Predictability of Fixed-Job Priority Schedulers on Heterogeneous Multiprocessor Real-Time Systems

The multiprocessor Fixed-Job Priority (FJP) scheduling of real-time systems is studied. An important property for the schedulability analysis, the predictability (regardless to the execution times), is studied for heterogeneous multiprocessor platforms. Our main contribution is to show that any FJP schedulers are predictable on unrelated platforms. A convenient consequence is the fact that any FJP schedulers are predictable on uniform multiprocessors

On the periodic behavior of real-time schedulers on identical multiprocessor platforms

This paper is proposing a general periodicity result concerning any deterministic and memoryless scheduling algorithm (including non-work-conserving algorithms), for any context, on identical multiprocessor platforms. By context we mean the hardware architecture (uniprocessor, multicore), as well as task constraints like critical sections, precedence constraints, self-suspension, etc. Since the result is based only on the releases and deadlines, it is independent from any other parameter. Note that we do not claim that the given interval is minimal, but it is an upper bound for any cycle of any feasible schedule provided by any deterministic and memoryless scheduler

A Survey of Probabilistic Timing Analysis Techniques for Real-Time Systems

This survey covers probabilistic timing analysis techniques for real-time systems. It reviews and critiques the key results in the field from its origins in 2000 to the latest research published up to the end of August 2018. The survey provides a taxonomy of the different methods used, and a classification of existing research. A detailed review is provided covering the main subject areas: static probabilistic timing analysis, measurement-based probabilistic timing analysis, and hybrid methods. In addition, research on supporting mechanisms and techniques, case studies, and evaluations is also reviewed. The survey concludes by identifying open issues, key challenges and possible directions for future research

Impact of job dropping on the schedulability of uniprocessor probabilistic real-time systems with variable execution times

held in conjunction with the 22th Euromicro Intl Conference on Real-Time Systems, July 7-9, 2010, Brussels, BelgiumInternational audienceIn this paper we address the problem of uniprocessor probabilistic scheduling of real-time systems with variable execution times. For these systems the tasks have an associated probability of missing the deadline, i.e., some jobs may miss their deadlines without affecting the schedulability of the system. Therefore dropping these jobs does not affect the schedulability of the system and it could increase the probability of other jobs to meet their deadline. The problem of deciding what jobs to drop is not trivial and we discuss a possible solution

Systematic Searches for Global Multiprocessor Real-Time Scheduling

In this paper we address the problem of global real-time periodic scheduling on homogeneous multiprocessor platforms. A number of theoretical results have been obtained in the field of real-time systems, but mainly focusing on properties of specific algorithms in uniprocessor settings. The multiprocessor case has been considered only recently, with few resolution techniques proposed and experimented with up to now. In this paper we discuss several systematic search algorithms—exploring different search spaces—that exploit various features of the problem. These approaches are then evaluated experimentally on numerous randomly generated problems. This work shows (1) how two heuristic approaches can solve most (feasible and unfeasible) problems in no time, and (2) how to improve a state of the art algorithm by looking at jobs' laxities and by focusing the search on bottlenecks. We also discuss limitations of the proposed solvers and future work

A Survey of Probabilistic Schedulability Analysis Techniques for Real-Time Systems

This survey covers schedulability analysis techniques for probabilistic real-time systems. It reviews the key results in the field from its origins in the late 1980s to the latest research published up to the end of August 2018. The survey outlines fundamental concepts and highlights key issues. It provides a taxonomy of the different methods used, and a classification of existing research. A detailed review is provided covering the main subject areas as well as research on supporting techniques. The survey concludes by identifying open issues, key challenges and possible directions for future research

Probabilistic Analysis for Mixed Criticality Scheduling with SMC and AMC

This paper introduces probabilistic analysis for fixed priority preemptive scheduling of mixed criticality systems on a uniprocessor using the Adaptive Mixed Criticality (AMC) and Static Mixed Criticality (SMC) schemes. We compare this analysis to the equivalent deterministic methods, highlighting the performance gains that can be obtained by utilising more detailed information about worst-case execution time estimates described in terms of probability distributions

The temporal correlation of data in a multirate system

International audienceTechnologies within embedded real-time systems are continuously evolving making them intelligent; at some point they can achieve targeted functions autonomously. Such systems are extended with capability of sensing the surrounding environment and deciding on their own. In addition to the feasible scheduling policy, the correctness of such decisions highly depends on the quality of the used input data. Thereby, the data management within such systems must fulfill some properties in order to guarantee their correct functioning. In this paper we address the problem of data temporal correlation and validity when the system scheduling properties are defined. We present preliminary results on expected properties of the architectures and underline future work

Ensuring data freshness for periodic real-time tasks

International audienceAutomotive systems are composed of embedded applications which are continuously exchanging real-time data. Exchanged data are then propagated within a list of applications involved at different rates in the definition of a function. Different rates of executions for the applications provoke over-and/or under-sampling of data and the age of the data has an obvious impact on the driving decisions. Ensuring that the appropriate data are consumed by an application motivates to maintain the freshness or the temporal validity of real-time data. In this paper we propose a method calculating the freshness of the data for real-time systems where multi-rate sampling of data is considered