Mixed criticality systems with weakly-hard constraints

Gettings, Oliver; Quinton, Sophie; Davis, Robert I.

doi:10.1145/2834848.2834850

Cited by 49 publications

(57 citation statements)

References 30 publications

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“…In this context, a plethora of studies has been carried out to improve the service offered to the LC tasks [3], [20], [28], [41], [40], [43], [5], [31], [36], [30], [18], [19], [25]. These approaches can be classified into four major categories:…”

Section: Related Workmentioning

confidence: 99%

Combining Task-level and System-level Scheduling Modes for Mixed Criticality Systems

Boudjadar

Ramanathan

Easwaran

et al. 2019

2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

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Different scheduling algorithms for mixed criticality systems have been recently proposed. The common denominator of these algorithms is to discard low critical tasks whenever high critical tasks are in lack of computation resources. This is achieved upon a switch of the scheduling mode from Normal to Critical. We distinguish two main categories of the algorithms: system-level mode switch and task-level mode switch. System-level mode algorithms allow low criticality (LC) tasks to execute only in normal mode. Task-level mode switch algorithms enable to switch the mode of an individual high criticality task (HC), from low (LO) to high (HI), to obtain priority over all LC tasks. This paper investigates an online scheduling algorithm for mixedcriticality systems that supports dynamic mode switches for both task level and system level. When a HC task job overruns its LC budget, then only that particular job is switched to HI mode. If the job cannot be accommodated, then the system switches to Critical mode. To accommodate for resource availability of the HC jobs, the LC tasks are degraded by stretching their periods until the Critical mode exhibiting job complete its execution. The stretching will be carried out until the resource availability is met. We have mechanized and implemented the proposed algorithm using Uppaal. To study the efficiency of our scheduling algorithm, we examine a case study and compare our results to the state of the art algorithms.

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Section: Related Workmentioning

confidence: 99%

Combining Task-level and System-level Scheduling Modes for Mixed Criticality Systems

Boudjadar

Ramanathan

Easwaran

et al. 2019

2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

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“…C(HI) ≤ C(LO) for these tasks) [24] perhaps by switching to simpler version of the software. Drop jobs from a specific subset of tasks [25], [26] or skip s i in every m i jobs of each task [27].…”

Section: Degraded Service For Lo-criticality Tasksmentioning

confidence: 99%

“…We note that the approach taken by the bailout protocol is orthogonal to those of job dropping [26] and weakly-hard guarantees for LO-criticality tasks proposed in [27], hence it is possible that the different techniques could be combined; such work is however beyond the scope of this paper.…”

Section: Degraded Service For Lo-criticality Tasksmentioning

confidence: 99%

An Enhanced Bailout Protocol for Mixed Criticality Embedded Software

Bate

Burns

Davis

2017

IIEEE Trans. Software Eng.

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Abstract-To move mixed criticality research into industrial practice requires models whose run-time behaviour is acceptable to systems engineers. Certain aspects of current models, such as abandoning lower criticality tasks when certain situations arise, do not give the robustness required in application domains such as the automotive and aerospace industries. In this paper a new bailout protocol is developed that still guarantees high criticality software but minimises the negative impact on lower criticality software via a timely return to normal operation. We show how the bailout protocol can be integrated with existing techniques, utilising both offline slack and online gain-time to further improve performance. Static analysis is provided for schedulability guarantees, while scenario-based evaluation via simulation is used to explore the effectiveness of the protocol.Index Terms-Real-Time Systems, Mixed Criticality, Fixed Priority Scheduling, Mode Changes. ! Preliminary publicationThis paper extends initial research into a bailout protocol for mixed criticality systems presented at ECRTS 2015 [1]. The additional material includes: An extended worked example illustrating, in figures 1 and 2, the behaviour of the bailout protocol as compared to the baseline Adaptive Mixed Criticality (AMC) scheduling policy. Extensions to reclaim gain-time, which becomes available when a task executes for less than its worst-case execution time budget. Integration of this technique with the bailout protocol is described in Section 5. An extended scenario based evaluation, in Section 6. This examines the benefits of gain-time reclamation in conjunction with the baseline Adaptive Mixed Criticality (AMC) scheduling policy and with the bailout protocol. The evaluation also covers additional metrics including the number of times that the system has to go into a HI-criticality mode, and the amount of time spent in that mode. It is also extended to show how a variety of different factors impact the performance of the bailout protocol and other scheduling policies, thus showing the broad range of circumstances in which the protocol is effective. Finally, in Section 8 we show how the bailout protocol can be adapted to systems with multiple criticality levels.

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“…1) The first category of studies [10]- [13], [16], [19], [20] support LC tasks by offering a degraded (and in some cases guaranteed) service to all of them when the system is in the HC mode. They do this either by reducing the dispatch frequency of jobs or by executing the LC tasks as background (lowpriority) workload.…”

Section: ) Scheduling Policy Edf-uvd (Section Iii-c)mentioning

confidence: 99%

Dynamic Budget Management with Service Guarantees for Mixed-Criticality Systems

Easwaran

2016

2016 IEEE Real-Time Systems Symposium (RTSS)

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Many existing studies on mixed-criticality (MC) scheduling assume that low-criticality budgets for high-criticality applications are known apriori. These budgets are primarily used as guidance to determine when the scheduler should switch the system mode from low to high. Based on this key observation, in this paper we propose a dynamic MC scheduling model under which low-criticality budgets for individual high-criticality applications are determined at runtime as opposed to being fixed offline. To ensure sufficient budget for high-criticality applications at all times, we use offline schedulability analysis to determine a system-wide total low-criticality budget allocation for all the high-criticality applications combined. This total budget is used as guidance in our model to determine the need for a modeswitch. The runtime strategy then distributes this total budget among the various applications depending on their execution requirement and with the objective of postponing mode-switch as much as possible. We show that this runtime strategy is able to postpone mode-switches for a longer time than any strategy that uses a fixed low-criticality budget allocation for each application. Finally, since we are able to control the total budget allocation for high-criticality applications before mode-switch, we also propose techniques to determine these budgets considering system-wide objectives such as schedulability and service guarantee for lowcriticality applications.

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Mixed criticality systems with weakly-hard constraints

Cited by 49 publications

References 30 publications

Combining Task-level and System-level Scheduling Modes for Mixed Criticality Systems

Combining Task-level and System-level Scheduling Modes for Mixed Criticality Systems

An Enhanced Bailout Protocol for Mixed Criticality Embedded Software

Dynamic Budget Management with Service Guarantees for Mixed-Criticality Systems

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