ANN for Predicting Medical Expenses

Abstract: In this research, the Artificial Neural Network (ANN) model was developed and tested to predict the rate of treatment expenditure on an individual or family in a country. A number of factors have been identified that may affect treatment expenses. Factors such as age, grade level such as primary, preparatory, secondary or college, sex, size of disability, social status, and annual medical expenses in fixed dollars excluding dental and outpatient clinics among others, as input variables for the ANN model. A model based on the multi-layer Perceptron topology was developed and trained using data on 5574 cases. The evaluation of the test data shows that the ANN model is capable of predicting correctly Medical Expenses

Roméo et Juliette, April 21, 2011

This is the concert program of the Roméo et Juliette by Charles Gounod performance on Thursday - Sunday, April 21 - 14, 2011 at 8:00 p.m., at the Boston University Theater, 264 Huntington Avenue, Bosotn, Massachusetts. Digitization for Boston University Concert Programs was supported by the Boston University Center for the Humanities Library Endowed Fund

Two Analytical Models for Evaluating Performance of Gigabit Ethernet Hosts with Finite Buffer

Two analytical models are developed to study the impact of interrupt overhead on operating system performance of network hosts with limited-size or finite buffer. Under heavy network traffic such as that of Gigabit Ethernet, the system performance will be negatively affected due to interrupt overhead caused by incoming traffic. In particular, packet loss, excessive latency and significant degradation in system throughput can be experienced. Also, user applications may livelock as the CPU power is mostly consumed by interrupt handling and protocol processing. In this paper, we present and compare two analytical models that capture host behavior and evaluate its performance. The first model is based on Markov processes and queueing theory, while the second, which is more accurate but more complex, is a pure Markov process. The models yield equations for a number of important system performance metrics. These performance metrics include throughput, latency, packet loss, stability condition, CPU utilizations of interrupt handling and protocol processing, and CPU availability for user applications. Both models yield closed-form solutions and equations that are either mathematically equivalent or very closely matching. Our analysis yields insight into understanding and predicting the impact of system and network choices on the performance of interrupt-driven systems when subjected to light and heavy network loads. More importantly, our analytical work can also be valuable in improving host performance. The paper gives guidelines and recommendations to address design and implementation issues. Simulation and reported experimental results show that our analytical models are valid and give a good approximation

Throughput-Delay Analysis of Interrupt-Driven Kernels with DMA Enabled and Disabled in High-Speed Networks

Interrupt processing can be a major bottleneck in the end-to-end performance of high-speed networks. The performance of Gigabit network end hosts or servers can be severely degraded due to interrupt overhead caused by heavy incoming traffic. Under heavy network traffic, the system performance will be negatively affected due to interrupt overhead caused by the incoming traffic. In particular, excessive latency and significant degradation in system throughput can be experienced. In this paper, we present a throughput-delay analysis of such behavior. We develop analytical models based on queueing theory and Markov processes. In our analysis, we consider and model three systems: ideal, PIO, and DMA. In ideal system, the interrupt overhead is ignored. In PIO, DMA is disabled and copying of incoming packets is performed by the CPU. In DMA, copying of incoming packet is performed by DMA engines. For high-speed network hosts, both PIO and DMA can be desirable configuration options. The analysis yields insight into understanding and predicting the impact of system and network choices on the performance of interrupt-driven systems when subjected to light and heavy network loads. Simulations and reported experimental results show that our analytical models are valid and give a good approximation

Two Analytical Models (with Infinite Buffer) for Evaluating Performance of Gigabit Ethernet Hosts

Two analytical models are developed to study the impact of interrupt overhead on operating system performance of network hosts when subjected to Gigabit network traffic. Under heavy network traffic, the system performance will be negatively affected due to interrupt overhead caused by incoming traffic. In particular, excessive latency and significant degradation in system throughput can be experienced. Also, user applications may livelock as the CPU power is mostly consumed by interrupt handling and protocol processing. In this paper, we present and compare two analytical models that capture host behavior and evaluate its performance. The first model is based on Markov processes and queueing theory, while the second, which is more accurate but more complex, is a pure Markov process. For the most part both models give mathematically-equivalent closed-form solutions for a number of important system performance metrics. These metrics include throughput, latency, stability condition, CPU utilizations of interrupt handling and protocol processing, and CPU availability for user applications. The analysis yields insight into understanding and predicting the impact of system and network choices on the performance of interrupt-driven systems when subjected to light and heavy network loads. More importantly, our analytical work can also be valuable in improving host performance. The paper gives guidelines and recommendations to address design and implementation issues. Simulation and reported experimental results show that our analytical models are valid and give a good approximation

Performance Evaluation of Interrupt-Driven Kernels in Gigabit Networks

Abstract—The paper presents models and analytical techniques for studying system behavior of an interrupt-driven kernel due to high packet arrival rate found in gigabit networks. An analytical study is presented describing the impact of high interrupt rate on system performance. The performance is studied in terms of throughput, latency, and system power. Equations are derived for system throughput, latency, power, and stability condition. Results from both reported experimental findings and simulations show that our analytical model is valid and give a good approximation. To the best of authors' knowledge, the impact of interrupts on system performance had never been studied analytically in the past, and this analytical work is the first of its kind

Two Analytical Models for Evaluating Performance of Gigabit Ethernet Hosts with Finite Buffer

Two analytical models are developed to study the impact of interrupt overhead on operating system performance of network hosts with limited-size or finite buffer. Under heavy network traffic such as that of Gigabit Ethernet, the system performance will be negatively affected due to interrupt overhead caused by incoming traffic. In particular, packet loss, excessive latency and significant degradation in system throughput can be experienced. Also, user applications may livelock as the CPU power is mostly consumed by interrupt handling and protocol processing. In this paper, we present and compare two analytical models that capture host behavior and evaluate its performance. The first model is based on Markov processes and queueing theory, while the second, which is more accurate but more complex, is a pure Markov process. The models yield equations for a number of important system performance metrics. These performance metrics include throughput, latency, packet loss, stability condition, CPU utilizations of interrupt handling and protocol processing, and CPU availability for user applications. Both models yield closed-form solutions and equations that are either mathematically equivalent or very closely matching. Our analysis yields insight into understanding and predicting the impact of system and network choices on the performance of interrupt-driven systems when subjected to light and heavy network loads. More importantly, our analytical work can also be valuable in improving host performance. The paper gives guidelines and recommendations to address design and implementation issues. Simulation and reported experimental results show that our analytical models are valid and give a good approximation

An Analytic Approach for Deploying Desktop Videoconferencing

The deployment of desktop videoconferencing, also known as Video and Voice over IP (VVoIP), over existing IP networks is gaining popularity these days. Such a deployment has become a major and challenging task for data network researchers and designers. This paper presents an analytic approach for deploying videoconferencing. The approach utilizes queueing network analysis and investigates two key performance bounds for videoconferencing: delay and bandwidth. The approach can be used to assess the support and readiness of an existing IP network. Prior to the purchase and deployment of desktop videoconferencing equipment, the approach predicts the number of videoconferencing sessions or calls that can be sustained by an existing network while satisfying QoS requirements of all network services and leaving adequate capacity for future growth. As a case study, we apply our approach to a typical network of a small enterprise. In addition, we use OPNET network simulator to verify and validate our analysis. Results obtained from analysis and simulation are in line and give a close match

An Analytic Approach for Deploying Desktop Videoconferencing

The deployment of desktop videoconferencing, also known as Video and Voice over IP (VVoIP), over existing IP networks is gaining popularity these days. Such a deployment has become a major and challenging task for data network researchers and designers. This paper presents an analytic approach for deploying videoconferencing. The approach utilizes queueing network analysis and investigates two key performance bounds for videoconferencing: delay and bandwidth. The approach can be used to assess the support and readiness of an existing IP network. Prior to the purchase and deployment of desktop videoconferencing equipment, the approach predicts the number of videoconferencing sessions or calls that can be sustained by an existing network while satisfying QoS requirements of all network services and leaving adequate capacity for future growth. As a case study, we apply our approach to a typical network of a small enterprise. In addition, we use OPNET network simulator to verify and validate our analysis. Results obtained from analysis and simulation are in line and give a close match