In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

https://beloglazov.info/papers/2011-energy-aware-fgcs.pdf

Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

An iterative algorithm, based on recent work in compressive sensing, is developed for volume image reconstruction from a circular cone-beam scan. The algorithm minimizes the total variation (TV) of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. The constraints are enforced by the use of projection onto convex sets (POCS) and the TV objective is minimized by steepest descent with an adaptive step-size. The algorithm is referred to as adaptive-steepest-descent-POCS (ASD-POCS). It appears to be robust against cone-beam artifacts, and may be particularly useful when the angular range is limited or when the angular sampling rate is low. The ASD-POCS algorithm is tested with the Defrise disk and jaw computerized phantoms. Some comparisons are performed with the POCS and expectation-maximization (EM) algorithms. Although the algorithm is presented in the context of circular cone-beam image reconstruction, it can also be applied to scanning geometries involving other x-ray source trajectories.

/pdf/image-reconstruction-in-circular-cone-beam-computed-4wndbkpv6w.pdf

Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization

The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allows Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy-performance trade-off, as aggressive consolidation may lead to performance degradation. Because of the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct a competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the service level agreement. We validate the high efficiency of the proposed algorithms by extensive simulations using real-world workload traces from more than a thousand PlanetLab VMs. Copyright © 2011 John Wiley & Sons, Ltd.

Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers

Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-a properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

/pdf/self-adaptive-software-landscape-and-research-challenges-4rt6vhdmq2.pdf

Self-adaptive software: Landscape and research challenges

An off-line scheduling algorithm considers resource, precedence, and synchronization requirements of a task graph, and generates a schedule guaranteeing its timing requirements. This schedule must, however, be executed in a dynamic and unpredictable operating environment where resources may fail and tasks may execute longer than expected. To accommodate such execution uncertainties, this paper addresses the synthesis of robust task schedules using a slack-based approach and proposes a solution using integer linear programming (ILP). An ILP model, whose solution maximizes the temporal flexibility of the overall task schedule, is formulated. Two different ILP solvers are used to solve this model and their performance compared. For large task graphs, an efficient approximate method is presented and its performance evaluated.

/pdf/synthesis-of-robust-task-schedules-for-minimum-disruption-224zo1wyft.pdf

Synthesis of robust task schedules for minimum disruption repair

This experience report describes the design and implementation of safety-critical software and hardware for respiratory gating of a medical linear accelerator. Respiratory gating refers to a radiotherapy technique for treating cancer in the lung, liver, and abdomen, where tumors move while a patient breathes. A computer software program tracks the position of the tumor within the human body using x-ray fluoroscopy. When the tumor is in the correct position, the linear accelerator is triggered, delivering a beam of radiation toward the target. As part of the gating system, a comprehensive strategy for safety has been developed. This paper describes these safety features, focusing on the online monitoring techniques used to confirm the proper operation of the fluoroscopic imaging panels and the pattern recognition algorithms used for tumor identification.

A Dependable System Architecture for Safety-Critical Respiratory-Gated Radiation Therapy

In this paper, a metric based on mathematical modeling is proposed to evaluate the strength in security of a logic-locked circuit against a satisfiability (SAT) based attack. Current approaches estimate the SAT resilience experimentally based on time-to-solve or the number of calls to a SAT-solver. However, the estimate is often based on one sample or a small sample size. Due to the possible variation in the search path length of the SAT-attack, a measure of resilience based on statistical characterization is proposed. A probabilistic model of a SAT-attack search process is developed to properly capture the variation in the path length and report the SAT resilience as an expectation of the computational complexity. An estimator of the expected complexity, assuming an equally likely branching probability, is proposed. The model and the estimator allow for 1) the derivation of a closed-form estimate of the expected security, and 2) characterization of the key search space without experimental bias toward SAT-attack implementation or circuit topology. As a case study, an analysis of the security gain per inserted key gate is performed on a full adder circuit. The study reveals a monotonically increasing resilience and provides insights on the most efficient key gate placement strategy that maximizes the achievable security.

Modeling SAT-Attack Search Complexity

This paper describes a step by step approach in designing wireless physical layer modules starting from a software implementation in MATLAB to a hardware implementation using Xilinx SysGen and ModelSim. The described design flow promotes baseband physical layer research by providing high flexibility and speed to the process of module creation verification and deployment. The novelty introduced into our system lies within the flexible components created using this design flow, which enables on-the-fly modification of multiple parameters to suit various wireless protocols.

/pdf/rapid-prototyping-of-wireless-physical-layer-modules-using-53hyye5o6d.pdf

Rapid Prototyping of Wireless Physical Layer Modules Using Flexible Software/Hardware Design Flow

Online performance monitoring of computer systems incurs a variety of costs: the very act of monitoring a system interferes with its performance and if the information is transmitted to a monitoring station for analysis and logging, this consumes network bandwidth and disk space. Compressive sampling-based schemes can help reduce these costs on the local machine by acquiring data directly from the system in a compressed form, and in a computationally efficient way. This paper focuses on reducing the computational cost associated with recovering the original signal from the transmitted sample set at the monitoring station for anomaly detection. Towards this end, we show that the compressed samples preserve, in an approximate form, properties such as mean, variance, as well as correlation between data points in the original full-length signal. We then use this result to detect changes in the original signal that could be indicative of an underlying anomaly such as abrupt changes in magnitude and gradual trends without the need to recover the full-length data. We illustrate the usefulness of our approach via case studies involving IBM's Trade Performance Benchmark using signals from the disk and memory subsystems. Experiments indicate that abrupt changes can be detected using a compressed sample size of 25% with a hit rate of 95% for a fixed false alarm rate of 5%; trends can be detected within a confidence interval of 95% using a sample size of only 6%.

http://tingshanhuang.com/content/ISSRE2015.pdf

Nagarajan Kandasamy

Papers

Synthesis of robust task schedules for minimum disruption repair

A Dependable System Architecture for Safety-Critical Respiratory-Gated Radiation Therapy

Modeling SAT-Attack Search Complexity

Rapid Prototyping of Wireless Physical Layer Modules Using Flexible Software/Hardware Design Flow

Anomaly detection in computer systems using compressed measurements