Chunhui Guo

Performance Comparisons of Parallel Power Flow Solvers on GPU System

This paper transforms sequential power flow problem to a parallel problem and solves it on GPU. In particular, we implement parallel Gauss-Seidel solver, Newton-Raphson solver, and P-Q decoupled solver using CUDA (Compute Unified Device Architecture) on GPU. The aim is to investigate the performance of the three different parallel power flow solvers. We use four IEEE standard power systems and one actual running power system from Shang dong Province as the test cases when comparing the speedups that a GPU system can provide. The results show that Newton-Raphson solver has the best speedup when it is operated on GPU, Gauss-Seidel solver performs the worst, and P-Q decoupled solver is in the middle. The test results also indicate that when the size of the system is small, GPU does not seem to have advantages over CPU from computation time perspective. However, as the size of the system increases, the advantages of GPU becomes more clear. For instance, when the system has close to one thousand bus counts, the GPU can provide as high as over fifty-three times speedup.

Use Two-Level Rejuvenation to Combat Software Aging and Maximize Average Resource Performance

Software aging is a common phenomenon which is often manifested through system performance degradation. Rejuvenation is one of the most commonly used approaches to handle issues caused by software aging. To combat resource performance degradation and at the same time maintain maximized average resource performance, we present a two-level rejuvenation strategy, i.e., interleaving a set of n warm rejuvenations with one cold rejuvenation. Our target is to find the optimal n that maximizes system average performance. We first define a resource model that takes into consideration of performance degradation and two-level rejuvenations. Based on the resource model, we formally analyze the resource supply and present the MAX-PERFORMANCE algorithm to determine the optimal rejuvenation pattern that maximizes the average resource performance. The simulation results show that with a two-level rejuvenation strategy, we can achieve 25.22% higher average resource performance compared with a single level rejuvenation strategy.

Transforming medical best practice guidelines to executable and verifiable statechart models

Improving effectiveness and safety of patient care is an ultimate objective for medical cyber- physical systems. However, the existing medical best practice guidelines in hospital handbooks are often lengthy and difficult for medical staff to remember and apply clinically. Statechart is a widely used model in designing complex systems and enables rapid prototyping and clinical validation with medical doctors. However, clinical validation is often not adequate for guaranteeing the correctness and safety of medical cyber-physical systems, and formal verification is required. The paper presents an approach that transforms medical best practice guidelines to verifiable statechart models and supports both clinical validation in collaboration with medical doctors and formal verification. In particular, we use an open source statechart tool Yakindu to model best practice guidelines and use the statechart to interact with doctors for validating the model correctness. The statechart model is then automatically transformed to a verifiable formal model, such as timed automata, so that existing formal verification tool, such as UPPAAL, can be used to verify required safety properties. The approach also provides the ability to trace back to the paths in the statechart model (Yakindu model) when a specific property in its associated formal model (UPPAAL model) fails. A cardiac arrest scenario is used as a case study to validate the proposed approach. The tool is available on our website www.cs.iit.edu/~code/software/Y2U.

Maximize System Reliability for Long Lasting and Continuous Applications

In this paper, we use software rejuvenation as a preventive and proactive fault-tolerance technique to maximize the level of reliability for continuous and safety critical systems. We take both transient faults caused by software aging effects and network transmission faults into consideration and mathematically analyze the optimal software rejuvenation period that maximizes system’s reliability. The theoretical result is verified through empirical studies.

Modeling and integrating physical environment assumptions in medical cyber-physical system design

Implicit physical environment assumptions made by safety critical cyber-physical systems, such as medical cyber-physical systems (M-CPS), can lead to catastrophes. Several recent U.S. Food and Drug Administration (FDA) medical device recalls are due to implicit physical environment assumptions. In this paper, we develop a mathematical assumption model and composition rules that allow M-CPS engineers to explicitly and precisely specify assumptions about the physical environment in which the designed M-CPS operates. Algorithms are developed to integrate the mathematical assumption model with system model so that the safety of the system can be not only validated by both medical and engineering professionals but also formally verified by existing formal verification tools. We use an FDA recalled medical ventilator scenario as a case study to show how the mathematical assumption model and its integration in M-CPS design may improve the safety of the ventilator and M-CPS in general.

Reliability guaranteed energy minimization on mixed-criticality systems

Analyze resource demand of MC task set under reliability and deadline constraints.Develop a heuristic approach to solve the formulated problem.Evaluate the proposed approach through simulation under various scenarios.Achieve up to 10% more energy saving comparing with the existing approaches. This paper studies the energy minimization problem in mixed-criticality systems that have stringent reliability and deadline constraints. We first analyze the resource demand of a mixed-criticality task set that has both reliability and deadline requirements. Based on the analysis, we present a heuristic task scheduling algorithm that minimizes systems energy consumption and at the same time also guarantees systems reliability and deadline constraints. Extensive experiments are conducted to evaluate and validate the performance of the proposed algorithm. The empirical results show that the algorithm further improves energy saving by up to 10% compared with the approaches proposed in our earlier work.

Schedulability Analysis for Real-Time Task Set on Resource with Performance Degradation and Periodic Rejuvenation

Most schedulability analyses in the literature assume that the performance of computing resource does not change over time. However, due to ever increased complexity of computer systems, software aging issues become more difficult, if not impossible, to eradicate. Hence, the assumption that computing resource has a constant performance in its entire lifetime does not hold in real world long-standing systems. In this paper, we study real-time task schedulability under a resource model that the resources performance degrades with a known degradation function and the resource is periodically rejuvenated. The resource model is referred to as P2-resource model for performance degradation and periodic rejuvenation. We address three real-task schedulability related questions under the P2-resource model, i.e., (1) resource supply bounds of the P2-resource, (2) task set utilization bounds under Earliest Deadline First (EDF) and Rate Monotonic (RM) scheduling policies, respectively, and (3) experimentally study the tightness of the bounds developed, and the impact of resource degradation rate, rejuvenation period, and rejuvenation cost on the bounds.

A Note on the EDF Preemption Behavior in “Rate Monotonic Versus EDF: Judgment Day”

In G. C. Buttazzo, Real-Time Syst., vol. 29, no. 1, pp. 5-26, 2005, the author empirically compared earliest deadline first (EDF) and rate monotonic (RM) scheduling algorithms and made a few EDF preemption behavior observations based on data obtained from the first 1000 time units of scheduling activities. However, based on test settings given in (G. C. Buttazzo, Real-Time Syst., vol. 29, no. 1, pp. 5-26, 2005), the first 1000 time units occupies only a small percentage of the entire task sets hyper-period. We extend EDF preemption behavior study by extending scheduling activities from the first small percentage of a hyper-period of a given task to the entire hyper-period. The extended experiments indicate that the number of preemptions occurred at the beginning of a task sets hyper-period does not necessarily represent the trend for the entire hyper-period. Hence, comparisons and conclusions made based on a small percentage of a scheduling interval over a task sets hyper-period may not be accurate. Second, the total number of preemptions within a task sets hyper-period does not decrease when the task set total utilization increases which is different from the observation obtained in (G. C. Buttazzo, Real-Time Syst., vol. 29, no. 1, pp. 5-26, 2005). We also investigate the impact of execution time differences among tasks on the preemption behavior.

Pattern-Based Statechart Modeling Approach for Medical Best Practice Guidelines - A Case Study

Improving effectiveness and safety of patient care is an ultimate objective for medical cyber-physical systems. Many medical best practice guidelines exist in the format of hospital handbooks which are often lengthy and difficult for medical staff to remember and apply clinically. Statechart is an effective tool to model medical guidelines and enables clinical validation with medical staffs. However, some advanced statechart elements could result in high cost, such as low understandability, high difficulty in clinical validation, formal verification, and failure trace back. The paper presents a pattern-based statechart modeling approach for medical best practice guidelines, i.e., model medical guidelines with basic statechart elements and model patterns which are built upon these basic elements. For practical use, we implement the proposed approach based on open-source Yakindu statecharts. We also use a simplified cardiac arrest scenario provided to our team by Carle Foundation Hospital as a case study to validate the proposed approach.

Modeling and Integrating Human Interaction Assumptions in Medical Cyber-Physical System Design

For a cyber-physical system, its execution behaviors are often impacted by human interactive behaviors. However, the assumptions about a cyber-physical systems expected human interactive behaviors are often informally documented, or even left implicit and unspecified in system design. Unfortunately, such implicit human interaction assumptions made by safety critical cyber-physical systems, such as medical cyber-physical systems (M-CPS), can lead to catastrophes. Several recent U.S. Food and Drug Administration (FDA) medical device recalls are due to implicit human interaction assumptions. In this paper, we classify the categories of constraints in human interaction assumptions in the medical domain and develop a mathematical assumption model that allow M-CPS engineers to explicitly and precisely specify assumptions about human interactions. Algorithms are developed to integrate mathematical assumption models with system model so that the safety of the system can be not only validated by both medical and engineering professionals but also formally verified by existing formal verification tools. We use an FDA recalled medical ventilator scenario as a case study to show how the mathematical assumption model and its integration in M-CPS design may improve the safety of the ventilator and M-CPS in general.