Resource-Sharing Servers for Open Environments

Bertogna, Marko; Fisher, Nathan; Baruah, Sanjoy

doi:10.1109/tii.2009.2026051

Cited by 38 publications

(67 citation statements)

References 38 publications

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“…In the specific case in which Z k (t) is lower bounded by the supply bound function, the test becomes only sufficient and the set of testing points can be better restricted as stated in the following theorem (Bertogna et al, 2009):…”

Section: Theorem 1 (Bini Et Al 2009) a Set Of Preemptive Periodic mentioning

confidence: 99%

Handling Overload Conditions in Real-Time Systems

Buttazzo¹

2012

Real-Time Systems, Architecture, Scheduling, and Application

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This chapter deals with the problem of handling overload conditions, that is, those critical situations in which the computational demand requested by the application exceeds the processor capacity. If not properly handled, an overload can cause an abrupt performance degradation, or even a system crash. Therefore, a real-time system should be designed to anticipate and tolerate unexpected overload situations through speciﬁc kernel mechanisms

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Section: Theorem 1 (Bini Et Al 2009) a Set Of Preemptive Periodic mentioning

confidence: 99%

Handling Overload Conditions in Real-Time Systems

Buttazzo¹

2012

Real-Time Systems, Architecture, Scheduling, and Application

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“…If a task that accesses a global shared resource is suspended during its execution due to the exhaustion of its budget, excessive blocking periods can occur which may hamper the correct timeliness of other components [2]. To accommodate resource sharing between components, three synchronization protocols [3], [4], [5] have been proposed based on the stack resource policy (SRP) [6]. Each of these protocols describes a run-time mechanism to handle the depletion of a component's budget during global resource access.…”

Section: Introductionmentioning

confidence: 99%

“…Each of these protocols describes a run-time mechanism to handle the depletion of a component's budget during global resource access. In short, two general approaches are proposed: (i) selfblocking before accessing a shared resource when the remaining budget is insufficient to complete a critical section [4], [5] or (ii) overrun the budget until the critical section ends [3]. However, when a task exceeds its specified worst-case criticalsection length, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…it misbehaves during global resource access, temporal isolation between components is no longer guaranteed. The protocols in [3], [4], [5] therefore break the temporal encapsulation and fault-containment properties of an HSF without the presence of complementary protection.…”

Section: Introductionmentioning

confidence: 99%

“…To achieve temporal isolation between components, even when resource-sharing components misbehave, we propose a modified SRP-based synchronization protocol, named Hierarchical Synchronization protocol with Temporal Protection (HSTP). It supports fixed-priority as well as earliest-deadline-first (EDF) scheduled systems and it complements existing synchronization protocols [3], [4], [5] for HSFs. We efficiently achieve fault-containment by disabling preemptions of other tasks within the same component during global resource access.…”

Section: Introductionmentioning

confidence: 99%

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Dependable Resource Sharing for Compositional Real-Time Systems

Heuvel

Bril

Lukkien

2011

2011 IEEE 17th International Conference on Embedded and Real-Time Computing Systems and Applications

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Citation for published version (APA):Heuvel, van den, M. M. H. P., Bril, R. J., & Lukkien, J. J. (2011). Dependable resource sharing for compositional real-time systems. In Proceedings of the 17th IEEE International Conference on Embedded and Real-time Computing Systems and Applications (RTCSA 2011, Toyama, Japan, August 28-31, 2011 Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publicationGeneral rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement: www.tue.nl/taverne Take down policyIf you believe that this document breaches copyright please contact us at: openaccess@tue.nl providing details and we will investigate your claim. AbstractHierarchical scheduling frameworks (HSFs) have been extensively investigated as a paradigm for facilitating temporal isolation between components that need to be integrated on a single shared processor. In the presence of shared resources, however, temporal isolation may break when one of the accessing components executes longer than specified during global resource access. The ability to confine such temporal faults makes the HSF more dependable. As a solution we propose a stack-resource-policy (SRP)-based synchronization protocol for HSFs, named Hierarchical Synchronization protocol with Temporal Protection (HSTP).When a component exceeds its specified critical-section length, HSTP enforces a component to self-donate its own budget to accelerate the resource release. In addition, a component that blocks on a locked resource may donate budget. The schedulability of those components that are independent of the locked resource is unaffected. HSTP efficiently limits the propagation of temporal faults to resource-sharing components by disabling local preemptions in a component durin...

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Timing isolation and improved scheduling of deep neural networks for real‐time systems

2020

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In recent years, the performance of deep neural networks (DNNs) is significantly improved, making them suitable for many application fields, such as autonomous driving, advanced robotics, and industrial control. Despite a lot of research being devoted to improving the accuracy of DNNs, only limited efforts have been spent to enhance their timing predictability, required in several real-time applications. This paper proposes a software infrastructure based on the Linux operating system to integrate DNNs within a real-time multicore system. It has been realized by modifying both the internal scheduler of the popular TensorFlow framework and the SCHED_DEADLINE scheduling class of Linux. The proposed infrastructure allows providing timing isolation of DNN inference tasks, hence improving the determinism of the temporal interference generated by TensorFlow. The proposal is finally evaluated with a case study derived from a state-of-the-art benchmark inspired by an autonomous industrial system. Extensive experiments demonstrate the effectiveness of the proposed solution and show a significant reduction of both average and longest-observed response times of TensorFlow tasks.

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Resource-Sharing Servers for Open Environments

Cited by 38 publications

References 38 publications

Handling Overload Conditions in Real-Time Systems

Handling Overload Conditions in Real-Time Systems

Dependable Resource Sharing for Compositional Real-Time Systems

Timing isolation and improved scheduling of deep neural networks for real‐time systems

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