Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

A Methodological Framework for Modeling Pavement Maintenance Costs for Projects with Performance-based Contracts

Performance-based maintenance contracts differ significantly from material and method-based contracts that have been traditionally used to maintain roads. Road agencies around the world have moved towards a performance-based contract approach because it offers several advantages like cost saving, better budgeting certainty, better customer satisfaction with better road services and conditions. Payments for the maintenance of road are explicitly linked to the contractor successfully meeting certain clearly defined minimum performance indicators in these contracts. Quantitative evaluation of the cost of performance-based contracts has several difficulties due to the complexity of the pavement deterioration process. Based on a probabilistic analysis of failures of achieving multiple performance criteria over the length of the contract period, an effort has been made to develop a model that is capable of estimating the cost of these performance-based contracts. One of the essential functions of such model is to predict performance of the pavement as accurately as possible. Prediction of future degradation of pavement is done using Markov Chain Process, which requires estimating transition probabilities from previous deterioration rate for similar pavements. Transition probabilities were derived using historical pavement condition rating data, both for predicting pavement deterioration when there is no maintenance, and for predicting pavement improvement when maintenance activities are performed. A methodological framework has been developed to estimate the cost of maintaining road based on multiple performance criteria such as crack, rut and, roughness. The application of the developed model has been demonstrated via a real case study of Miami Dade Expressways (MDX) using pavement condition rating data from Florida Department of Transportation (FDOT) for a typical performance-based asphalt pavement maintenance contract. Results indicated that the pavement performance model developed could predict the pavement deterioration quite accurately. Sensitivity analysis performed shows that the model is very responsive to even slight changes in pavement deterioration rate and performance constraints. It is expected that the use of this model will assist the highway agencies and contractors in arriving at a fair contract value for executing long term performance-based pavement maintenance works.

Modeling 2R regenerator based on High Nonlinear Dispersion Imbalanced Loop Mirror (HN-DILM)

The model of Reshaping and Re-amplification (2R) regenerator based on High Nonlinear Dispersion Imbalanced Loop Mirror (HN-DILM) has been designed to examine its capability to reduce the necessary of fiber loop length and input peak power by deploying High Non linear Fiber (HNLF) compared to Dispersion Shifted Fiber (DSF). The simulation results show by deployed a HNLF as a nonlinear element in Dispersion Imbalanced Loop Mirror (DILM) requires only 400mW peak powers to obtain a peak of transmission compared to DSF which requires a higher peak power at 2000mW to obtain a certain transmissivity. It also shows that HNLF required shorter fiber length to achieve the highest transmission. The 2R regenerator also increases the extinction ratio (ER) of the entire system. © 2010 IEEE.

A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.

A Collaborative Multiagent Framework based on Online Risk-Aware Planning and Decision-Making

Planning is an essential process in teams of multiple agents pursuing a common goal. When the effects of actions undertaken by agents are uncertain, evaluating the potential risk of such actions alongside their utility might lead to more rational decisions upon planning. This challenge has been recently tackled for single agent settings, yet domains with multiple agents that present diverse viewpoints towards risk still necessitate comprehensive decision making mechanisms that balance the utility and risk of actions. In this work, we propose a novel collaborative multi-agent planning framework that integrates (i) a team-level online planner under uncertainty that extends the classical UCT approximate algorithm, and (ii) a preference modeling and multicriteria group decision making approach that allows agents to find accepted and rational solutions for planning problems, predicated on the attitude each agent adopts towards risk. When utilised in risk-pervaded scenarios, the proposed framework can reduce the cost of reaching the common goal sought and increase effectiveness, before making collective decisions by appropriately balancing risk and utility of actions. 

Finite Element Modeling of Spiral Frequency Steerable Acoustic Transducers (FSATs) for guided waves based Structural Health Monitoring of plate-like structures

Structural Health Monitoring (SHM) is an emerging area of research associated to improvement of maintainability and the safety of aerospace, civil and mechanical infrastructures by means of monitoring and damage detection. Guided wave structural testing method is an approach for health monitoring of plate-like structures using smart material piezoelectric transducers. Among many kinds of transducers, the ones that have beam steering feature can perform more accurate surface interrogation. A frequency steerable acoustic transducer (FSATs) is capable of beam steering by varying the input frequency and consequently can detect and localize damage in structures. Guided wave inspection is typically performed through phased arrays which feature a large number of piezoelectric transducers, complexity and limitations. To overcome the weight penalty, the complex circuity and maintenance concern associated with wiring a large number of transducers, new FSATs are proposed that present inherent directional capabilities when generating and sensing elastic waves. The first generation of Spiral FSAT has two main limitations. First, waves are excited or sensed in one direction and in the opposite one (180 ̊ ambiguity) and second, just a relatively rude approximation of the desired directivity has been attained. Second generation of Spiral FSAT is proposed to overcome the first generation limitations. The importance of simulation tools becomes higher when a new idea is proposed and starts to be developed. The shaped transducer concept, especially the second generation of spiral FSAT is a novel idea in guided waves based of Structural Health Monitoring systems, hence finding a simulation tool is a necessity to develop various design aspects of this innovative transducer. In this work, the numerical simulation of the 1st and 2nd generations of Spiral FSAT has been conducted to prove the directional capability of excited guided waves through a plate-like structure.

On precise three-dimensional environment modeling via UAV-based photogrammetric systems

Abstract : Images acquired from unmanned aerial vehicles (UAVs) can provide data with unprecedented spatial and temporal resolution for three-dimensional (3D) modeling. Solutions developed for this purpose are mainly operating based on photogrammetry concepts, namely UAV-Photogrammetry Systems (UAV-PS). Such systems are used in applications where both geospatial and visual information of the environment is required. These applications include, but are not limited to, natural resource management such as precision agriculture, military and police-related services such as traffic-law enforcement, precision engineering such as infrastructure inspection, and health services such as epidemic emergency management. UAV-photogrammetry systems can be differentiated based on their spatial characteristics in terms of accuracy and resolution. That is some applications, such as precision engineering, require high-resolution and high-accuracy information of the environment (e.g. 3D modeling with less than one centimeter accuracy and resolution). In other applications, lower levels of accuracy might be sufficient, (e.g. wildlife management needing few decimeters of resolution). However, even in those applications, the specific characteristics of UAV-PSs should be well considered in the steps of both system development and application in order to yield satisfying results. In this regard, this thesis presents a comprehensive review of the applications of unmanned aerial imagery, where the objective was to determine the challenges that remote-sensing applications of UAV systems currently face. This review also allowed recognizing the specific characteristics and requirements of UAV-PSs, which are mostly ignored or not thoroughly assessed in recent studies. Accordingly, the focus of the first part of this thesis is on exploring the methodological and experimental aspects of implementing a UAV-PS. The developed system was extensively evaluated for precise modeling of an open-pit gravel mine and performing volumetric-change measurements. This application was selected for two main reasons. Firstly, this case study provided a challenging environment for 3D modeling, in terms of scale changes, terrain relief variations as well as structure and texture diversities. Secondly, open-pit-mine monitoring demands high levels of accuracy, which justifies our efforts to improve the developed UAV-PS to its maximum capacities. The hardware of the system consisted of an electric-powered helicopter, a high-resolution digital camera, and an inertial navigation system. The software of the system included the in-house programs specifically designed for camera calibration, platform calibration, system integration, onboard data acquisition, flight planning and ground control point (GCP) detection. The detailed features of the system are discussed in the thesis, and solutions are proposed in order to enhance the system and its photogrammetric outputs. The accuracy of the results was evaluated under various mapping conditions, including direct georeferencing and indirect georeferencing with different numbers, distributions and types of ground control points. Additionally, the effects of imaging configuration and network stability on modeling accuracy were assessed. The second part of this thesis concentrates on improving the techniques of sparse and dense reconstruction. The proposed solutions are alternatives to traditional aerial photogrammetry techniques, properly adapted to specific characteristics of unmanned, low-altitude imagery. Firstly, a method was developed for robust sparse matching and epipolar-geometry estimation. The main achievement of this method was its capacity to handle a very high percentage of outliers (errors among corresponding points) with remarkable computational efficiency (compared to the state-of-the-art techniques). Secondly, a block bundle adjustment (BBA) strategy was proposed based on the integration of intrinsic camera calibration parameters as pseudo-observations to Gauss-Helmert model. The principal advantage of this strategy was controlling the adverse effect of unstable imaging networks and noisy image observations on the accuracy of self-calibration. The sparse implementation of this strategy was also performed, which allowed its application to data sets containing a lot of tie points. Finally, the concepts of intrinsic curves were revisited for dense stereo matching. The proposed technique could achieve a high level of accuracy and efficiency by searching only through a small fraction of the whole disparity search space as well as internally handling occlusions and matching ambiguities. These photogrammetric solutions were extensively tested using synthetic data, close-range images and the images acquired from the gravel-pit mine. Achieving absolute 3D mapping accuracy of 11±7 mm illustrated the success of this system for high-precision modeling of the environment.

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.

Assessing the activity coefficients of water in cholinium-based ionic liquids: experimental measurements and COSMO-RS modeling

The vapor liquid-equilibrium of water + ionic liquids is relevant for a wide range of applications of these compounds. It is usually measured by ebulliometric techniques, but these are time consuming and expensive. In this work it is shown that the activity coefficients of water in a series of cholinium-based ionic liquids can be reliably and quickly estimated at 298.15K using a humidity meter instrument. The cholinium based ionic liquids were chosen to test this experimental methodology since data for water activities of quaternary ammonium salts are available in the literature allowing the validation of the proposed technique. The COSMO-RS method provides a reliable description of the data and was also used to understand the molecular interactions occurring on these binary systems. The estimated excess enthalpies indicate that hydrogen bonding between water and ionic liquid anion is the dominant interaction that governs the behavior of water and cholinium-based ionic liquids systems, while the electrostatic-misfit and van der Walls forces have a minor contribution to the total excess enthalpies.

Time cumulative complexity modeling and analysis for space-based networks

In this paper, the notion of the cumulative time varying graph (C-TVG) is proposed to model the high dynamics and relationships between ordered static graph sequences for space-based information networks (SBINs). In order to improve the performance of management and control of the SBIN, the complexity and social properties of the SBIN's high dynamic topology during a period of time is investigated based on the proposed C-TVG. Moreover, a cumulative topology generation algorithm is designed to establish the topology evolution of the SBIN, which supports the C-TVG based complexity analysis and reduces network congestions and collisions resulting from traditional link establishment mechanisms between satellites. Simulations test the social properties of the SBIN cumulative topology generated through the proposed C-TVG algorithm. Results indicate that through the C-TVG based analysis, more complexity properties of the SBIN can be revealed than the topology analysis without time cumulation. In addition, the application of attack on the SBIN is simulated, and results indicate the validity and effectiveness of the proposed C-TVG and C-TVG based complexity analysis for the SBIN.

Pattern-based analysis of EAI languages : the case of the business modelling language

Enterprise Application Integration (EAI) is a challenging area that is attracting growing attention from the software industry and the research community. A landscape of languages and techniques for EAI has emerged and is continuously being enriched with new proposals from different software vendors and coalitions. However, little or no effort has been dedicated to systematically evaluate and compare these languages and techniques. The work reported in this paper is a first step in this direction. It presents an in-depth analysis of a language, namely the Business Modeling Language, specifically developed for EAI. The framework used for this analysis is based on a number of workflow and communication patterns. This framework provides a basis for evaluating the advantages and drawbacks of EAI languages with respect to recurrent problems and situations.

Value-oriented process modeling : integrating financial perspectives into business process re-design

Purpose – Financial information about costs and return on investments are of key importance to strategic decision-making but also in the context of process improvement or business engineering. In this paper we propose a value-oriented approach to business process modeling based on key concepts and metrics from operations and financial management, to aid decision making in process re-design projects on the basis of process models. Design/methodology/approach – We suggest a theoretically founded extension to current process modeling approaches, and delineate a framework as well as methodical support to incorporate financial information into process re-design. We use two case studies to evaluate the suggested approach. Findings – Based on two case studies, we show that the value-oriented process modeling approach facilitates and improves managerial decision-making in the context of process re-design. Research limitations / implications – We present design work and two case studies. More research is needed to more thoroughly evaluate the presented approach in a variety of real-life process modeling settings. Practical implications – We show how our approach enables decision makers to make investment decisions in process re-design projects, and also how other decisions, for instance in the context of enterprise architecture design, can be facilitated. Originality/value – This study reports on an attempt to integrate financial considerations into the act of process modeling, in order to provide more comprehensive decision making support in process re-design projects.