PREDICTING INTERNET TRAFFIC BURSTS USING EXTREME VALUE THEORY

Computer networks play an important role in today’s organization and people life. These interconnected devices share a common medium and they tend to compete for it. Quality of Service (QoS) comes into play as to define what level of services users get. Accurately defining the QoS metrics is thus important. Bursts and serious deteriorations are omnipresent in Internet and considered as an important aspects of it. This thesis examines bursts and serious deteriorations in Internet traffic and applies Extreme Value Theory (EVT) to their prediction and modelling. EVT itself is a field of statistics that has been in application in fields like hydrology and finance, with only a recent introduction to the field of telecommunications. Model fitting is based on real traces from Belcore laboratory along with some simulated traces based on fractional Gaussian noise and linear fractional alpha stable motion. QoS traces from University of Napoli are also used in the prediction stage. Three methods from EVT are successfully used for the bursts prediction problem. They are Block Maxima (BM) method, Peaks Over Threshold (POT) method, and RLargest Order Statistics (RLOS) method. Bursts in internet traffic are predicted using the above three methods. A clear methodology was developed for the bursts prediction problem. New metrics for QoS are suggested based on Return Level and Return Period. Thus, robust QoS metrics can be defined. In turn, a superior QoS will be obtained that would support mission critical applications

PREDICTING INTERNET TRAFFIC BURSTS USING EXTREME VALUE THEORY

Computer networks play an important role in today’s organization and people life. These interconnected devices share a common medium and they tend to compete for it. Quality of Service (QoS) comes into play as to define what level of services users get. Accurately defining the QoS metrics is thus important. Bursts and serious deteriorations are omnipresent in Internet and considered as an important aspects of it. This thesis examines bursts and serious deteriorations in Internet traffic and applies Extreme Value Theory (EVT) to their prediction and modelling. EVT itself is a field of statistics that has been in application in fields like hydrology and finance, with only a recent introduction to the field of telecommunications. Model fitting is based on real traces from Belcore laboratory along with some simulated traces based on fractional Gaussian noise and linear fractional alpha stable motion. QoS traces from University of Napoli are also used in the prediction stage. Three methods from EVT are successfully used for the bursts prediction problem. They are Block Maxima (BM) method, Peaks Over Threshold (POT) method, and RLargest Order Statistics (RLOS) method. Bursts in internet traffic are predicted using the above three methods. A clear methodology was developed for the bursts prediction problem. New metrics for QoS are suggested based on Return Level and Return Period. Thus, robust QoS metrics can be defined. In turn, a superior QoS will be obtained that would support mission critical applications

Fast Economic Development Accelerates Biological Invasions in China

Increasing levels of global trade and intercontinental travel have been cited as the major causes of biological invasion. However, indirect factors such as economic development that affect the intensity of invasion have not been quantitatively explored. Herein, using principal factor analysis, we investigated the relationship between biological invasion and economic development together with climatic information for China from the 1970s to present. We demonstrate that the increase in biological invasion is coincident with the rapid economic development that has occurred in China over the past three decades. The results indicate that the geographic prevalence of invasive species varies substantially on the provincial scale, but can be surprisingly well predicted using the combination of economic development (R2 = 0.378) and climatic factors (R2 = 0.347). Economic factors are proven to be at least equal to if not more determinant of the occurrence of invasive species than climatic factors. International travel and trade are shown to have played a less significant role in accounting for the intensity of biological invasion in China. Our results demonstrate that more attention should be paid to economic factors to improve the understanding, prediction and management of biological invasions

Sustainable Assessment in Supply Chain and Infrastructure Management

In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management

Evaluating the Need to Seal Thermal Cracks in Alaska’s Asphalt Concrete Pavements

INE/AUTC 12.2

New Model for Bridge Management System (BMS): Bridge Repair Priority Ranking System (BRPRS), Case Based Reasoning for Bridge Deterioration, Cost Optimization, and Preservation Strategy

Most public transportation agencies (Such as, state department of transportations (DOTs) and department of public works for cities and towns.) in the United States are constantly pursuing ways to improve bridge asset management to optimize their use of limited available funds for rehabilitation, replacement, and preventive maintenance. Given the realities of available funding, there is a significant difference between available funds and funds required for maintaining bridges in good condition. The proper preventative maintenance and treatments should be performed at the right time to be cost effective and extend the life of bridges. Neglecting maintenance can cause higher future costs and further deteriorate the conditions that will increase the risk of bridge closure. This would require complete or partial replacement as well as additional funds needed for detours and traffic control which interrupts services to the motorist and creates more congestion. Development and implementation of a Bridge Management System (BMS) provide states and municipalities with a tool to help identify maintenance repair, prioritize bridge rehabilitation and replacement, develop preservation strategies, and allocate available funds accordingly. The primary objective of this research is to develop a Bridge Management System (BMS) to manage municipal and state bridge assets. Complete, accurate data in well-designed form is vital to a Bridge Management System (BMS). This system will make available work reports, engineering drawings, photographs, and a forecasting model for management staff use. Inventory and condition data are extracted from the U.S. Federal Highway Administration (FHWA) and National Bridge Inventory System (NBIS) coding guidelines. The proposed model provides: (1) A priority ranking system for Rehabilitation and Replacement projects, which enables the decision-makers to understand and compare the overall state of all the bridges in the network. It embraces seven factors condition, criticality, risk, functionally, bridge type, age, and size. (2) A deterioration model that uses optimized case-based reasoning (CBR) method. A similarity measure of classification is developed to identify how close the characteristics of bridge components are to each other based on a scoring system. (3) A cost model that considers different repair strategies and provide bridge repair recommendations with estimated cost repairs. (4)The model feeds data to a forecasting program that prepares 120-year preservation, maintenance, repair and rehabilitation budgets and schedules to sustain a bridge network at the highest performance level under approved budgets. The forecasting option contains default management costs that are upgraded as work report data yields costs based on locality and individual bridge projects. BMS will give accessibility through linkages to all available municipal, and DOT, bridge data in the state. The data will be available through ArcGIS on tablets, laptops, and smartphones with access to cloud storage

Development of a Life-Cycle Cost Analysis Tool for Improved Maintenance and Management of Bridges

The Moving Ahead for Progress in the 21st Century Act (MAP-21) of 2012 requires states to develop and implement a transportation asset management plan (TAMP) for their respective portions of the National Highway System (NHS). Life-cycle cost and risk management analyses must be included in a state’s TAMP. As defined in the 1998 Transportation Equity Act for the 21st Century (TEA-21), life-cycle cost analysis (LCCA) is “a process for evaluating the total economic worth of a usable project segment by analyzing initial costs and discounted future costs, such as maintenance, user costs, and reconstruction, rehabilitation, restoring, and resurfacing costs, over the life of the project segment.” The main objective of this research project was to develop a LCCA tool for Iowa’s bridges based on survival analysis of condition ratings. This tool was designed to cover the most common types of bridges in Iowa while integrating historical data from maintenance crews, contractors, and past inspections into the predictive models that account for the costs of maintenance and repair during a bridge’s service life. The tool developed in this project provides a user friendly way to evaluate and compare maintenance costs for bridge decks over the lifetime of a bridge. With this information, transportation investment decisions can be made in consideration of all of the maintenance costs incurred during the period over which the maintenance alternatives are being compared

An Enhanced Bridge Weigh-in-motion Methodology and A Bayesian Framework for Predicting Extreme Traffic Load Effects of Bridges

In the past few decades, the rapid growth of traffic volume and weight, and the aging of transportation infrastructures have raised serious concerns over transportation safety. Under these circumstances, vehicle overweight enforcement and bridge condition assessment through structural health monitoring (SHM) have become critical to the protection of the safety of the public and transportation infrastructures. The main objectives of this dissertation are to: (1) develop an enhanced bridge weigh-in-motion (BWIM) methodology that can be integrated into the SHM system for overweight enforcement and monitoring traffic loading; (2) present a Bayesian framework to predict the extreme traffic load effects (LEs) of bridges and assess the implication of the growing traffic on bridge safety. Firstly, an enhanced BWIM methodology is developed. A comprehensive review on the BWIM technology is first presented. Then, a novel axle detection method using wavelet transformation of the bridge global response is proposed. Simulation results demonstrate that the proposed axle detection method can accurately identify vehicle axles, except for cases with rough road surface profiles or relatively high measurement noises. Furthermore, a two-dimensional nothing-on-road (NOR) BWIM algorithm that is able to identify the transverse position (TP) and axle weight of vehicles using only weighing sensors is proposed. Results from numerical and experimental studies show that the proposed algorithm can accurately identify the vehicle’s TP under various conditions and significantly improve the identification accuracy of vehicle weight compared with the traditional Moses’s algorithm. Secondly, a Bayesian framework for predicting extreme traffic LEs of bridges is presented. The Bayesian method offers a natural framework for uncertainty quantification in parameter estimation and thus can provide more reliable predictions compared with conventional methods. A framework for bridge condition assessment that utilizes the predicted traffic LEs is proposed and a case study on the condition assessment of an instrumented field bridge is presented to demonstrate the proposed methodology. Moreover, the non-stationary Bayesian method is adopted to predict the maximum traffic LEs during the lifetime of bridges subject to different types of traffic growth and the influence of the traffic growth on the bridge safety is investigated

Reliability-based assessment procedures for existing concrete structures

A feasibility study of reliability theory as a tool for the assessment of present safety and residual service life of damaged concrete structures has been performed in order to find a transparent methodology for the assessment procedure. It is concluded that the current guidelines are open to interpretation and that the variation in the results obtained regarding the structural safety is too great to be acceptable. Interpretations by the engineer are also included when deterministic methods are used, but probabilistic methods are more sensitive to the assumptions made and the differences in the results will therefore be greater. In a literature survey it is concluded that residual service life predictions should not be expected to be valid for more than 10 to 15 years, due to the large variability of the variables involved in the analysis. Based on these conclusions predictive models that are suitable for the inclusion of new data, and methods for the incorporation of new data are proposed. Information from the field of medical statistics and robotics suggests that linear regression models are well suited for this type of updated monitoring. Two test cases were studied, a concrete dam and a railway bridge. From the dam case, it was concluded that the safety philosophy in the deterministic dam specific assessment guidelines further development. Probabilistic descriptions of important variables, such as ice loads and friction coefficients, are needed if reliability theory is to be used for assessment purposes. During the study of the railway bridge it became clear that model uncertainties for different failure mechanisms used in concrete design are lacking. If Bayesian updating is to be used as a tool for incorporation of test data regarding concrete strength info the reliability analysis, a priori information must be established. A need for a probabilistic description of the hardening process of concrete was identified for the purpose of establishing a priori information. This description can also be used as qualitative assessment of the concrete. If there is a large discrepancy between the predicted value and the measured value, the concrete should be investigated regarding deterioration due to, for example internal frost or alkali silica reactions. Reliability theory is well suited for the assessment process since features of the reliability theory such as sensitivity analysis give good decision support for matters concerning both safety and service life predictions

Surgical trial in traumatic intracerebral haemorrhage (STITCH) : A randomised controlled trial of early surgery compared with Initial conservative treatment

Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme.Peer reviewedPublisher PD