Towards mixed criticality task scheduling in cyber physical systems: Challenges and perspectives

Capota, Eugenia Ana; Stangaciu, Cristina S.; Micea, Mihai V.; Curiac, Daniel-Ioan

doi:10.1016/j.jss.2019.06.099

Cited by 22 publications

(8 citation statements)

References 28 publications

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“…Real-time schedulers like earliest deadline first (EDF) and deadline monotonic (DM), in contrast, cater to real-time systems where all tasks have a fixed priority and the criticality of tasks is not considered [23]. However, AVs are categorized as cyber-physical systems and require schedulers that can schedule for mixed criticalities and multiple DAGs [18]. In this regard, Xie et al [57] propose two dynamic schedulers; fairness-based dynamic scheduler (FDS MIMF) and an adaptive dynamic scheduler (ADS MIMF), to optimize the makespan of the DAGs and to achieve low deadline miss ratio (DMR) by considering safety and criticality of the system values for high-criticality DAGs, respectively.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneity-Aware Scheduling on SoCs for Autonomous Vehicles

Amarnath

Pal

Kassa

et al. 2021

IEEE Comput. Arch. Lett.

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Systems-on-Chips (SoCs) that power autonomous vehicles (AVs) must meet stringent performance and safety requirements prior to deployment. With increasing complexity in AV applications, the system needs to meet stringent real-time demands of multiple safety-critical applications simultaneously. A typical AV-SoC is a heterogeneous multiprocessor consisting of accelerators supported by general-purpose cores. Such heterogeneity, while needed for power-performance efficiency, complicates the art of task (process) scheduling.In this paper, we demonstrate that hardware heterogeneity impacts the scheduler's effectiveness and that optimizing for only the real-time aspect of applications is not sufficient in AVs. Therefore, a more holistic approach is required -one that considers global Quality-of-Mission (QoM) metrics, as defined in the paper. We then propose HetSched, a multi-step scheduler that leverages dynamic runtime information about the underlying heterogeneous hardware platform, along with the applications' real-time constraints and the task traffic in the system to optimize overall mission performance. HetSched proposes two scheduling policies: M Sstat and M S dyn and scheduling optimizations like task pruning, hybrid heterogeneous ranking and rank update. HetSched improves overall mission performance on average by 4.6×, 2.6× and 2.6× when compared against CPATH, ADS and 2lvl-EDF (state-of-the-art real-time schedulers built for heterogeneous systems), respectively, and achieves an average of 53.3% higher hardware utilization, while meeting 100% critical deadlines for real-world applications of autonomous driving and aerial vehicles. Furthermore, when used as part of an SoC design space exploration loop, in comparison to the prior schedulers, HetSched reduces the number of processing elements required by an SoC to safely complete AV's missions by 35% on average while achieving 2.7× lower energy-mission time product.

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Section: Related Workmentioning

confidence: 99%

Heterogeneity-Aware Scheduling on SoCs for Autonomous Vehicles

Amarnath

Pal

Kassa

et al. 2021

IEEE Comput. Arch. Lett.

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“…For CPS scheduling for tuning the variable, soft computing is adapted [24]. Mixed criticality scheduling algorithms must be used for handling both cyber and physical parameter effectively [25]. Other criteria of the scheduling problem in CPS are adaptive energy-efficient scheduling [26].…”

Section: Introductionmentioning

confidence: 99%

Algorithm fuzzy scheduling (AFS) for realtime jobs on multiprocessor systems

Holagundi¹,

Ashwathsetty²,

Basthikodi

2022

IJEECS

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The computing in Real-time is rapidly focusing much developments in technologies so that the real-time jobs are to be scheduled and executed on computing systems in particular time frame. The scheduling and load balancing techniques in distributed systems face numerous challenges because of lack of centralized strategy to dispatch the jobs in multiprocessors systems. In this work, we propose an Algorithm Fuzzy Scheduling (AFS) for real-time jobs that includes of Arrival time, Deadline and Computation time as the scheduling parameters of input. The approach AFS is analyzed and compared with Existing Fuzzy Algorithm (EFA) model for evaluation of performances from the outcome of the simulation. The jobs are scheduled on multiprocessor at higher system load by making use of fuzzy mechanisms in the algorithms. The experimental results prove that the proposed AFS achieves a better performance comparatively to EFA at various system load factors with respect to mean turnaroundtime, mean response time and count of missed deadlines. This is the initial phase of the algorithm, that will be enhanced to consider a greater number of parameters to be associated with jobs for better decision making and to investigate the scope for algorithm level parallelism.

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“…Moreover, the conception of Cyber-Physical Systems (CPS) [ 30 , 31 , 32 ], Mixed-Criticality Systems (MCS) [ 33 ] or Industrial Cyber-Physical Systems (ICPS) [ 34 ] have prompted a change in the definition of systems, their monitoring and study to obtain the best information and interaction in real-time between a physical system and the monitoring, data computation, communication and interrelation with other systems [ 35 ]. K Meng’s paper, called Smart Recovery Decision-Making (SRDM) [ 36 ], uses data computation for end-of-life prediction of products.…”

Section: Introductionmentioning

confidence: 99%

Maintenance Strategies for Industrial Multi-Stage Machines: The Study of a Thermoforming Machine

García

Salgado

2021

Sensors

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The study of reliability, availability and control of industrial manufacturing machines is a constant challenge in the industrial environment. This paper compares the results offered by several maintenance strategies for multi-stage industrial manufacturing machines by analysing a real case of a multi-stage thermoforming machine. Specifically, two strategies based on preventive maintenance, Preventive Programming Maintenance (PPM) and Improve Preventive Programming Maintenance (IPPM) are compared with two new strategies based on predictive maintenance, namely Algorithm Life Optimisation Programming (ALOP) and Digital Behaviour Twin (DBT). The condition of machine components can be assessed with the latter two proposals (ALOP and DBT) using sensors and algorithms, thus providing a warning value for early decision-making before unexpected faults occur. The study shows that the ALOP and DBT models detect unexpected failures early enough, while the PPM and IPPM strategies warn of scheduled component replacement at the end of their life cycle. The ALOP and DBT strategies algorithms can also be valid for managing the maintenance of other multi-stage industrial manufacturing machines. The authors consider that the combination of preventive and predictive maintenance strategies may be an ideal approach because operating conditions affect the mechanical, electrical, electronic and pneumatic components of multi-stage industrial manufacturing machines differently.

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Towards mixed criticality task scheduling in cyber physical systems: Challenges and perspectives

Cited by 22 publications

References 28 publications

Heterogeneity-Aware Scheduling on SoCs for Autonomous Vehicles

Heterogeneity-Aware Scheduling on SoCs for Autonomous Vehicles

Algorithm fuzzy scheduling (AFS) for realtime jobs on multiprocessor systems

Maintenance Strategies for Industrial Multi-Stage Machines: The Study of a Thermoforming Machine

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