From Boolean to quantitative notions of correctness

In scheduling algorithms based on the Rate Monotonic (RM) method which are widely used in development of real-time systems, tasks with shorter periods have higher priorities. In contrast, ones with longer periods are likely to suffer from increased response times and jitter due to their lower priorities. We propose an Execution Right Delegation (ERD) method based on RM where a high-priority server for particular (or important) task is introduced to shorten response time and jitter of the task. In the evaluation, it is confirmed that response times and jitter of a particular (important) task are reduced. We also show Response Time Analysis (RTA), which assures worst-case response time of the task. This paper shows the algorithm and RTA of ERD and evaluates it by comparing it to a Deadline Monotonic method. The evaluation by simulation shows that ERD can reduce the average worst-case response time by 13.45% at maximum compared to the Deadline Monotonic scheduling. In addition, we confirm that the RTA provides a worst-case response time close to the simulation results.

A novel asynchronous data-path design is described. Circuits for the asynchronous adder unit, asynchronous comparator unit, and asynchronous selector unit are proposed. An asynchronous data-path ACS (add-compare-select) for a Viterbi decoder is formed. The performance of the 4-bit ACS is analyzed with a novel method based on a multi-delay model. The results of performance analysis of the asynchronous 4-bit ACS show that the average case response time of 6.81 ns is only 84% of the worst-case response time, 8.08 ns. It reveals that the asynchronous data-path has some performance advantages over the synchronous one.

A novel asynchronous ACS (Add-Compare-Select) is described. The circuit of a novel asynchronous comparator unit is proposed. The performance of ACS is analyzed with the novel method based on multi-delay model. The results of performance analysis of asynchronous ACS show that the average case response time 3.66ns is only 45% the worst-case response time 8.1ns. It reveals that the asynchronous ACS has some performance advantages than the synchronous one.

A number of methods have been proposed for the response-time analysis (RTA) of controller area networks (CANs), which are the industry standard for in-vehicle network protocols. However, most of these methods are unsuitable for analyzing nonperiodically transmitted messages because they assume a constant transmission period for each message. Moreover, the calculated evaluation criteria are limited and comprise many over-pessimistic estimations, such as the worst-case response time (WCRT). To tackle these problems, this paper proposes an RTA method for CANs with randomly occurring messages, which replaces the constant transmission period with the average number of transmission instances per unit time. This approach finds the approximate probability distribution of the response time to any message in a CAN, providing a more general measure than the WCRT. To this end, a mathematical model based on M/G/1 queueing theory is developed. The model accurately emulates the behavior of nonperiodic CAN messages and enables RTA with minimal pessimistic estimations. When tested against the benchmarks, the proposed method accurately obtained response time distributions within several seconds to several tens of seconds.

Traditional methods rely on Static Timing Analysis techniques to compute the Worst Case Response Time for tasks in real-time systems. Multi-Core real-time systems are faced up with concurrent task executions, semaphore accesses, and task migrations where it may be difficult to obtain the worst case upper bound. A new three staged probabilistic estimation concept is presented. Worst Case Response Times are estimated for tasksets which consist of tasks with multiple time bases. The concept involves data generation with sample classification and sample size equalization, model fit and Worst Case Response Time estimation on the basis of extreme value distribution models. A Generalized Pareto Distribution model fit method which includes threshold detection and parameter estimation is also presented. Sample classification in combination with the new Generalized Pareto Distribution model fit method allows to estimate Worst Case Response Times with low pessimism ranges compared to estimation methods that uses the Generalized Pareto or the Gumbel max distribution without sample classification.

Statistical modeling of emergency medical services’ response and rescue times to road traffic crashes in the Kingdom of Saudi Arabia

The use of process data, such as response time (RT) in psychometrics, has generally focused on the relationship between speed and accuracy. The potential relationships between RT and item discrimination remain less explored. In this study, we propose a model for simultaneously estimating the relationships between RT and item discrimination at the person, item, and person-by-item (residual) levels and illustrate our approach through an item-level meta-analysis of 40 empirical data sets comprising 1.84 million item responses. We find no evidence of average differences in item discrimination between items of different time intensity or persons of different average RT, while residual RT strongly and negatively predicts item discrimination (pooled coef. = -.27% per 1% difference in RT, SE = .04, = .17). While heterogeneity is high, we find little evidence of moderation by overall data set characteristics. Flexible generalized additive models show that the relationship between residual RT and item discrimination is generally curvilinear, with discrimination maximized just below average RT and minimized at the extremes. Our results suggest that RT data can provide insights into the measurement properties of educational and psychological assessments, but that the relationships between RT and item discrimination are highly variable.

Priority-based Functional Reactive Programming (P-FRP) is a new variant of FRP to model reactive applications in real-time systems. In P-FRP, when the currently running task is preempted by an arriving higher-priority task, the lower-priority running task is aborted and the higher-priority task will execute. The lower-priority task restarts when the higher-priority one completes. However, unlike the preemptive model, when a task aborts, all the changes made by this task are discarded. That is to say, when an aborted ask restarts, it should execute from the beginning. In order to provide a realistic Worst-Case Response Time (WCRT) of the tasks in P-FRP, it is therefore mandatory to derive a realistic Worst Case Execution Time (WCET) of each task. Previous studies have ignored memory latency in the derivation of the WCRT, making the resulting estimate inaccurate and unrealistic. Furthermore, these studies have also assumed that the WCET of each task is known a priori. In this paper, we introduce a scratchpad memory (SPM)-based platform for executing P-FRP tasks and an approach to determine the WCET of the tasks by considering the memory cost of the aborted tasks. We first compute the WCET of a task in a P-FRP system, and then calculate the memory penalty caused by preemption. In the next step, we derive the WCRT of the task sets in P-FRP by considering memory latency in the proposed platform. Experimental results from the derivations of the WCET and WCRT using task sets from the SNU real-time benchmarks and randomly generated tasks are presented to validate this approach.

<p><b>The client-centric multi-cloud has become a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud providers to achieve high-quality services with lower operation costs and higher application resilience. From the perspective of application providers, the location of cloud resources for application deployment significantly impacts the deployment cost and performance of applications, e.g., request response time. This gives rise to the problem of location-aware application deployment in multi-cloud to select suitable cloud resources from widely distributed multi-cloud data centers to balance the cost and performance. Existing research works did not pay full attention to the key impact of the location for application deployment. Therefore, it is urgent to study the problem both theoretically and in practice. In this thesis, innovative optimization methods and machine learning techniques are proposed for three common scenarios, namely composite application deployment, application replication and deployment, and elastic application deployment.</b></p> <p>First, this thesis studies the composite application deployment problem with the goal to minimize the average response time of composite applications subject to a budget constraint. We propose a Hybrid Genetic Algorithm (GA)-based approach, i.e., H-GA, for solving the problem with an extremely large search space. H-GA features a newly designed and domain-tailored service clustering algorithm, repair algorithm, solution representation, population initialization, and genetic operators. Experiments show that H-GA can outperform significantly several state-of-the-art approaches, achieving up to about 8% performance improvement in terms of response time, and 100% budget satisfaction in the meantime.</p> <p>Second, this thesis studies the application replication and deployment problem with the goal to minimize the total deployment cost of all application replicas subject to a stringent requirement on average response time. We propose two approaches under different optimization frameworks to solve the problem. With user requests dispatched to the closest application replicas, we develop an approach under a GA framework for Application Replication and Deployment (ARD), i.e., GA-ARD. GAARD features problem-specific solution representation, fitness measurement, and population initialization, which are effective to optimize the deployment of application replicas in multi-cloud. The experiments show that GA-ARD outperforms common application replication and placement strategies in the industry. With user requests flexibly dispatched among different application replicas, we develop another approach under a two-stage optimization framework, i.e., MCApp. MCApp can optimize both replica deployment and request dispatching by combining mixed-integer linear programming with domain-tailored large neighborhood search. Our experiments show that MCApp can achieve up to 25% reduction in total deployment cost compared with several recently developed approaches.</p> <p>Third, this thesis studies the elastic application deployment problem to minimize the deployment cost over a time span such as a billing day while satisfying the constraint on average response time. The goal of adapting resources for application deployment in response to dynamic and distributed workloads motivates us to adopt deep reinforcement learning (DRL) techniques. The proposed approach, namely DeepScale, applies a deep Q-network (DQN) to capture the optimal scaling policy that can perform online resource scaling. DeepScale also includes a long short-term memory-based prediction model to allow the DQN to consider predicted future requests while making cost-effective scaling decisions. Besides, we design a penalty-based reward function and a safety-aware action executor to ensure that any scaling decisions made by DRL can satisfy the response time constraint. The experiments show that DeepScale can significantly reduce the deployment cost of applications compared with the state-of-the-art baselines, including Amazon auto-scaling service and recently proposed RL-based algorithms. In the meanwhile, DeepScale can effectively satisfy the constraint on the average response time.</p> <p>In summary, this thesis studies three new problems for location-aware application deployment in multi-cloud. We propose four novel approaches under different optimization and machine learning frameworks, i.e., HGA, GA-ARD, MCApp, and DeepScale, for solving these problems. New constraint handling techniques are developed to satisfy the practical deployment requirements of enterprise applications.</p>

Despite the potential of quantum computing for drastically accelerating suitable real-time applications, response time analysis is still required to guarantee that quantum programs running on quantum computers satisfy application-specific timing requirements. This paper describes an inaugural project to determine whether a quantum program running on a quantum computer satisfies the timing constraints of a realtime application, that is, is quantum computing punctual and reliable in this time-sensitive application domain? Can this timing guarantee be formally verified? We leverage the existing work on the functional reactive programming (FRP) model to predict the worst-case response time (WCRT) of fault-tolerant classical computing systems since the timing analysis of re-executions for fault recovery plus transient-faults-induced wasted execution times is similar to determining the response time of FRP tasks. Ongoing work shows that accounting for wasted execution times due to errors in quantum computers resulting from quantum decoherence and state fidelity can be treated similarly and develops a mapping from quantum programs to FRP programs for efficient timing analysis.

The Controller Area Network (CAN) is widely employed in automotive control system networks. In the last few years, the amount of data has been increasing rapidly in these networks. With the purpose of improving CAN bandwidth efficiency, scheduling and analysis are considered to be particularly important. As an effective method, it has been known that assigning offsets to the CAN messages can reduce their worst case response time (WCRT). Meanwhile, the fact is that many commercial CAN controllers have been equipped with a priority or first-in-first-out (FIFO) queue to transmit messages to the CAN bus. However, previous researches for WCRT analysis of CAN messages either assumed a priority queue or did not consider the offset. For this reason, in this paper we propose a WCRT analysis method for CAN messages with assigned offset in the FIFO queue. We first present a critical instant theorem, then we propose two algorithms for WCRT calculation based on the given theorem. Experimental results on generated message sets and a real message set have validated the effectiveness of the proposed algorithms.

With the surge in data volume and dimensions, existing decision-making systems are difficult to capture nonlinear relationships and cope with the complexity and diversity of data, which leads to decision lag. To solve this problem, this article studies an intelligent decision-making system. The system integrates multi-layer feedforward neural networks and decision tree algorithms, and designs a distributed computing architecture based on the Apache Spark platform. Through the integration of the big data platform, the system integrates and processes multiple data sources; then, a three-layer feedforward neural network model is adopted, and a specific mechanism is used to prevent overfitting. After that, the decision tree algorithm is used to deeply explore the complex relationships in the data; Apache Spark is applied to accelerate the calculation process and efficiently query and analyze the stored data. The system also supports online learning strategies to continuously update the model to adapt to new data changes. The research results show that in the field of financial risk analysis, the average accuracy of the intelligent decision-making system in this article is 92.29 %, and the average response time is only 0.013 seconds; the average accuracy and response time of the traditional system are 82.54 % and 0.034 seconds respectively; the system in this article is significantly better than the traditional system. According to the study, an intelligent decision-making system not only increases decision-making's efficiency and accuracy but also offers solid base for more intelligent decisions in the future.

The detection of early-warning signals of performance deterioration can help technical support teams in taking swift remedial actions, thus ensuring rigor in production support operations of large scale software systems. Performance anomalies or deterioration, if left unattended, often result in system slowness and unavailability. In this paper, we presents a simple, intuitive and low-overhead technique for recognizing the early warning signs in near real time before they impact the system The technique is based on the inverse relationship which exists between throughput and average response time in a closed system. Because of this relationship, a significant increase in the average system response time causes an abrupt fall in system throughput. To identify such occurrences automatically, Individuals and Moving Range (XmR) control charts are used. We also provide a case study from a real-world production system, in which the technique has been successfully used. The use of this technique has reduced the occurrence of performance related incidents significantly in our daily operations. The technique is tool agnostic and can also be easily implemented in popular system monitoring tools by building custom extensions.

In recent years, the complexity of real-time embedded systems has increased dramatically. For those modern real-time systems, the limitations of original static Response Time Analysis (RTA) become more and more conspicuous. Most static analysis methods not only require much detailed system information, but also only target to some specific system model with non-realistic assumptions. As a result, those methods may produce overly pessimistic results, making them unsuitable to be applied on a complex industrial system. The best system model may be the system itself. Therefore, statistical RTA, which can produce probabilistic analysis results based on samples provided by real systems or simulators, may become more expedient. Statistical RTA usually requires more relaxed assumptions and less system information than static RTA. In this paper, we present an Extreme Value Theory (EVT) based method to compute Worst-Case Response Time (WCRT) targeting complex real-time systems. In the evaluation phase, we have applied this method to the calculation of worst-case transmission delays of messages over Controller Area Network (CAN), and some comparisons with static RTA are also provided. According to the experimental results, as the system complexity increases, our approach performs much more stable and less pessimistic.

The group-based preemptive scheduling provides a flexible mechanism to define the preemptive relations between tasks. However, this scheduling scheme together with a resource access synchronization protocol and requirements of fault tolerance makes the predication of a real-time system's behaviors more difficult than traditional scheduling scheme. The major contribution of our work is an algorithm to calculate the worst-case response time for tasks under the fault-tolerant group-based preemptive scheduling. We first present the algorithm assuming that no task suffers any error. Then the algorithm is extended to consider a fault model and a primary/alternative fault-tolerant mechanism. Based on the tasks' worst-case response time, the schedulability of a given task set can be determined. Moreover, we also introduce a method to calculate the minimum allowed time between two consecutive faults.