Adaptive Dynamic Scheduling on Multifunctional Mixed-Criticality Automotive Cyber-Physical Systems

Xie, Guoqi; Zeng, Gang; Li, Zhetao; Li, Renfa; Li, Keqin

doi:10.1109/tvt.2017.2674302

Cited by 62 publications

(46 citation statements)

References 44 publications

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“…Xie et al presented a fairness-based and adaptive dynamic scheduling algorithm on multiple-functional mixed-criticality. Directed acyclic graph (DAG) is used as a task graph to represent distributed automotive functions and to construct a model according to the characteristics of the concerned ACPS (Xie et al, 2017). Another reference, the AUTOSAR adaptive platform is a heterogeneous platform that integrates parallel computing, real-time requirements, safety, criticality, and existing functions into a heterogeneous architecture (Fuerst, and Bechter, 2016).…”

Section: Run-time Monitoring Of Operation Of Softwarementioning

confidence: 99%

From Validation of Medical Devices towards Validation of Adaptive Cyber-Physical Systems

Tavčar

Duhovnik

Horváth

2020

JID

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Conventionally, designing of a technical system is completed in the product development phase in terms of all details and aspects. This can be done if the expected behavior and the conditions of operation are known in advance and can be validated before the product is launched on the market. Validation is typically based on a predictive analysis or simulation of the designed operation. However, this contradicts with the case of smart systems, such as smart cyber-physical systems (CPSs), which self-manage their operation or at least a part of it. Being able to adapt at run-time and evolve over time, smart CPSs cannot be validated using conventional deterministic approaches. This is especially true for smart CPSs used as instrumentation in the medical field. This gave the stimulation of our background research, the results of which are concisely summarized and critically concluded in this paper. The literature has been found fairly narrow in terms of novel validation approaches for self-managing smart systems. The main finding is that the tasks of operational and behavioral validation should be shared among the system designers and the designed systems. Designers need prognostic approaches, while systems need to construct validation plans and execute them at run-time. This needs validation-specific functionalities and context dependent mechanisms such as run-time validation frameworks or meta-models, objective-sensitive self-monitoring mechanisms, self-constraining and self-supporting mechanisms, and other enablers. Extensive foundational research and system prototype testing are deemed to be indispensable. To make the first small step in this direction, this paper proposes a concept for validation of smart medical CPSs. This relies on the following hypothesis: If a system has freedom for self-adaptation, then it should also be equipped with selfcontrol mechanism, meta-knowledge and a supervisory controller. It all together enables a purpose-and context dependent semantic reasoning about the operational and behavioral objectives. The paper suggests a number of topics for future research towards a run-time validation engine.

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Section: Run-time Monitoring Of Operation Of Softwarementioning

confidence: 99%

From Validation of Medical Devices towards Validation of Adaptive Cyber-Physical Systems

Tavčar

Duhovnik

Horváth

2020

JID

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“…Multi-layered scheduling [37] Multi-layered scheduling scheme for time and control critical tasks. Adaptive Dynamic Scheduling for automotive CPS [38] Adapt. dynamic scheduling to adjust the exec.…”

Section: Workmentioning

confidence: 99%

“…In addition, the independent functioning of the nodes which could have their own workload makes those schemes difficult to apply in practice. The current proposals for CPS environments provide different contributions and aim to include additional methods to overcome these issues such as different criticality level of the tasks [37][38][39]47], mathematical techniques [27,35,44], agents [41], and control mechanisms [46]. Other works deal with network and physical dynamic issues [36,40,42,43].…”

Section: Workmentioning

confidence: 99%

Distributed computational model for shared processing on Cyber-Physical System environments

Mora

Colom

Gil

et al. 2017

Computer Communications

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  A computation model to distribute the application workload over the CPS is proposed.  The scheduling method dynamically shares the tasks among the computing nodes of the CPS.  This approach facilitates the networking and integration of heterogeneous computer devices.

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“…In Varvara (2016), a multi-agent systems for the semiconductor final testing secluding problem is designed on the basis of the information from CPPS. Xie et al (2017) propose an an automotive cyber-physical scheduling technique that is system-based as well as adaptive and dynamic for integrated automotive architecture. In general, the production system is a process-aware information system as it tries to manage and execute operational processes which involves people, production resources and flows, and information sources on the basis of process models (Ma 2010;Kriglstein et al 2016).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Scheduling Algorithm for Dynamic Jobs for Dealing with the Flexible Job Shop Scheduling Problem

Cao

Zhou

et al. 2019

Bus Inf Syst Eng

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Modern manufacturing systems build on an effective scheduling scheme that makes full use of the system resource to increase the production, in which an important aspect is how to minimize the makespan for a certain production task (i.e., the time that elapses from the start of work to the end) in order to achieve the economic profit. This can be a difficult problem, especially when the production flow is complicated and production tasks may suddenly change. As a consequence, exact approaches are not able to schedule the production in a short time. In this paper, an adaptive scheduling algorithm is proposed to address the makespan minimization in the dynamic job shop scheduling problem. Instead of a linear order, the directed acyclic graph is used to represent the complex precedence constraints among operations in jobs. Inspired by the heterogeneous earliest finish time (HEFT) algorithm, the adaptive scheduling algorithm can make some fast adaptations on the fly to accommodate new jobs which continuously arrive in a manufacturing system. The performance of the proposed adaptive HEFT algorithm is compared with other state-of-the-art algorithms and further heuristic methods for minimizing the makespan. Extensive experimental results demonstrate the high efficiency of the proposed approach.

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Adaptive Dynamic Scheduling on Multifunctional Mixed-Criticality Automotive Cyber-Physical Systems

Cited by 62 publications

References 44 publications

From Validation of Medical Devices towards Validation of Adaptive Cyber-Physical Systems

From Validation of Medical Devices towards Validation of Adaptive Cyber-Physical Systems

Distributed computational model for shared processing on Cyber-Physical System environments

An Adaptive Scheduling Algorithm for Dynamic Jobs for Dealing with the Flexible Job Shop Scheduling Problem

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