while using O(n) messages, where n is the number of nodes in the system. The broadcast algorithm is used to build overlay multicast. We introduce bulk operation, which enables a node to efficiently make multiple lookups or send a message to all nodes in a specified set of identifiers. The algorithm ensures that all specified nodes are reached in O(log n) time, sending maximum O(log n) messages per node, regardless of the input size of the bulk operation. Moreover, the algorithm avoids sending redundant messages. Previous approaches required multiple lookups, which consume more messages and can render the initiator a bottleneck. Our algorithms are used in DHT-based storage systems, where nodes can do thousands of lookups to fetch large files. We use the bulk operation algorithm to construct a pseudo-reliable broadcast algorithm. Bulk operations can also be used to implement efficient range queries. Finally, we describe a novel way to place replicas in a DHT, called symmetric replication, that enables parallel recursive lookups. Parallel lookups are known to reduce latencies. However, costly iterative lookups have previously been used to do parallel lookups. Moreover, joins or leaves only require exchanging O(1) messages, while other schemes require at least log(f) messages for a replication degree of f. The algorithms have been implemented in a middleware called the Distributed k-ary System (DKS), which is briefly described. / QC 20100824

The main contributions of this thesis are located in the domain of wireless sensor netorks. More in detail, we introduce energyaware algorithms and protocols in the context of the following topics: self-synchronized duty-cycling in networks with energy harvesting capabilities, distributed graph coloring and minimum energy broadcasting with realistic antennas. In the following, we review the research conducted in each case. We propose a self-synchronized duty-cycling mechanism for sensor networks. This mechanism is based on the working and resting phases of natural ant colonies, which show self-synchronized activity phases. The main goal of duty-cycling methods is to save energy by efficiently alternating between different states. In the case at hand, we considered two different states: the sleep state, where communications are not possible and energy consumption is low; and the active state, where communication result in a higher energy consumption. In order to test the model, we conducted an extensive experimentation with synchronous simulations on mobile networks and static networks, and also considering asynchronous networks. Later, we extended this work by assuming a broader point of view and including a comprehensive study of the parameters. In addition, thanks to a collaboration with the Technical University of Braunschweig, we were able to test our algorithm in the real sensor network simulator Shawn (http://shawn.sf.net). The second part of this thesis is devoted to the desynchronization of wireless sensor nodes and its application to the distributed graph coloring problem. In particular, our research is inspired by the calling behavior of Japanese tree frogs, whose males use their calls to attract females. Interestingly, as female frogs are only able to correctly localize the male frogs when their calls are not too close in time, groups of males that are located nearby each other desynchronize their calls. Based on a model of this behavior from the literature, we propose a novel algorithm with applications to the field of sensor networks. More in detail, we analyzed the ability of the algorithm to desynchronize neighboring nodes. Furthermore, we considered extensions of the original model, hereby improving its desynchronization capabilities.To illustrate the potential benefits of desynchronized networks, we then focused on distributed graph coloring. Later, we analyzed the algorithm more extensively and show its performance on a larger set of benchmark instances. The classical minimum energy broadcast (MEB) problem in wireless ad hoc networks, which is well-studied in the scientific literature, considers an antenna model that allows the adjustment of the transmission power to any desired real value from zero up to the maximum transmission power level. However, when specifically considering sensor networks, a look at the currently available hardware shows that this antenna model is not very realistic. In this work we re-formulate the MEB problem for an antenna model that is realistic for sensor networks. In this antenna model transmission power levels are chosen from a finite set of possible ones. A further contribution concerns the adaptation of an ant colony optimization algorithm --currently being the state of the art for the classical MEB problem-- to the more realistic problem version, the so-called minimum energy broadcast problem with realistic antennas (MEBRA). The obtained results show that the advantage of ant colony optimization over classical heuristics even grows when the number of possible transmission power levels decreases. Finally we build a distributed version of the algorithm, which also compares quite favorably against centralized heuristics from the literature. / Las principles contribuciones de esta tesis se encuentran en el domino de las redes de sensores inalámbricas. Más en detalle, introducimos algoritmos y protocolos que intentan minimizar el consumo energético para los siguientes problemas: gestión autosincronizada de encendido y apagado de sensores con capacidad para obtener energía del ambiente, coloreado de grafos distribuido y broadcasting de consumo mínimo en entornos con antenas reales. En primer lugar, proponemos un sistema capaz de autosincronizar los ciclos de encendido y apagado de los nodos de una red de sensores. El mecanismo está basado en las fases de trabajo y reposo de las colonias de hormigas tal y como estas pueden observarse en la naturaleza, es decir, con fases de actividad autosincronizadas. El principal objectivo de este tipo de técnicas es ahorrar energía gracias a alternar estados de forma eficiente. En este caso en concreto, consideramos dos estados diferentes: el estado dormido, en el que los nodos no pueden comunicarse y el consumo energético es bajo; y el estado activo, en el que las comunicaciones propician un consumo energético elevado. Con el objetivo de probar el modelo, se ha llevado a cabo una extensa experimentación que incluye tanto simulaciones síncronas en redes móviles y estáticas, como simulaciones en redes asíncronas. Además, este trabajo se extendió asumiendo un punto de vista más amplio e incluyendo un detallado estudio de los parámetros del algoritmo. Finalmente, gracias a la colaboración con la Technical University of Braunschweig, tuvimos la oportunidad de probar el mecanismo en el simulador realista de redes de sensores, Shawn (http://shawn.sf.net). La segunda parte de esta tesis está dedicada a la desincronización de nodos en redes de sensores y a su aplicación al problema del coloreado de grafos de forma distribuida. En particular, nuestra investigación está inspirada por el canto de las ranas de árbol japonesas, cuyos machos utilizan su canto para atraer a las hembras. Resulta interesante que debido a que las hembras solo son capaces de localizar las ranas macho cuando sus cantos no están demasiado cerca en el tiempo, los grupos de machos que se hallan en una misma región desincronizan sus cantos. Basado en un modelo de este comportamiento que se encuentra en la literatura, proponemos un nuevo algoritmo con aplicaciones al campo de las redes de sensores. Más en detalle, analizamos la habilidad del algoritmo para desincronizar nodos vecinos. Además, consideramos extensiones del modelo original, mejorando su capacidad de desincronización. Para ilustrar los potenciales beneficios de las redes desincronizadas, nos centramos en el problema del coloreado de grafos distribuido que tiene relación con diferentes tareas habituales en redes de sensores. El clásico problema del broadcasting de consumo mínimo en redes ad hoc ha sido bien estudiado en la literatura. El problema considera un modelo de antena que permite transmitir a cualquier potencia elegida (hasta un máximo establecido por el dispositivo). Sin embargo, cuando se trabaja de forma específica con redes de sensores, un vistazo al hardware actualmente disponible muestra que este modelo de antena no es demasiado realista. En este trabajo reformulamos el problema para el modelo de antena más habitual en redes de sensores. En este modelo, los niveles de potencia de transmisión se eligen de un conjunto finito de posibilidades. La siguiente contribución consiste en en la adaptación de un algoritmo de optimización por colonias de hormigas a la versión más realista del problema, también conocida como broadcasting de consumo mínimo con antenas realistas. Los resultados obtenidos muestran que la ventaja de este método sobre heurísticas clásicas incluso crece cuando el número de posibles potencias de transmisión decrece. Además, se ha presentado una versión distribuida del algoritmo, que también se compara de forma bastante favorable contra las heurísticas centralizadas conocidas.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2009. / Includes bibliographical references.

We analyze spatio-temporal routing under various constraints specific to multi-robot applications. Spatio-temporal routing requires multiple robots to visit spatial locations at specified time instants, while optimizing certain criteria like the total distance traveled, or the total energy consumed. Such a spatio-temporal concept is intuitively demonstrable through music (e.g. a musician routes multiple fingers to play a series of notes on an instrument at specified time instants). As such, we showcase much of our work on routing through this medium. Particular to robotic applications, we analyze constraints like maximum velocities that the robots cannot exceed, and information-exchange networks that must remain connected. Furthermore, we consider a notion of heterogeneity where robots and spatial locations are associated with multiple skills, and a robot can visit a location only if it has at least one skill in common with the skill set of that location. To extend the scope of our work, we analyze spatio-temporal routing in the context of a distributed framework, and a dynamic environment.

Wireless networks promise ubiquitous communication, and thus facilitate an array of applications that positively impact human life. At a fundamental level, these networks deal with compression and transmission of sources over channels. Thus, accomplishing this task efficiently is the primary challenge shared by these applications. In practice, sources include data and video while channels include interference and relay networks. Hence, effective source and channel aware resource allocation for these scenarios would result in a comprehensive solution applicable to real-world networks. This dissertation studies the problem of source and channel aware resource allocation in certain scenarios. A framework for network resource allocation that stems from rate-distortion theory is presented. Then, an optimal decomposition into an application-layer compression control, a transport-layer congestion control and a network-layer scheduling is obtained. After deducing insights into compression and congestion control, the scheduling problem is explored in two cross-layer scenarios. First, appropriate queue architecture for cooperative relay networks is presented, and throughput-optimality of network algorithms that do not assume channel-fading and input-queue distributions are established. Second, decentralized algorithms that perform rate allocation, which achieve the same overall throughput region as optimal centralized algorithms, are derived. In network optimization, an underlying throughput region is assumed. Hence, improving this throughput region is the next logical step. This dissertation addresses this problem in the context of three significant classes of interference networks. First, degraded networks that capture highly correlated channels are explored, and the exact sum capacity of these networks is established. Next, multiple antenna networks in the presence of channel uncertainty are considered. For these networks, robust optimization problems that result from linear precoding are investigated, and efficient iterative algorithms are derived. Last, multi-cell time-division-duplex systems are studied in the context of corrupted channel estimates, and an efficient linear precoding to manage interference is developed. / text

Video traffic constitutes a major part of the Internet traffic nowadays. Yet most video delivery services remain best-effort, relying on server bandwidth over-provisioning to guarantee Quality of Service (QoS). Cloud computing is changing the way that video services are offered, enabling elastic and efficient resource allocation through auto-scaling. In this thesis, we propose a new framework of cloud workload management for multimedia delivery services, incorporating demand forecast, predictive resource allocation and quality assurance, as well as resource pricing as inter-dependent components. Based on the trace analysis of a production Video-on-Demand (VoD) system, we propose time-series techniques to predict video bandwidth demand from online monitoring, and determine bandwidth reservations from multiple data centers and the related load direction policy. We further study how such quality-guaranteed cloud services should be priced, in both a game theoretical model and an optimization model.Particularly, when multiple video providers coexist to use cloud resources, we use pricing to control resource allocation in order to maximize the aggregate network utility, which is a standard network utility maximization (NUM) problem with coupled objectives. We propose a novel class of iterative distributed solutions to such problems with a simple economic interpretation of pricing. The method proves to be more efficient than the conventional approach of dual decomposition and gradient methods for large-scale systems, both in theory and in trace-driven simulations.

Video traffic constitutes a major part of the Internet traffic nowadays. Yet most video delivery services remain best-effort, relying on server bandwidth over-provisioning to guarantee Quality of Service (QoS). Cloud computing is changing the way that video services are offered, enabling elastic and efficient resource allocation through auto-scaling. In this thesis, we propose a new framework of cloud workload management for multimedia delivery services, incorporating demand forecast, predictive resource allocation and quality assurance, as well as resource pricing as inter-dependent components. Based on the trace analysis of a production Video-on-Demand (VoD) system, we propose time-series techniques to predict video bandwidth demand from online monitoring, and determine bandwidth reservations from multiple data centers and the related load direction policy. We further study how such quality-guaranteed cloud services should be priced, in both a game theoretical model and an optimization model.Particularly, when multiple video providers coexist to use cloud resources, we use pricing to control resource allocation in order to maximize the aggregate network utility, which is a standard network utility maximization (NUM) problem with coupled objectives. We propose a novel class of iterative distributed solutions to such problems with a simple economic interpretation of pricing. The method proves to be more efficient than the conventional approach of dual decomposition and gradient methods for large-scale systems, both in theory and in trace-driven simulations.