The AMC-IA mixed-criticality scheduling analysis was proposed as an improvement to the AMC-MAX adaptive mixed-criticality scheduling analysis. However, we have identified several necessary corrections to the AMC-IA analysis. In this article, we motivate and describe those corrections, and discuss and illustrate why the corrected AMC-IA analysis cannot be shown to outperform AMC-MAX.

Traditional fixed-priority scheduling analysis for periodic and sporadic task sets is based on the assumption that all tasks are equally critical to the correct operation of the system. Therefore, every task has to be schedulable under the chosen scheduling policy, and estimates of tasks' worst-case execution times must be conservative in case a task runs longer than is usual. To address the significant underutilization of a system's resources under normal operating conditions that can arise from these assumptions, several mixed-criticality scheduling approaches have been proposed. However, to date, there have been few quantitative comparisons of system schedulability or runtime overhead for the different approaches. In this article, we present a side-by-side implementation and evaluation of the known mixed-criticality scheduling approaches, for periodic and sporadic mixed-criticality tasks on uniprocessor systems, under a mixed-criticality scheduling model that is common to all these approaches. To make a fair evaluation of mixed-criticality scheduling, we also address previously open issues and propose modifications to improve particular approaches. Our empirical evaluations demonstrate that user-space implementations of mechanisms to enforce different mixed-criticality scheduling approaches can be achieved atop Linux without kernel modification, with reasonably low (but in some cases nontrivial) overhead for mixed-criticality real-time task sets.

Traditional fixed-priority scheduling analysis for periodic task sets is based on the assumption that all tasks are equally critical to the correct operation of the system. Therefore, every task has to be schedulable under the scheduling policy, and estimates of tasks' worst case execution times must be conservative in case a task runs longer than is usual. To address the significant under-utilization of a system's resources under normal operating conditions that can arise from these assumptions, three main approaches have been proposed: priority assignment, period transformation, and zero-slack scheduling. However, to date there has been no quantitative comparison of system schedulability or run-time overhead for the different approaches. In this paper, we present what is to our knowledge the first side-by-side evaluation of those approaches, for periodic mixed-criticality tasks on uniprocessor systems, under a mixed-criticality scheduling model that is common to all three approaches. To make a fair evaluation of zero-slack scheduling, we also address two previously open issues: how to accommodate execution of a task after its deadline, and how to account for previously unidentified forms of interference between mixed-criticality tasks. Our simulations show that while priority assignment and period transformation are most likely to be able to schedule a randomly selected task set, a small fraction of the task sets are schedulable only under the zero-slack approach. Our empirical evaluation demonstrates that user-space implementations of mechanisms to enforce period transformation and zero-slack scheduling can be achieved on Linux without kernel modification, with suitably low overhead for mixed-criticality real-time task sets.

Driven by the evolution of modern computer architectures from uni-processor to multi-core platforms, there is an increasing need to provide light-weight, efficient, and predictable support for fine-grained parallel and distributed execution of soft real-time tasks with end-to-end timing constraints, modeled as directed a cyclic graphs whose edges capture dependences among their subtasks. At the same time, there is a need to support state of the art programming models such as distributed components, whose ability to encapsulate functionality and allow context-specific optimizations is essential to manage the increasing complexity of modern distributed real-time and embedded systems and systems-of-systems. Real-time distributed middleware such as RT-CORBA has not kept pace with these developments, and a new generation of middleware is needed that can map these dependent subtask graphs onto distributed hosts with multi-core architectures, efficiently and within a simple, lightweight, and intuitive component programming model. To overcome these limitations, we have designed and implemented MC Flow, a novel distributed real-time component middleware for dependent subtask graphs running on multi-core platforms. MC Flow provides three new contributions to the state of the art in real-time component middleware: (1) a very lightweight component model that facilitates system integration and deployment through automatic code generation at compile time from a deployment plan specification, (2) transparent optimization of inter-component communication, and (3) the use of interface polymorphism to separate functional correctness from data copying and other performance constraints so that they can be configured and enforced independently but in a type-safe manner. Empirical evaluations of our approach in comparison to the widely used TAO real-time middleware show that MC Flow performs comparably to TAO when only one core is used and outperforms TAO when multiple cores are involved.

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.

Real-time image transmission is crucial to an emerging class of distributed embedded systems operating in open network environments. Examples include avionics mission re-planning over Link-16, security systems based on wireless camera networks, and online collaboration using camera phones. Meeting image transmission deadlines is a key challenge in such systems due to unpredictable network conditions. In this paper, we present CAMRIT, a Control-based Adaptive Middleware framework for Real-time Image Transmission in distributed real-time embedded systems. CAMRIT features a distributed feedback control loop that meets image transmission deadlines by dynamically adjusting the quality of image tiles. We derive an analytic model that captures the dynamics of a distributed middleware architecture. A control theoretic methodology is applied to systematically design a control algorithm with analytic assurance of system stability and performance, despite uncertainties in network bandwidth. Experimental results demonstrate that CAMRIT can provide robust real-time guarantees for a representative application scenario.

Developing distributed real-time and embedded (DRE) systems in which multiple quality-of-service (QoS) dimensions must be managed is an important and challenging problem. This paper makes three contributions to research on multi-dimensional QoS for DRE systems. First, it describes the design and implementation of a fault-tolerant real-time CORBA event service for the ACE ORB (TAO). Second, it describes our enhancements and extensions to features in TAO, to integrate real-time and fault tolerance properties. Third, it presents an empirical evaluation of our approach. Our results show that with some refinements, real-time and fault-tolerance features can be integrated effectively and efficiently in a CORBA event service.

Real-time image transmission is crucial to an emerging class of distributed embedded systems operating in open network environments. Examples include avionics mission re-planning over Link-16, security systems based on wireless camera networks, and online collaboration using camera phones. Meeting image transmission deadlines is a key challenge in such systems due to unpredictable network conditions. In this paper, we present CAMRIT, a control-based adaptive middleware framework for real-time image transmission in distributed real-time embedded systems. CAMRIT features a distributed feedback control loop that meets image transmission deadlines by dynamically adjusting the quality of image tiles. We derive an analytic model that captures the dynamics of a distributed middleware architecture. A control theoretic methodology is applied to systematically design a control algorithm with analytic assurance of system stability and performance, despite uncertainties in network bandwidth. Experimental results demonstrate that CAMRIT can provide robust real-time guarantees for a representative application scenario.