Robust and sustainable schedulability analysis of embedded software

For real-time systems, most of the analysis involves efficient or exact schedulability checking. While this is important, analysis is often based on the assumption that the task parameters such as execution requirements and inter-arrival times between jobs are known exactly. In most cases, however, only a worst-case estimate of these quantities is available at the time of analysis. It is therefore imperative that schedulability analysis hold for better parameter values ( Sustainable Analysis ). On the other hand, if the task or system parameters turn out to be worse off, then the analysis should tolerate some deterioration ( Robust Analysis ). Robust analysis is especially important, because the implication of task schedulability is often weakened in the presence of optimizations that are performed on its code, or dynamic system parameters. In this work, we define and address sustainability and robustness questions for analysis of embedded real-time software that is modeled by conditional real-time tasks. Specifically, we show that, while the analysis is sustainable for changes in the task such as lower job execution times and increased relative deadlines, it is not the case for code changes such as job splitting and reordering. We discuss the impact of these results in the context of common compiler optimizations, and then develop robust schedulability techniques for operations where the original analysis is not sustainable.

BackgroundA number of studies have previously demonstrated that “goodness of fit” is insufficient in reliably classifying the credibility of a biological model. Robustness and/or sensitivity analysis is commonly employed as a secondary method for evaluating the suitability of a particular model. The results of such analyses invariably depend on the particular parameter set tested, yet many parameter values for biological models are uncertain.ResultsHere, we propose a novel robustness analysis that aims to determine the “common robustness” of the model with multiple, biologically plausible parameter sets, rather than the local robustness for a particular parameter set. Our method is applied to two published models of the Arabidopsis circadian clock (the one-loop [1] and two-loop [2] models). The results reinforce current findings suggesting the greater reliability of the two-loop model and pinpoint the crucial role of TOC1 in the circadian network.ConclusionsConsistent Robustness Analysis can indicate both the relative plausibility of different models and also the critical components and processes controlling each model.

The generalized multiframe task model (GMF) extends the sporadic task model and multiframe task model. Each frame in the GMF model contains an execution time, a relative deadline, and a minimum inter-arrival time. These parameters are fixed after task specification time in the GMF model. However, multimedia and adaptive control systems may be overloaded and no longer stabilized when the task parameters in such systems are not flexible. In order to address this problem, deadlines and periods of frames may change to alleviate temporal overload, e.g., in the parameter adaptation and elastic scheduling model. In this paper, we propose a new model GMF-PA (the GMF model with parameter adaptation). This model allows task parameters to be flexible in arbitrary-deadline systems. A necessary schedulability test based on mixed-integer linear programming is given to check the schedulability under EDF scheduling and optimally assign frame deadlines and periods at the same time. We also prove that the test is a sufficient and necessary schedulability test when frame deadlines and periods must be integers. An approximation algorithm is also deployed to reduce computational running time and indicates a sufficient schedulability test in general. The speed-up factor of our approximation algorithm is $$1+\epsilon $$ where $$\epsilon $$ can be arbitrarily small, with respect to the exact schedulability test of GMF-PA tasks under EDF. We also apply the GMF model to self-suspending tasks. By extending recent work on scheduling self-suspending tasks, we remove the assumption that frame deadlines are equally assigned in self-suspending tasks, and the system is extended from constrained-deadline systems to arbitrary-deadline systems. We have done extensive experiments to show that the schedulability ratio is improved using our techniques in our GMF-PA model.

Patients with schizophrenia exhibit impairments in working memory that often appear in attenuated form in persons at high risk for the illness. The authors hypothesized that deviations in task-related brain activation and deactivation would occur in persons with an at-risk mental state performing a working memory task that entailed the maintenance and manipulation of letters. Participants at ultra high risk for developing psychosis (N=60), identified using the Comprehensive Assessment of At-Risk Mental States, and healthy comparison subjects (N=38) 14 to 29 years of age underwent functional MRI while performing a verbal working memory task. Group differences in brain activation were identified using analysis of covariance. The two groups did not show significant differences in speed or accuracy of performance, even after accounting for differences in education. Irrespective of task condition, at-risk participants exhibited significantly less activation than healthy comparison subjects in the left anterior insula. During letter manipulation, at-risk persons exhibited greater task-related deactivation within the default-mode network than comparison subjects. Region-of-interest analysis in the at-risk group revealed significantly greater right dorsolateral prefrontal cortex activation during manipulation of letters. Despite comparable behavioral performance, at-risk participants performing a verbal working memory task exhibited altered brain activation compared with healthy subjects. These findings demonstrate an altered pattern of brain activation in at-risk persons that contains elements of reduced function as well as compensation.

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types. This imposes significant constraints on experimental design, limiting their application to the narrow set of imaging methods for which they were designed. One of the approaches to address these limitations is pattern recognition, which was originally developed for remote sensing, and is increasingly being applied to the biology domain. This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. The generality of this approach promises to enable data mining in extensive image repositories, and provide objective and quantitative imaging assays for routine use. Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. We list available software tools that can be used by biologists and suggest practical experimental considerations to make the best use of pattern recognition techniques for imaging assays.

In this paper, the calcium system in a neuron is a highly non-linear system. It could be delineated by unknown bounded model that is a subject of robustness analysis. We view a neural cell as a biological system and glutamate as a kind of chemical effect that disturbs the calcium homeostasis of the neural cell. Then we use robustness analysis to study the stable robustness of Ca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> in neurons after disturbed by glutamate in five concentrations. So the conclusion is that calcium system in a neuron is a dynamic system and its stable robustness reflects the persistence of structure, state and behavior of the system. If the deviation from the steady-state caused by larger perturbations is small enough, the stable robustness of the system is strong. Otherwise, it is weak.

The generalized multiframe task model (GMF) extends the sporadic task model and multiframe task model. Each frame in the GMF model contains an execution time, a relative deadline, and a minimum inter-arrival time. These parameters are fixed after task specification time in the GMF model. However, multimedia and adaptive control systems may be overloaded and no longer stabilized when the task parameters in such systems are not flexible. In order to address this problem, deadlines and periods may change to alleviate temporal overload, for example in the parameter adaption and elastic scheduling model. In this paper, we propose a new model GMF-PA (the GMF model with parameter adaption). This model allows task parameters to be flexible in arbitrary-deadline systems. A necessary schedulability test based on mixed-integer linear programming (MILP) is given to check the schedulability under EDF scheduling and optimally assign deadlines and periods at the same time. We also prove that the test is a sufficient and necessary schedulability test when task parameters must be integers. An approximation algorithm is also deployed to reduce computational running time. The speed-up factor of our approximation algorithm is 1+a#x0395; where a#x0395; can be arbitrarily small, with respect to the exact schedulability test of GMF-PA tasks under EDF. We also apply the GMF model to self-suspending tasks. By extending recent work on scheduling self-suspending tasks, we remove the assumption that deadlines are equally assigned in self-suspending tasks, and the system is extended from constrained-deadline systems to arbitrary-deadline systems. We have done exhaustive experiments to show that the schedulability ratio is improved using our techniques in our GMF-PA model.

Consider constrained-deadline sporadic tasks scheduled on a multiprocessor where (i) each task is characterized by its execution requirement, deadline, and minimum inter-arrival time, (ii) each task generates a sequence of jobs, (iii) the execution requirement of a job and its potential for parallel execution is described by one or many stages with a stage having one or many segments and different segments in the same stage can execute in parallel and a segment is only allowed to start execution if all segments of previous stages have finished execution, and (iv) there is contention for shared resources in the memory system (cache eviction, reordering in memory controller, memory bus contention). We present an algorithm that (i) performs schedulability testing for tasks scheduled with global-Earliest-Deadline-First (gEDF), (ii) configures the virtual-to-physical address translation so that a cache block fetched to the last-level cache by one task cannot be evicted by another task, (iii) configures the virtual-to-physical address translation to attempt to eliminate the extra execution time caused by the reordering effect in the memory controller and if this is not possible, then the reordering effect is considered in the schedulability analysis, and (iv) considers the effect of contention for the memory bus. Our solution is based on formulating this problem as a Mixed-Integer Linear Program (MILP). We have implemented a tool based on this theory and validated its output against measurements on a real computer.

Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of model parameters leads to epistemic uncertainty. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. However, the underlying models exclusively capture aleatoric but not epistemic uncertainty, and thus require that model parameters are known precisely. Our contribution to overcoming this restriction is a novel abstraction-based controller synthesis method for continuous-state models with stochastic noise and uncertain parameters. By sampling techniques and robust analysis, we capture both aleatoric and epistemic uncertainty, with a user-specified confidence level, in the transition probability intervals of a so-called interval Markov decision process (iMDP). We synthesize an optimal policy on this iMDP, which translates (with the specified confidence level) to a feedback controller for the continuous model with the same performance guarantees. Our experimental benchmarks confirm that accounting for epistemic uncertainty leads to controllers that are more robust against variations in parameter values.

The direct adaptive regulation of affine in the control nonlinear square (system states equals to control inputs) dynamical systems with modeling error effects, is considered in this paper. The method is based on a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Dynamical Systems (FDS) operating in conjunction with High Order Neural Network Functions, which in the sequel approximate the fuzzy rules. This way the unknown plant is modeled by a fuzzy-recurrent high order neural network (F-RHONN), which is of known structure considering the neglected nonlinearities. The development is combined with a sensitivity analysis of the closed loop in the presence of modeling imperfections and provides a comprehensive and rigorous analysis of the stability properties of the closed loop system. The existence and boundness of the control signal is always assured by introducing a novel method of parameter hopping and incorporating it in weight updating law. Simulations illustrate the potency of the method and its applicability is tested on well known benchmarks where it is shown that our approach is superior to the case of simple RHONN's.

Foreword

Advances in sensor deployment and computational modeling have allowed significant strides to be made recently in the field of Structural Health Monitoring (SHM). One widely used SHM technique is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability and unknown values for model parameters. Not accounting for uncertainty in the analysis can lead to false-positives or false-negatives in the assessment of the structural condition. To manage the aforementioned uncertainty, we propose a robust-SHM methodology that combines three technologies. A time series algorithm is trained using "baseline" data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate "size" of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes in time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM.

A robust image analysis technique for the study of horizontal air-water plug flow

Concerns a Delta-operator approach to approximation of an analog system. It has several advantages over the q-operator approximation, including convergence of digital results and models to their continuous counterparts as the sampling rate is increased, so that a uniform set of techniques for the stability and control can be obtained for both analog and discrete-time systems. The Delta-operator formulated systems, which in the limit approach their analog counterparts, seem to be suitable for a straight forward robust stability analysis of digital systems, for the available analog stability techniques may be easily extended to a Delta-operator formulated digital model. In this paper, a review of the Delta-operator formulation of an analog system, and its stability and robust stability analysis techniques are presented. Moreover, some of the existing analog robust stability techniques are evaluated for extension to Delta-operator formulated digital systems.

Robust probabilistic predictable feature analysis and its application for dynamic process monitoring