Resource Modification On Multicore Server With Kernel Bypass

Technology develops very fast marked by many innovations both from hardware and software. Multicore servers with a growing number of cores require efficient software. Kernel and Hardware used to handle various operational needs have some limitations. This limitation is due to the high level of complexity especially in handling as a server such as single socket discriptor, single IRQ and lack of pooling so that it requires some modifications. The Kernel Bypass is one of the methods to overcome the deficiencies of the kernel. Modifications on this server are a combination increase throughput and decrease server latency. Modifications at the driver level with hashing rx signal and multiple receives modification with multiple ip receivers, multiple thread receivers and multiple port listener used to increase throughput. Modifications using pooling principles at either the kernel level or the program level are used to decrease the latency. This combination of modifications makes the server more reliable with an average throughput increase of 250.44% and a decrease in latency 65.83%

Bayesian Dynamic Modeling and Monitoring of Network Flows

In the context of a motivating study of dynamic network flow data on a large-scale e-commerce web site, we develop Bayesian models for on-line/sequential analysis for monitoring and adapting to changes reflected in node-node traffic. For large-scale networks, we customize core Bayesian time series analysis methods using dynamic generalized linear models (DGLMs). These are integrated into the context of multivariate networks using the concept of decouple/recouple that was recently introduced in multivariate time series. This method enables flexible dynamic modeling of flows on large-scale networks and exploitation of partial parallelization of analysis while maintaining coherence with an over-arching multivariate dynamic flow model. This approach is anchored in a case-study on internet data, with flows of visitors to a commercial news web site defining a long time series of node-node counts on over 56,000 node pairs. Central questions include characterizing inherent stochasticity in traffic patterns, understanding node-node interactions, adapting to dynamic changes in flows and allowing for sensitive monitoring to flag anomalies. The methodology of dynamic network DGLMs applies to many dynamic network flow studies.Comment: 34 pages, 24 figure

Microscopy Techniques for Investigating Interactions in Microbial Systems

Biological interactions occur on multiple length scales, ranging from molecular to population wide interactions. This work describes the study of two specific areas of biological interactions in microbial systems: intracellular protein-protein interactions and cell-to-cell interactions. The implementation of optical and atomic force microscopy and the methodologies developed during this study proved to be invaluable tools for investigating these systems. Identifying and characterizing protein interactions are fundamental steps toward understanding complex cellular networks. We have developed a unique methodology which combines an imaging-based protein interaction assay with a fluorescence recovery after photobleaching technique (FRAP). Protein interactions are readily detected by co-localization of two proteins of interest fused to green fluorescent protein (GFP) and DivIVA, a cell division protein from Bacillus subtilis. We demonstrate that the modified co-localization assay is sensitive enough to detect protein interactions over four orders of magnitude. FRAP data was analyzed using a combination of various image processing techniques and analytical models. This combined approach made it possible to estimate cell morphology parameters such as length, diameter, the effective laser probe volume, as well as to the mobile protein concentration in vivo, the number of bound molecules at the cellular poles, and the biophysical parameter koff. Cells not only utilize molecular interactions in the intracellular environment, but also express proteins, polysaccharides and other complex molecules to mediate interactions with the surrounding extracellular environment. In Azospirillum brasilense, cell surface properties, including exopolysaccharide production, are thought to play a direct role in promoting cell-to-cell interactions. Recently, the Che1 chemotaxis-like pathway from A. brasilense was shown to modulate flocculation, suggesting an associated modulation of cell surface properties. Using atomic force microscopy, distinct changes in the surface morphology of flocculating A. brasilense Che1 mutant strains were detected. Further analyses suggest that the extracellular matrix differs between the cheA1 and the cheY1 deletion mutants, despite similarity in the macroscopic floc structures. Collectively, these data indicate that disruption of the Che1 pathway is correlated with distinctive changes in the extracellular matrix, which likely result from changes in surface polysaccharides structure and/or composition

Chemical Kinetics

Chemical Kinetics relates to the rates of chemical reactions and factors such as concentration and temperature, which affects the rates of chemical reactions. Such studies are important in providing essential evidence as to the mechanisms of chemical processes. The book is designed to help the reader, particularly students and researchers of physical science, understand the chemical kinetics mechanics and chemical reactions. The selection of topics addressed and the examples, tables and graphs used to illustrate them are governed, to a large extent, by the fact that this book is aimed primarily at physical science (mainly chemistry) technologists. Undoubtedly, this book contains "must read" materials for students, engineers, and researchers working in the chemistry and chemical kinetics area. This book provides valuable insight into the mechanisms and chemical reactions. It is written in concise, self-explanatory and informative manner by a world class scientists in the field

Proceedings, MSVSCC 2014

Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia

Deep Model for Improved Operator Function State Assessment

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation

Performance Evaluation of Network Anomaly Detection Systems

Nowadays, there is a huge and growing concern about security in information and communication technology (ICT) among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. Attacks, problems, and internal failures when not detected early may badly harm an entire Network system. Thus, this thesis presents an autonomous profile-based anomaly detection system based on the statistical method Principal Component Analysis (PCADS-AD). This approach creates a network profile called Digital Signature of Network Segment using Flow Analysis (DSNSF) that denotes the predicted normal behavior of a network traffic activity through historical data analysis. That digital signature is used as a threshold for volume anomaly detection to detect disparities in the normal traffic trend. The proposed system uses seven traffic flow attributes: Bits, Packets and Number of Flows to detect problems, and Source and Destination IP addresses and Ports, to provides the network administrator necessary information to solve them. Via evaluation techniques, addition of a different anomaly detection approach, and comparisons to other methods performed in this thesis using real network traffic data, results showed good traffic prediction by the DSNSF and encouraging false alarm generation and detection accuracy on the detection schema. The observed results seek to contribute to the advance of the state of the art in methods and strategies for anomaly detection that aim to surpass some challenges that emerge from the constant growth in complexity, speed and size of today’s large scale networks, also providing high-value results for a better detection in real time.Atualmente, existe uma enorme e crescente preocupação com segurança em tecnologia da informação e comunicação (TIC) entre a comunidade científica. Isto porque qualquer ataque ou anomalia na rede pode afetar a qualidade, interoperabilidade, disponibilidade, e integridade em muitos domínios, como segurança nacional, armazenamento de dados privados, bem-estar social, questões econômicas, e assim por diante. Portanto, a deteção de anomalias é uma ampla área de pesquisa, e muitas técnicas e abordagens diferentes para esse propósito surgiram ao longo dos anos. Ataques, problemas e falhas internas quando não detetados precocemente podem prejudicar gravemente todo um sistema de rede. Assim, esta Tese apresenta um sistema autônomo de deteção de anomalias baseado em perfil utilizando o método estatístico Análise de Componentes Principais (PCADS-AD). Essa abordagem cria um perfil de rede chamado Assinatura Digital do Segmento de Rede usando Análise de Fluxos (DSNSF) que denota o comportamento normal previsto de uma atividade de tráfego de rede por meio da análise de dados históricos. Essa assinatura digital é utilizada como um limiar para deteção de anomalia de volume e identificar disparidades na tendência de tráfego normal. O sistema proposto utiliza sete atributos de fluxo de tráfego: bits, pacotes e número de fluxos para detetar problemas, além de endereços IP e portas de origem e destino para fornecer ao administrador de rede as informações necessárias para resolvê-los. Por meio da utilização de métricas de avaliação, do acrescimento de uma abordagem de deteção distinta da proposta principal e comparações com outros métodos realizados nesta tese usando dados reais de tráfego de rede, os resultados mostraram boas previsões de tráfego pelo DSNSF e resultados encorajadores quanto a geração de alarmes falsos e precisão de deteção. Com os resultados observados nesta tese, este trabalho de doutoramento busca contribuir para o avanço do estado da arte em métodos e estratégias de deteção de anomalias, visando superar alguns desafios que emergem do constante crescimento em complexidade, velocidade e tamanho das redes de grande porte da atualidade, proporcionando também alta performance. Ainda, a baixa complexidade e agilidade do sistema proposto contribuem para que possa ser aplicado a deteção em tempo real

Feasibility Design of a Continuous Insulin Sensor from Lessons Learned using Glucose Sensors, and Point of Care Insulin Sensors

abstract: Glucose sensors have had many paradigm shifts, beginning with using urine, to point of care blood, now being approved for implant. This review covers various aspects of the sensors, ranging from the types of surface chemistry, and electron transduction. All the way to the algorithms, and filters used to alter and understand the signal being transduced. Focus is given to Dr. Hellerâ’s work using redox mediators, as well as Dr. Sode in his advances for direct electron transfer. Simple process of designing sensors are described, as well as the possible errors that may come with glucose sensor use. Finally, a small window into the future trends of glucose sensors is described both from a device view point, as well as organic viewpoint. Using this history the initial point of care sensor for insulin published through LaBelle’s lab is reevaluated critically. In addition, the modeling of the possibility of continuously measuring insulin is researched. To better understand the design for a continuous glucose sensor, the basic kinetic model is set up, and ran through a design of experiments to then optimized what the binding kinetics for an ideal insulin molecular recognition element would be. In addition, the phenomena of two electrochemical impedance spectroscopy peaks is analyzed, and two theories are suggests, and demonstrated to a modest level.Dissertation/ThesisMasters Thesis Biomedical Engineering 201