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

Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN). Consequently, there is a growing need for an extensible simulation framework that can perform architectural explorations with SNNs, including both platform-based design of today's hardware, and hardware-software co-design and design-technology co-optimization of the future. We present NeuroXplorer, a fast and extensible framework that is based on a generalized template for modeling a neuromorphic architecture that can be infused with the specific details of a given hardware and/or technology. NeuroXplorer can perform both low-level cycle-accurate architectural simulations and high-level analysis with data-flow abstractions. NeuroXplorer's optimization engine can incorporate hardware-oriented metrics such as energy, throughput, and latency, as well as SNN-oriented metrics such as inter-spike interval distortion and spike disorder, which directly impact SNN performance. We demonstrate the architectural exploration capabilities of NeuroXplorer through case studies with many state-of-the-art machine learning models.

NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks

Neuromorphic computation is based on spike trains in which the location and frequency of spikes occurring within the network guide the execution. This paper develops a frame-work to monitor the correctness of a neuromorphic program’s execution using model-based redundancy in which a software-based monitor compares discrepancies between the behavior of neurons mapped to hardware and that predicted by a corresponding mathematical model in real time. Our approach reduces the hardware overhead needed to support the monitoring infrastructure and minimizes intrusion on the executing application. Fault-injection experiments utilizing CARLSim, a high-fidelity SNN simulator, show that the framework achieves high fault coverage using parsimonious models which can operate with low computational overhead in real time.

Online Performance Monitoring of Neuromorphic Computing Systems

An off-line scheduling algorithm considers resource, precedence, and synchronisation 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). Earlier we formulated a time slot based ILP model whose solutions maximise the temporal flexibility of the overall task schedule. In this paper, we propose an improved, interval based model, compare it to the former, and evaluate both on a set of random scenarios using two public domain ILP solvers and a proprietary SAT/ILP mixed solver.

/pdf/synthesising-robust-schedules-for-minimum-disruption-repair-4cr8c1tvb7.pdf

Synthesising robust schedules for minimum disruption repair using linear programming

Naloxone is a life-saving drug capable of reversing a fatal opioid overdose. Although this drug has existed for over 50 years, opioid overdose-related deaths have consistently risen and surpassed 120,000 globally in 2021. Opioids induce respiratory depression by activating $\mu\text{-opioid}$ receptors at specific sites in the central nervous system. This results in overdose deaths caused by slow and shallow breathing, also known as opioid-induced respiratory depression. 1.6 million individuals suffer from opioid use disorder annually, making them at high risk of overdose, primarily due to the increasing prevalence of Fentanyl. Over 52% of these deaths occur when the individual is alone. Immediate response to an overdose by delivering naloxone can save the individual's life. To solve this problem, we developed a closed-loop sensor-driven auto-injector that can determine a fatal overdose and inject naloxone. 76% of this population is willing to wear such a device on the shoulder, a canonical injection site. This paper presents the DOVE, a shoulder-based opioid overdose detection and reversal device. It noninvasively measures the subject's motion state and changes in blood oxygen levels (SpO2) along with the respiration state. These biomarkers are measured from the shoulder using an optical sensor and accelerometer to determine if a fatal overdose occurred. We evaluated our DOVE device against an FDA-cleared commercial pulse oximeter by inducing apneic events as they have very similar SpO2 trends to an overdose. Results show that SpO2 can be measured on the shoulder across different skin tones with an accuracy of 96.8% and a high Pearson correlation of 0.766 (p < 0.0001).

Dove: Shoulder-Based Opioid Overdose Detection and Reversal Device

Performance monitoring of datacenters provides vital information for dynamic resource provisioning, anomaly detection, capacity planning, and metering decisions. Online monitoring, however, incurs a variety of costs: the very act of monitoring a system interferes with its performance, consuming network bandwidth and disk space. With the goal of reducing these costs, we develop and validate a strategy based on exploiting the underlying structure of the signal being monitored to sparsify it prior to transmission to a monitoring station for analysis and logging. Specifically, predictive models are designed to estimate the signals of interest. These models are then used to obtain prediction errors -- the error between the signal and the corresponding estimate -- that are then treated as a sparse representation of the original signal while retaining key information. This transformation allows for far less data to be transmitted to the monitoring station, at which point the signal is reconstructed by simply using the prediction errors. We show that classical techniques such as principal component analysis (PCA) can be applied to the reconstructed signal for anomaly detection. Experimental results using the Trade6 and RuBBoS benchmarks indicate a significant reduction in overall transmission costs -- greater that 95% in some cases -- while retaining sufficient detection accuracy.

Nagarajan Kandasamy

Papers

NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks

Online Performance Monitoring of Neuromorphic Computing Systems

Synthesising robust schedules for minimum disruption repair using linear programming

Dove: Shoulder-Based Opioid Overdose Detection and Reversal Device

Efficient online performance monitoring of computing systems using predictive models