Terry Tidwell

Cyber-physical systems for real-time hybrid structural testing: a case study

Real-time hybrid testing of civil structures, in which computational models and physical components must be integrated with high fidelity at run-time, represents a grand challenge in the emerging area of cyber-physical systems. Actuator dynamics, complex interactions among computers and physical components, and computation and communication delays all must be managed carefully to achieve accurate tests. In this paper we present a case study of several fundamental interlocking challenges in developing and evaluating cyber-physical systems for real-time hybrid structural testing: (1) how physical and simulated components can be integrated flexibly and efficiently within a common reusable middleware architecture; (2) how predictable timing can be achieved atop commonly available hardware and software platforms; and (3) how physical vs. simulated versions of different components within a system can be interchanged with high fidelity between comparable configurations. Experimental results obtained through this case study give evidence of the feasibility and efficacy of these steps towards our overall goal: to develop a Cyber-physical Instrument for Real-time hybrid Structural Testing (CIRST).

Towards Configurable Real-Time Hybrid Structural Testing: A Cyber-Physical System Approach

Real-time hybrid testing of civil structures represents agrand challenge in the emerging area of cyber-physical systems. Hybrid testing improves significantly on either purely numerical or purely empirical approaches by integrating physical structural components and computational models. Actuator dynamics, complex interactions among computers and physical components, and computation and communication delays all hamper the ability to conduct accurate tests. To address these challenges, this paper presents initial work towards a Cyber-physical Instrument for Real-time hybrid Structural Testing (CIRST). CIRST aims to provide two salient features: a highly configurable architecture for integrating computers and physical components; and system support for real-time operations in distributed hybrid testing. This paper presents the motivation of the CIRST architectureand preliminary test results from a proof-of-concept implementation that integrates a simple structural element and simulation model. CIRST will have broad impacts on thefields of both civil engineering and real-time computing.It will enable high-fidelity real-time hybrid testing of awide range of civil infrastructures, and will also providea high-impact cyber-physical application for the study andevaluation of real-time middleware.

Scheduling Design and Verification for Open Soft Real-Time Systems

Open soft real-time systems, such as mobile robots, experience unpredictable interactions with their environments and yet must respond both adaptively and with reasonable temporal predictability. New scheduling approaches are needed to address the demands of such systems, in which many of the assumptions made by traditional real-time scheduling theory do not hold. In previous work we established foundations for a scheduling policy design and verification approach for open soft real-time systems, that can use different decision models, e.g., a Markov decision process (MDP), to capture the nuances of their scheduling semantics.However, several important refinements to the preliminary techniques developed in that work are needed to make the approach applicable in practice. This paper makes three main contributions to the state of the art in scheduling open soft real-time systems: (1) it defines a novel representation of the scheduling state space that is both more compact and more expressive than the model defined in our previous work; (2) it exploits regular structure of that representation to allow efficient verification of properties involving both discrete and continuous system state variables under specific scheduling policies; and (3) it removes the unnecessary use of a time horizon in our previous approach, thus allowing the more precise specification and enforcement of a wider range of scheduling policies for open soft real-time systems.

Optimizing Expected Time Utility in Cyber-Physical Systems Schedulers

Classical scheduling abstractions such as deadlines and priorities do not readily capture the complex timing semantics found in many real-time cyber-physical systems. Time utility functions provide a necessarily richer description of timing semantics, but designing utility-aware scheduling policies using them is an open research problem. In particular, scheduling design that optimizes expected utility accrual is needed for real-time cyber-physical domains. In this paper we design scheduling policies that optimize expected utility accrual for cyber-physical systems with periodic, non-preemptable tasks that run with stochastic duration. These policies are derived by solving a Markov Decision Process formulation of the scheduling problem. We use this formulation to demonstrate that our technique improves on existing heuristic utility accrual scheduling policies.

Scalable Scheduling Policy Design for Open Soft Real-Time Systems

Open soft real-time systems, such as mobile robots, must respond adaptively to varying operating conditions, while balancing the need to perform multiple mission specific tasks against the requirement that those tasks complete in a timely manner. Setting and enforcing a utilization target for shared resources is a key mechanism for achieving this behavior. However, because of the uncertainty and non-preempt ability of some tasks, key assumptions of classical scheduling approaches do not hold. In previous work we presented foundational methods for generating task scheduling policies to enforce proportional resource utilization for open soft real-time systems with these properties. However, these methods scale exponentially in the number of tasks, limiting their practical applicability.In this paper, we present a novel parameterized scheduling policy that scales our technique to a much wider range of systems. These policies can represent geometric features of the scheduling policies produced by our earlier methods, but only require a number of parameters that is quadratic in the number of tasks. We provide empirical evidence that the best of these policies are competitive with exact solution methods in small problems, and significantly outperform heuristic methods in larger ones.

Scheduling for Reliable Execution in Autonomic Systems

Scheduling the execution of multiple concurrent tasks on shared resources such as CPUs and network links is essential to ensuring the reliable operation of many autonomic systems. Well known techniques such as rate-monotonic scheduling can offer rigorous timing and preemption guarantees, but only under assumptions (i.e., a fixed set of tasks with well-known execution times and invocation rates) that do not hold in many autonomic systems. New hierarchical scheduling techniques are better suited to enforce the more flexible execution constraints and enforcement mechanisms that are required for autonomic systems, but a rigorous foundation for verifying and enforcing concurrency and timing guarantees is still needed for these approaches. The primary contributions of this paper are: (1) a scheduling policy design technique that can use different decision models across a wide range of systems models, and an example of how a specific (Markov Decision Process) decision model can be applied to a basic multi-threaded system model; (2) novel model checking techniques that can evaluate the behavior of the system model when it is placed under the control of the resulting scheduling policy; and (3) an evaluation of those scheduling policy design and model checking techniques for a simple but representative example of the kinds of execution scenarios that can arise in autonomic systems.

Scheduling policy design for autonomic systems

Scheduling the execution of multiple concurrent tasks on shared resources such as CPUs and network links is essential to ensuring the reliable operation of many autonomic systems. Well-known techniques such as rate-monotonic scheduling can offer rigorous timing and preemption guarantees, but only under assumptions (i.e. a fixed set of tasks with well-known execution times and invocation rates) that do not hold in many autonomic systems. New hierarchical scheduling techniques are better suited to enforce the more flexible execution constraints and enforcement mechanisms that are required for autonomic systems, but a rigorous and efficient foundation for verifying and enforcing concurrency and timing guarantees is still needed for these approaches. This paper summarises our previous work on addressing these challenges, on Markov decision process-based scheduling policy design and on wrapping repeated structure of the scheduling state spaces involved into a more efficient model, and presents a new algorithm called expanding state policy iteration (ESPI), that allows us to compute the optimal policy for a wrapped state model.

Scalable Utility Aware Scheduling Heuristics for Real-time Tasks with Stochastic Non-preemptive Execution Intervals

Time utility functions can describe the complex timing constraints of real-time and cyber-physical systems. However, utility aware scheduling policy design is an open research problem. Previously we solved a Markov Decision Process formulation of the scheduling problem to derive value-optimal scheduling policies for systems with periodic real-time task sets and stochastic non-preemptive execution intervals. However, the complexity of computing solutions and their policy storage requirements necessitate the exploration of scalable solutions. In this paper we generalize the Utility Accrual Packet Scheduling Algorithm. We compare several heuristics to Markov Decision Process policy evaluation under soft and hard real-time conditions, different load conditions, and different classes of time utility functions. Based on these evaluations we present guidelines for which heuristics are best suited to particular scheduling criteria.

Group Scheduling in SELinux to Mitigate CPU-Focused Denial of Service Attacks

Popular security techniques such as public-private key encryption, firewalls, and role-based access control offer significant protection of system data, but offer only limited protection of the computations using that data from significant interference due to accident or adversarial attack. However, in an increasing number of modern systems, ensuring the reliable execution of system activities is every bit as important as ensuring data security. This paper makes three contributions to the state of the art in protection of the execution of system activities from accidental or adversarial interference. First, we consider the motivating problem of CPU-focused denial of service attacks, and explain how limitations of current approaches to these kinds of attacks make it difficult to offer sufficiently rigorous and fine-grained assurances of protection for the execution of system computations. Second, we describe a novel solution approach in which we have integrated fine-grained scheduling decision functions with system call hooks from the Security Enhanced Linux (SELinux) framework within the Linux 2.6 kernel. Third, we present empirical evaluations of the efficacy of our approach in controlling the CPU utilization of competing greedy computations that are either completely CPU bound, or that interleave I/O and CPU access, across a range of relative allocations of the CPU.

Optimal Time Utility Based Scheduling Policy Design for Cyber-Physical Systems

Classical scheduling abstractions such as deadlines and priorities do not readily capture the complex timing semantics found in many real-time cyber-physical systems. Time utility functions provide a necessarily richer description of timing semantics, but designing utility-aware scheduling policies using them is an open research problem. In particular, optimal utility accrual scheduling design is needed for real-time cyber-physical domains. In this paper we design optimal utility accrual scheduling policies for cyber-physical systems with periodic, non-preemptable tasks that run with stochastic duration. These policies are derived by solving a Markov Decision Process formulation of the scheduling problem. We use this formulation to demonstrate that our technique improves on existing heuristic utility accrual scheduling policies. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Optimal Time Utility Based Scheduling Policy Design for Cyber-Physical Systems Terry Tidwell, Robert Glaubius, Christopher D. Gill and William D. Smart Department of Computer Science and Engineering Washington University in St. Louis Email: {ttidwell,rlg1,cdgill,wds}@cse.wustl.edu Abstract—Classical scheduling abstractions such as deadlines and priorities do not readily capture the complex timing semantics found in many real-time cyber-physical systems. Time utility functions provide a necessarily richer description of timing semantics, but designing utility-aware scheduling policies using them is an open research problem. In particular, optimal utility accrual scheduling design is needed for real-time cyber-physical domains. In this paper we design optimal utility accrual scheduling policies for cyber-physical systems with periodic, non-preemptable tasks that run with stochastic duration. These policies are derived by solving a Markov Decision Process formulation of the scheduling problem. We use this formulation to demonstrate that our technique improves on existing heuristic utility accrual scheduling policies.Classical scheduling abstractions such as deadlines and priorities do not readily capture the complex timing semantics found in many real-time cyber-physical systems. Time utility functions provide a necessarily richer description of timing semantics, but designing utility-aware scheduling policies using them is an open research problem. In particular, optimal utility accrual scheduling design is needed for real-time cyber-physical domains. In this paper we design optimal utility accrual scheduling policies for cyber-physical systems with periodic, non-preemptable tasks that run with stochastic duration. These policies are derived by solving a Markov Decision Process formulation of the scheduling problem. We use this formulation to demonstrate that our technique improves on existing heuristic utility accrual scheduling policies.