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Detection and identification of important biological targets, such as DNA, proteins, and diseased human cells are crucial for early diagnosis and prognosis. The key to discriminate healthy cells from the diseased cells is the biophysical properties that differ radically. Micro and nanosystems, such as solid-state micropores and nanopores can measure and translate these properties of biological targets into electrical spikes to decode useful insights. Nonetheless, such approaches result in sizable data streams that are often plagued with inherit noise and baseline wanders. Moreover, the extant detection approaches are tedious, time-consuming, and error-prone, and there is no error-resilient software that can analyze large data sets instantly. The ability to effectively process and detect biological targets in larger data sets lie in the automated and accelerated data processing strategies using state-of-the-art distributed computing systems. In this dissertation, we design and develop techniques for the detection and classification of biological targets and a distributed detection framework to support data processing from multiple bio-nano devices. In a distributed setup, the collected raw data stream on a server node is split into data segments and distributed across the participating worker nodes. Each node reduces noise in the assigned data segment using moving-average filtering, and detects the electric spikes by comparing them against a statistical threshold (based on the mean and standard deviation of the data), in a Single Program Multiple Data (SPMD) style. Our proposed framework enables the detection of cancer cells in a mixture of cancer cells, red blood cells (RBCs), and white blood cells (WBCs), and achieves a maximum speedup of 6X over a single-node machine by processing 10 gigabytes of raw data using an 8-node cluster in less than a minute, which will otherwise take hours using manual analysis. Diseases such as cancer can be mitigated, if detected and treated at an early stage. Micro and nanoscale devices, such as micropores and nanopores, enable the translocation of biological targets at finer granularity. These devices are tiny orifices in silicon-based membranes, and the output is a current signal, measured in nanoamperes. Solid-state micropore is capable of electrically measuring the biophysical properties of human cells, when a blood sample is passed through it. The passage of cells via such pores results in an interesting pattern (pulse) in the baseline current, which can be measured at a very high rate, such as 500,000 samples per second, and even higher resolution. The pulse is essentially a sequence of temporal data samples that abruptly falls below and then reverts back to a normal baseline with an acceptable predefined time interval, i.e., pulse width. The pulse features, such as width and amplitude, correspond to the translocation behavior and the extent to which the pore is blocked, under a constant potential. These features are crucial in discriminating the diseased cells from healthy cells, such as identifying cancer cells in a mixture of cells. / Ph. D.

Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.

Algoritmos de balanceamento de carga são utilizados em sistemas distribuídos para homogeneizar a ocupação dos recursos computacionais disponíveis. A homogeneidade na ocupação do ambiente permite otimizar a alocação de recursos e, conseqüentemente, aumentar o desempenho das aplicações. Com o advento dos sistemas distribuídos de alta escala, fazem-se necessárias pesquisas para a construção de algoritmos de balanceamento de carga que sejam capazes de gerir com eficiência esses sistemas. Essa eficiência é medida através do número de mensagens geradas no ambiente, do suporte a ambientes heterogêneos, do uso de políticas que consomem poucos recursos do sistema, da estabilidade em alta carga, da escalabilidade do sistema e dos baixos tempos de resposta. Com o objetivo de atender as necessidades dos sistemas distribuídos de alta escala, este doutorado propõe, apresenta e avalia um novo algoritmo de balanceamento de carga denominado TLBA (Tree Load Balancing Algorithm). Esse algoritmo organiza os computadores do sistema em uma topologia lógica na forma de árvore, sobre a qual são executadas operações de balanceamento de carga. Para validar o TLBA foi construído um simulador que, submetido a testes, permitiu comprovar suas contribuições, que incluem: o baixo número de mensagens geradas pelas operações de balanceamento de carga; a estabilidade em altas cargas; os baixos tempos médios de resposta de processos. Para validar os resultados de simulação, foi construído um protótipo do TLBA. Esse protótipo confirmou os resultados de simulação e, conseqüentemente, as contribuições do algoritmo. / Load balancing algorithms are applied in distributed systems to homogenize the occupation of the available computational resources. The homogeneity of the environment occupation allows optimising the resource allocation and consequently, increasing the application performance. With the advent of the large-scale distributed systems, it was necessary to start researching the construction of load balancing algorithms which are able to manage these systems with efficiency. This efficiency is measured through the number of messages generated on the environment; the support to heterogeneous environments and the load balance policies which should spend the minimal resources time; the stability in overloaded situations; the system scalability; and the processes average response times, that should be small. With the aim to achieve the large-scale distributed systems requirements, this Ph.D. proposes, presents and evaluates a new load balancing algorithm named TLBA (Tree Load Balancing Algorithm). This algorithm arranges the computers on a logical network topology with a tree format. The load balancing operations are executed over this tree. To evaluate the TLBA algorithm, a simulator was built that was submitted to tests that confirmed the following characteristics: the small number of messages generated by the load balancing operations; the stability in overloaded situations; the small average processes response times. To validate the simulation results a TLBA prototype was implemented. This prototype confirmed the simulation results and consequently the contributions of the proposed algorithm.

Diversas políticas de escalonamento para aplicações paralelas voltadas a ambientes computacionais distribuídos têm sido propostas. Embora tais políticas apresentem bons resultados, elas são, geralmente, avaliadas em cenários específicos. Quando o cenário muda, com diferentes ambientes distribuídos e condições de carga, essas políticas podem ter seu desempenho deteriorado. Nesse contexto, este trabalho apresenta um estudo comparativo envolvendo dez políticas de escalonamento avaliadas em diferentes cenários. Cada uma das políticas foi submetida a uma combinação de quatro cargas de trabalho de ocupação da UCP e três variações da taxa de comunicação média entre os processos, utilizando a rede. Foram considerados ainda três sistemas distribuídos distintos: dois clusters, com diferentes quantidades de nós, e um grid computacional. Foi utilizada a simulação com ambientes próximos ao real e cargas de trabalho obtidas de modelos realísticos. Os resultados demonstraram que, embora as políticas sejam voltadas a ambientes computacionais paralelos e distribuídos, quando o cenário muda, o desempenho cai e a ordem de classificação entre as políticas se altera. Os resultados permitiram ainda demonstrar a necessidade de se considerar a comunicação entre os processos durante o escalonamento em grids computacionais. / Several scheduling policies for parallel applications directed to the distributed computational environments have been proposed. Although such policies present good results, they, generally, are evaluated in specific scenarios. When scenario change, by using different distributed environments and workload conditions, these policies can have its performance spoiled. In this context, this work presents a comparative study involving ten scheduling policies evaluated on different scenarios. Each policy was submitted to a combination of four CPU occupation workloads and three variations of interprocess average communication rates, using the network. Three different distributed systems had been yet considered: two clusters, with different amounts of nodes, and one grid computing. Simulation was used with environments near to the real and workloads obtained of realistic models. Although the policies are directed to parallel and distributed environments, the results have demonstrated that when scenario change, the performance falls and the ranking between the policies changes too. The results have still allowed to demonstrate the necessity of considering interprocess communication during the scheduling in a grid computing.

Protein-protein interaction networks provide important information about what the biological function of proteins whose roles are unknown might be in a cell. These interaction networks were analyzed by a variety of approaches by running them on a single computer and the roles of the proteins identified were used to predict the function of the proteins unidentified. The functional flow is an approach that takes the network connectivity, distance effect, topology of the network with local and global views into account. With these advantages, that the functional flow produces more accurate results on the prediction of protein functions was presented by the previos conducted researches. However, the application implemented for this approach could not be practically applied on the large and complex network produced for the complex species because of memory limitation. The purpose of this thesis is to provide a new application be implemented on the high computing performance where the application can be scaled on the large data sets. Therefore, Hadoop, one of the open source map/reduce environments, was installed on 18 hosts each of which has eight cores. Method / the first map/reduce job distributes the protein interaction network as a format which allows parallel distributed computing to all the worker nodes, the other map/reduce job generates flows for each known protein function and the role of the proteins unidentified are predicted by accumulating all of these generated flows. It has been observed in the experiments we performed that the application requiring high performance computing can be decomposed into worker nodes efficiently and the application can provide better performance as the resources increase.

The fact that loosely bonded DNA bases could tolerate large structural fluctuations, form a dissipative environment for a charge traveling through the DNA. Nonlinear stochastic nature of structural fluctuations facilitates rich charge dynamics in DNA. We study the complex charge dynamics by solving a nonlinear, stochastic, coupled system of differential equations. Charge transfer between donor and acceptor in DNA occurs via different mechanisms depending on the distance between donor and acceptor. It changes from tunneling regime to a polaron assisted hopping regime depending on the donor-acceptor separation. Also we found that charge transport strongly depends on the feasibility of polaron formation. Hence it has complex dependence on temperature and charge-vibrations coupling strength. Mismatched base pairs, such as different conformations of the G・A mispair, cause only minor structural changes in the host DNA molecule, thereby making mispair recognition an arduous task. Electron transport in DNA that depends strongly on the hopping transfer integrals between the nearest base pairs, which in turn are affected by the presence of a mispair, might be an attractive approach in this regard. I report here on our investigations, via the I –V characteristics, of the effect of a mispair on the electrical properties of homogeneous and generic DNA molecules. The I –V characteristics of DNA were studied numerically within the double-stranded tight-binding model. The parameters of the tight-binding model, such as the transfer integrals and on-site energies, are determined from first-principles calculations. The changes in electrical current through the DNA chain due to the presence of a mispair depend on the conformation of the G・A mispair and are appreciable for DNA consisting of up to 90 base pairs. For homogeneous DNA sequences the current through DNA is suppressed and the strongest suppression is realized for the G(anti)・A(syn) conformation of the G・A mispair. For inhomogeneous (generic) DNA molecules, the mispair result can be either suppression or an enhancement of the current, depending on the type of mispairs and actual DNA sequence.

Risk management is an essential tool for ensuring the safety and timeliness of maritime operations and transportation. Some of the many risk factors that can compromise the smooth operation of maritime activities include harsh weather and pirate activity. However, identifying and quantifying the extent of these risk factors for a particular vessel is not a trivial process. One challenge is that processing the vast amounts of automatic identification system (AIS) messages generated by the ships requires significant computational resources. Another is that the risk management process partially relies on human expertise, which can be timeconsuming and error-prone. In this thesis, an existing Risk Management Framework (RMF) is augmented to address these issues. A parallel/distributed version of the RMF is developed to e ciently process large volumes of AIS data and assess the risk levels of the corresponding vessels in near-real-time. A genetic fuzzy system is added to the RMF's Risk Assessment module in order to automatically learn the fuzzy rule base governing the risk assessment process, thereby reducing the reliance on human domain experts. A new weather risk feature is proposed, and an existing regional hostility feature is extended to automatically learn about pirate activity by ingesting unstructured news articles and incident reports. Finally, a geovisualization tool is developed to display the position and risk levels of ships at sea. Together, these contributions pave the way towards truly automatic risk management, a crucial component of modern maritime solutions. The outcomes of this thesis will contribute to enhance Larus Technologies' Total::Insight, a risk-aware decision support system successfully deployed in maritime scenarios.

Diversas políticas de escalonamento para aplicações paralelas voltadas a ambientes computacionais distribuídos têm sido propostas. Embora tais políticas apresentem bons resultados, elas são, geralmente, avaliadas em cenários específicos. Quando o cenário muda, com diferentes ambientes distribuídos e condições de carga, essas políticas podem ter seu desempenho deteriorado. Nesse contexto, este trabalho apresenta um estudo comparativo envolvendo dez políticas de escalonamento avaliadas em diferentes cenários. Cada uma das políticas foi submetida a uma combinação de quatro cargas de trabalho de ocupação da UCP e três variações da taxa de comunicação média entre os processos, utilizando a rede. Foram considerados ainda três sistemas distribuídos distintos: dois clusters, com diferentes quantidades de nós, e um grid computacional. Foi utilizada a simulação com ambientes próximos ao real e cargas de trabalho obtidas de modelos realísticos. Os resultados demonstraram que, embora as políticas sejam voltadas a ambientes computacionais paralelos e distribuídos, quando o cenário muda, o desempenho cai e a ordem de classificação entre as políticas se altera. Os resultados permitiram ainda demonstrar a necessidade de se considerar a comunicação entre os processos durante o escalonamento em grids computacionais. / Several scheduling policies for parallel applications directed to the distributed computational environments have been proposed. Although such policies present good results, they, generally, are evaluated in specific scenarios. When scenario change, by using different distributed environments and workload conditions, these policies can have its performance spoiled. In this context, this work presents a comparative study involving ten scheduling policies evaluated on different scenarios. Each policy was submitted to a combination of four CPU occupation workloads and three variations of interprocess average communication rates, using the network. Three different distributed systems had been yet considered: two clusters, with different amounts of nodes, and one grid computing. Simulation was used with environments near to the real and workloads obtained of realistic models. Although the policies are directed to parallel and distributed environments, the results have demonstrated that when scenario change, the performance falls and the ranking between the policies changes too. The results have still allowed to demonstrate the necessity of considering interprocess communication during the scheduling in a grid computing.

Algoritmos de balanceamento de carga são utilizados em sistemas distribuídos para homogeneizar a ocupação dos recursos computacionais disponíveis. A homogeneidade na ocupação do ambiente permite otimizar a alocação de recursos e, conseqüentemente, aumentar o desempenho das aplicações. Com o advento dos sistemas distribuídos de alta escala, fazem-se necessárias pesquisas para a construção de algoritmos de balanceamento de carga que sejam capazes de gerir com eficiência esses sistemas. Essa eficiência é medida através do número de mensagens geradas no ambiente, do suporte a ambientes heterogêneos, do uso de políticas que consomem poucos recursos do sistema, da estabilidade em alta carga, da escalabilidade do sistema e dos baixos tempos de resposta. Com o objetivo de atender as necessidades dos sistemas distribuídos de alta escala, este doutorado propõe, apresenta e avalia um novo algoritmo de balanceamento de carga denominado TLBA (Tree Load Balancing Algorithm). Esse algoritmo organiza os computadores do sistema em uma topologia lógica na forma de árvore, sobre a qual são executadas operações de balanceamento de carga. Para validar o TLBA foi construído um simulador que, submetido a testes, permitiu comprovar suas contribuições, que incluem: o baixo número de mensagens geradas pelas operações de balanceamento de carga; a estabilidade em altas cargas; os baixos tempos médios de resposta de processos. Para validar os resultados de simulação, foi construído um protótipo do TLBA. Esse protótipo confirmou os resultados de simulação e, conseqüentemente, as contribuições do algoritmo. / Load balancing algorithms are applied in distributed systems to homogenize the occupation of the available computational resources. The homogeneity of the environment occupation allows optimising the resource allocation and consequently, increasing the application performance. With the advent of the large-scale distributed systems, it was necessary to start researching the construction of load balancing algorithms which are able to manage these systems with efficiency. This efficiency is measured through the number of messages generated on the environment; the support to heterogeneous environments and the load balance policies which should spend the minimal resources time; the stability in overloaded situations; the system scalability; and the processes average response times, that should be small. With the aim to achieve the large-scale distributed systems requirements, this Ph.D. proposes, presents and evaluates a new load balancing algorithm named TLBA (Tree Load Balancing Algorithm). This algorithm arranges the computers on a logical network topology with a tree format. The load balancing operations are executed over this tree. To evaluate the TLBA algorithm, a simulator was built that was submitted to tests that confirmed the following characteristics: the small number of messages generated by the load balancing operations; the stability in overloaded situations; the small average processes response times. To validate the simulation results a TLBA prototype was implemented. This prototype confirmed the simulation results and consequently the contributions of the proposed algorithm.