Practical dynamic thermal management on Intel desktop computer

Fueled by increasing human appetite for high computing performance, semiconductor technology has now marched into the deep sub-micron era. As transistor size keeps shrinking, more and more transistors are integrated into a single chip. This has increased tremendously the power consumption and heat generation of IC chips. The rapidly growing heat dissipation greatly increases the packaging/cooling costs, and adversely affects the performance and reliability of a computing system. In addition, it also reduces the processor's life span and may even crash the entire computing system. Therefore, dynamic thermal management (DTM) is becoming a critical problem in modern computer system design. Extensive theoretical research has been conducted to study the DTM problem. However, most of them are based on theoretically idealized assumptions or simplified models. While these models and assumptions help to greatly simplify a complex problem and make it theoretically manageable, practical computer systems and applications must deal with many practical factors and details beyond these models or assumptions. The goal of our research was to develop a test platform that can be used to validate theoretical results on DTM under well-controlled conditions, to identify the limitations of existing theoretical results, and also to develop new and practical DTM techniques. This dissertation details the background and our research efforts in this endeavor. Specifically, in our research, we first developed a customized test platform based on an Intel desktop. We then tested a number of related theoretical works and examined their limitations under the practical hardware environment. With these limitations in mind, we developed a new reactive thermal management algorithm for single-core computing systems to optimize the throughput under a peak temperature constraint. We further extended our research to a multicore platform and developed an effective proactive DTM technique for throughput maximization on multicore processor based on task migration and dynamic voltage frequency scaling technique. The significance of our research lies in the fact that our research complements the current extensive theoretical research in dealing with increasingly critical thermal problems and enabling the continuous evolution of high performance computing systems.

Optimizing storage and memory systems for energy and performance

Electrical energy is an essential resource for the modern world. Unfortunately, its price has almost doubled in the last decade. Furthermore, energy production is also currently one of the primary sources of pollution. These concerns are becoming more important in data-centers. As more computational power is required to serve hundreds of millions of users, bigger data-centers are becoming necessary. This results in higher electrical energy consumption. Of all the energy used in data-centers, including power distribution units, lights, and cooling, computer hardware consumes as much as 80%. Consequently, there is opportunity to make data-centers more energy efficient by designing systems with lower energy footprint. Consuming less energy is critical not only in data-centers. It is also important in mobile devices where battery-based energy is a scarce resource. Reducing the energy consumption of these devices will allow them to last longer and re-charge less frequently. Saving energy in computer systems is a challenging problem. Improving a system's energy efficiency usually comes at the cost of compromises in other areas such as performance or reliability. In the case of secondary storage, for example, spinning-down the disks to save energy can incur high latencies if they are accessed while in this state. The challenge is to be able to increase the energy efficiency while keeping the system as reliable and responsive as before. This thesis tackles the problem of improving energy efficiency in existing systems while reducing the impact on performance. First, we propose a new technique to achieve fine grained energy proportionality in multi-disk systems; Second, we design and implement an energy-efficient cache system using flash memory that increases disk idleness to save energy; Finally, we identify and explore solutions for the page fetch-before-update problem in caching systems that can: (a) control better I/O traffic to secondary storage and (b) provide critical performance improvement for energy efficient systems.

Automating Hospitality Information: Network Technology and Systems Management

The local area network (LAN) interconnecting computer systems and soft- ware can make a significant contribution to the hospitality industry. The author discusses the advantages and disadvantages of such systems.

A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems

This paper is reviewing objective assessments of Parkinson’s disease(PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process.

User Behavioral Modeling of Web-based Systems for Continuous User Authentication

Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.

Detecting Danger: Applying a Novel Immunological Concept to Intrusion Detection Systems'

INTRODUCTION In recent years computer systems have become increasingly complex and consequently the challenge of protecting these systems has become increasingly difficult. Various techniques have been implemented to counteract the misuse of computer systems in the form of firewalls, antivirus software and intrusion detection systems. The complexity of networks and dynamic nature of computer systems leaves current methods with significant room for improvement. Computer scientists have recently drawn inspiration from mechanisms found in biological systems and, in the context of computer security, have focused on the human immune system (HIS). The human immune system provides an example of a robust, distributed system that provides a high level of protection from constant attacks. By examining the precise mechanisms of the human immune system, it is hoped the paradigm will improve the performance of real intrusion detection systems. This paper presents an introduction to recent developments in the field of immunology. It discusses the incorporation of a novel immunological paradigm, Danger Theory, and how this concept is inspiring artificial immune systems (AIS). Applications within the context of computer security are outlined drawing direct reference to the underlying principles of Danger Theory and finally, the current state of intrusion detection systems is discussed and improvements suggested.

Rule Generalisation in Intrusion Detection Systems using Snort

Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more necessary as reliance on Internet services increases and systems with sensitive data are more commonly open to Internet access. An IDS’s responsibility is to detect suspicious or unacceptable system and network activity and to alert a systems administrator to this activity. The majority of IDSs use a set of signatures that define what suspicious traffic is, and Snort is one popular and actively developing open-source IDS that uses such a set of signatures known as Snort rules. Our aim is to identify a way in which Snort could be developed further by generalising rules to identify novel attacks. In particular, we attempted to relax and vary the conditions and parameters of current Snort rules, using a similar approach to classic rule learning operators such as generalisation and specialisation. We demonstrate the effectiveness of our approach through experiments with standard datasets and show that we are able to detect previously undetected variants of various attacks. We conclude by discussing the general effectiveness and appropriateness of generalisation in Snort based IDS rule processing. Keywords: anomaly detection, intrusion detection, Snort, Snort rules

Detecting Danger: Applying a Novel Immunological Concept to Intrusion Detection Systems'

INTRODUCTION In recent years computer systems have become increasingly complex and consequently the challenge of protecting these systems has become increasingly difficult. Various techniques have been implemented to counteract the misuse of computer systems in the form of firewalls, antivirus software and intrusion detection systems. The complexity of networks and dynamic nature of computer systems leaves current methods with significant room for improvement. Computer scientists have recently drawn inspiration from mechanisms found in biological systems and, in the context of computer security, have focused on the human immune system (HIS). The human immune system provides an example of a robust, distributed system that provides a high level of protection from constant attacks. By examining the precise mechanisms of the human immune system, it is hoped the paradigm will improve the performance of real intrusion detection systems. This paper presents an introduction to recent developments in the field of immunology. It discusses the incorporation of a novel immunological paradigm, Danger Theory, and how this concept is inspiring artificial immune systems (AIS). Applications within the context of computer security are outlined drawing direct reference to the underlying principles of Danger Theory and finally, the current state of intrusion detection systems is discussed and improvements suggested.

Rule Generalisation in Intrusion Detection Systems using Snort

Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more necessary as reliance on Internet services increases and systems with sensitive data are more commonly open to Internet access. An IDS’s responsibility is to detect suspicious or unacceptable system and network activity and to alert a systems administrator to this activity. The majority of IDSs use a set of signatures that define what suspicious traffic is, and Snort is one popular and actively developing open-source IDS that uses such a set of signatures known as Snort rules. Our aim is to identify a way in which Snort could be developed further by generalising rules to identify novel attacks. In particular, we attempted to relax and vary the conditions and parameters of current Snort rules, using a similar approach to classic rule learning operators such as generalisation and specialisation. We demonstrate the effectiveness of our approach through experiments with standard datasets and show that we are able to detect previously undetected variants of various attacks. We conclude by discussing the general effectiveness and appropriateness of generalisation in Snort based IDS rule processing. Keywords: anomaly detection, intrusion detection, Snort, Snort rules

Aspectos motores da Paralisia Cerebral Espástica diparética : o Nintendo WII 'MARCA REGISTRADA' como atividade motora complementar = Motor aspects of Spastic Cerebral Palsy : the Nintendo WII 'TRADEMARK' as a supplementary motor activity

Universidade Estadual de Campinas . Faculdade de Educação Física

Rough Notes on Programming AVR Microcontrollers in C

These notes follow on from the material that you studied in CSSE1000 Introduction to Computer Systems. There you studied details of logic gates, binary numbers and instruction set architectures using the Atmel AVR microcontroller family as an example. In your present course (METR2800 Team Project I), you need to get on to designing and building an application which will include such a microcontroller. These notes focus on programming an AVR microcontroller in C and provide a number of example programs to illustrate the use of some of the AVR peripheral devices.

Work domain analysis for training-system definition and acquisition

Training-needs analysis is critical for defining and procuring effective training systems. However, traditional approaches to training-needs analysis are not suitable for capturing the demands of highly automated and computerized work domains. In this article, we propose that work domain analysis can identify the functional structure of a work domain that must be captured in a training system, so that workers can be trained to deal with unpredictable contingencies that cannot be handled by computer systems. To illustrate this argument, we outline a work domain analysis of a fighter aircraft that defines its functional structure in terms of its training objectives, measures of performance, basic training functions, physical functionality, and physical context. The functional structure or training needs identified by work domain analysis can then be used as a basis for developing functional specifications for training systems, specifically its design objectives, data collection capabilities, scenario generation capabilities, physical functionality, and physical attributes. Finally, work domain analysis also provides a useful framework for evaluating whether a tendered solution fulfills the training needs of a work domain.

Sistema de apoio à criminologia

Este trabalho consiste no desenvolvimento de um Sistema de Apoio à Criminologia – SAC, onde se pretende ajudar os detectives/analistas na prevenção proactiva da criminalidade e na gestão dos seus recursos materiais e humanos, bem como impulsionar estudos sobre a alta incidência de determinados tipos de crime numa dada região. Historicamente, a resolução de crimes tem sido uma prerrogativa da justiça penal e dos seus especialistas e, com o aumento da utilização de sistemas computacionais no sistema judicial para registar todos os dados que dizem respeito a ocorrências de crimes, dados de suspeitos e vítimas, registo criminal de indivíduos e outros dados que fluem dentro da organização, cresce a necessidade de transformar estes dados em informação proveitosa no combate à criminalidade. O SAC tira partido de técnicas de extracção de conhecimento de informação e aplica-as a um conjunto de dados de ocorrências de crimes numa dada região e espaço temporal, bem como a um conjunto de variáveis que influenciam a criminalidade, as quais foram estudadas e identificadas neste trabalho. Este trabalho é constituído por um modelo de extracção de conhecimento de informação e por uma aplicação que permite ao utilizador fornecer um conjunto de dados adequado, garantindo a máxima eficácia do modelo.

Ontologies for reusing data cleaning knowledge

The emergence of new business models, namely, the establishment of partnerships between organizations, the chance that companies have of adding existing data on the web, especially in the semantic web, to their information, led to the emphasis on some problems existing in databases, particularly related to data quality. Poor data can result in loss of competitiveness of the organizations holding these data, and may even lead to their disappearance, since many of their decision-making processes are based on these data. For this reason, data cleaning is essential. Current approaches to solve these problems are closely linked to database schemas and specific domains. In order that data cleaning can be used in different repositories, it is necessary for computer systems to understand these data, i.e., an associated semantic is needed. The solution presented in this paper includes the use of ontologies: (i) for the specification of data cleaning operations and, (ii) as a way of solving the semantic heterogeneity problems of data stored in different sources. With data cleaning operations defined at a conceptual level and existing mappings between domain ontologies and an ontology that results from a database, they may be instantiated and proposed to the expert/specialist to be executed over that database, thus enabling their interoperability.