Springer Science and Business Media LLC

This paper presents a scheduling model, called decision-driven scheduling, elaborates key optimality results for a fundamental scheduling model, and evaluates new heuristics solving more general versions of the problem. In the context of applications that need control and actuation, the traditional execution model has often been either time-driven or event-driven. In time-driven applications, sensors are sampled periodically, leading to the classical periodic task model. In event-driven applications, sensors are sampled when an event of interest occurs, such as motion-activated cameras, leading to an event-driven task activation model. In contrast, in decision-driven applications, sensors are sampled when a particular decision must be made. We offer a justification for why decision-driven scheduling might be of increasing interest to Internet-of-things applications, and explain why it leads to interesting new scheduling problems (unlike time-driven and event-driven scheduling), including the problems addressed in this paper.

This paper derives energy-optimal batching periods for asynchronous multistage data processing on sensor nodes in the sense of minimizing energy consumption while meeting end-to-end deadlines. Batching the processing of (sensor) data maximizes processor sleep periods, hence minimizing the wakeup frequency and the corresponding overhead. The algorithm is evaluated on mPlatform, a next-generation heterogeneous sensor node platform equipped with both a low-end microcontroller (MSP430) and a higher-end embedded systems processor (ARM). Experimental results show that the total energy consumption of mPlatform, when processing data flows at their optimal batching periods, is up to 35% lower than that for uniform period assignment. Moreover, processing data at the appropriate processor can use as much as 80% less energy than running the same task set on the ARM alone and 25% less energy than running the task set on the MSP430 alone.

When multiple model predictive controllers are implemented on a shared controller area network (CAN), their performance may degrade due to the variable timing and delays among messages. The priority based real-time scheduling of messages on the CAN introduces complex timing of events, especially when the types and number of messages change at runtime. This paper introduces a novel hybrid timing model to make runtime predictions on the timing of the messages for a finite time window. Controllers can be designed using the optimization algorithms for model predictive control by considering the timing as optimization constraints. This timing model allows multiple controllers to share a CAN without significant degradation in the controller performance. The timing model also provides a convenient way to check the schedulability of messages on the CAN at runtime. Simulation results demonstrate that the timing model is accurate and computationally efficient to meet the needs of real-time implementation. Simulation results also demonstrate that model predictive controllers designed when considering the timing constraints have superior performance than the controllers designed without considering the timing constraints.

With the increasing performance demand in real-time systems it becomes more and more important to provide feedback to programmers and software development tools on the performance-relevant code parts of a real-time program. So far, this information was limited to an estimation of the worst-case execution time (WCET) and its associated worst-case execution path (WCEP) only. However, both, the WCET and the WCEP, only provide partial information. Only code parts that are on one of the WCEPs are indicated to the programmer. No information is provided for all other code parts. To give a comprehensive view covering the entire code base, tools in the spirit of program profiling are required. This work proposes an efficient approach to compute worst-case timing information for all code parts of a program using a complementary metric, called criticality. Every statement of a program is assigned a criticality value, expressing how critical the code is with respect to the global WCET. This gives valuable information how close the worst execution path passing through a specific program part is to the global WCEP. We formally define the criticality metric and investigate some of its properties with respect to dominance in control-flow graphs. Exploiting some of those properties, we propose an algorithm that reduces the overhead of computing the metric to cover complete programs. We also investigate ways to efficiently find only those code parts whose criticality is above a given threshold. Experiments using well-established real-time benchmark programs show an interesting distribution of the criticality values, revealing considerable amounts of highly critical as well as uncritical code. The metric thus provides ideal information to programmers and software development tools to optimize the worst-case execution time of these programs.

Design of real time and concurrent systems requires formal approaches in order to facilitate verification and validation at each step. Methods based on formal logic have been previously suggested but they often work only in a specific domain and are generally only possible with specialized users. In an attempt to overcome these two restrictions, this paper proposes a method based on rewriting logic. A grounding in theory is not a prerequisite for users. The method integrates modularity and abstraction and follows the main principles of an object-oriented approach. Different tools are available: a graphical editor for the specification of the structure and the behavior of the objects, an inference engine for rule validation and a generator of prototypes.

This paper proposes methodologiesto control the access of B+-tree-indexed datain a batch and firm real-time fashion. Algorithms are proposedto insert, query, delete, and rebalance B+-tree-indexeddata based on the non-real-time algorithms proposed in Kerttu,Eljas, and Tatu (1996) and the idea of priority inheritance (Sha,Rajkumar, and Lehoczky, 1990). We propose methodologies to reducethe number of disk I/O to improve the system performancewithout introducing more priority inversion. When the schedulabilityof requests with critical timing constraints is highly important,we propose a mechanism for data reservation based on the ideaof preemption level and the Stack Resource Policy (Baker, 1990).The performance of our methodologies was evaluated by a seriesof experiments, from which we have obtained encouraging results.