Partitioned mixed-criticality scheduling on multiprocessor platforms

Gu, Chuancai; Guan, Nan; Deng, Qingxu; Wang, Yi

doi:10.7873/date.2014.305

Cited by 13 publications

(29 citation statements)

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“…• E-MPVD (Enhanced-MPVD): MPVD enhanced by the heavy low-criticality task cognizant assignment strategy in [15]; and…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…• MPVD (Mixed-criticality Partitioning with Virtual Deadlines): a further extension of EY algorithm with the hybrid packing model from [15];…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…• EY-FF: an enhanced Ekberg and Yi (EY) algorithm [13] exploiting First-Fit (FF) packing policy from [15];…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Later, Gu et al [15] extended the work on uniprocessor MC scheduling [16] to multicore systems and presented two improvements for processor schedulability by exploiting criticality-cognizance policy. Mollison et al developed a scheduling heuristic for MC tasks on multicore platforms, implementing various methods (i.e., Cyclic Executive, partitioned-EDF, global-EDF and global best effort) for five different criticalities and enabling temporal isolation between tasks through a bandwidth reservation server [17].…”

Section: Related Workmentioning

confidence: 99%

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Criticality-cognizant Clustering-based Task Scheduling on Multicore Processors in the Avionics Domain

Nagalakshmi¹,

Gomathi²

2018

IJCIS

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Scheduling of mixed-criticality systems (MCS) on a common computational platform is challenging because conventional scheduling approaches may cause inefficient utilization of shared computing resources. In this paper, we propose an approach called Clustering-based Partitioned Earliest Deadline First (C-PEDF) algorithm to schedule dual-criticality implicit-deadline sporadic tasks on a homogeneous multicore system. Our C-PEDF scheduling approach exploits (i) a Clustering-based bin-packing algorithm that explicitly accounts the demands of tasks based on their levels of confidence; and (ii) an Enhanced dual-mode scheduling policy to schedule tasks within a core. The proposed C-PEDF integrates every single high-level workload with a group of low-level workloads and coalesces them into a cluster. Within each cluster, tasks are scheduled under our Enhanced dualmode scheduling policy to improve the service level of high-level tasks without jeopardizing the schedulability of low-level tasks. Clusters are scheduled under Earliest Deadline First (EDF) scheduling approach. We conduct a schedulability test for the proposed technique, and we demonstrate how workloads can be clustered by means of Mixed Integer Nonlinear Programming (MINLP) model. Extensive simulation results reveal that our algorithm significantly outperforms other existing approaches both in acceptance ratio and the impact factor of low-level tasks.

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“…• E-MPVD (Enhanced-MPVD): MPVD enhanced by the heavy low-criticality task cognizant assignment strategy in [15]; and…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…• MPVD (Mixed-criticality Partitioning with Virtual Deadlines): a further extension of EY algorithm with the hybrid packing model from [15];…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…• EY-FF: an enhanced Ekberg and Yi (EY) algorithm [13] exploiting First-Fit (FF) packing policy from [15];…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Criticality-cognizant Clustering-based Task Scheduling on Multicore Processors in the Avionics Domain

Nagalakshmi¹,

Gomathi²

2018

IJCIS

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“…To address the effects of the physical environment on the system, Niz et al [11] presented a partitioned multiprocessor scheduling scheme based on a multi-mode extension of the zero slack rate-monotonic scheduling. Gu et al [24] extended the demand-based single-processor mixed-criticality scheduling [25] (called EY in this paper) to multiprocessor platforms and proposed two enhancements based on heavy low-criticality task awareness and balance factor to improve the system schedulability. However, these existing solutions assume that, whenever a job of a high-criticality task exhibits high-criticality behavior, all current and future jobs of every high-criticality task in the system will exhibit high-criticality behavior and thus all low-criticality tasks will be dropped.…”

Section: Related Workmentioning

confidence: 99%

Mixed-Criticality Scheduling on Multiprocessors Using Task Grouping

Ren

Phan

2015

2015 27th Euromicro Conference on Real-Time Systems

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Real-time systems are increasingly running a mix of tasks with different criticality levels: for instance, unmanned aerial vehicle has multiple software functions with different safety criticality levels, but runs them on a single, shared computational platform. In addition, these systems are increasingly deployed on multiprocessor platforms because this can help to reduce their cost, space, weight, and power consumption. To assure the safety of such systems, several mixed-criticality scheduling algorithms have been developed that can provide mixed-criticality timing guarantees. However, most existing algorithms have two important limitations: they do not guarantee strong isolation among the high-criticality tasks, and they offer poor realtime performance for the low-criticality tasks. Abstract-Real-time systems are increasingly running a mix of tasks with different criticality levels: for instance, unmanned aerial vehicle has multiple software functions with different safety criticality levels, but runs them on a single, shared computational platform. In addition, these systems are increasingly deployed on multiprocessor platforms because this can help to reduce their cost, space, weight, and power consumption. To assure the safety of such systems, several mixed-criticality scheduling algorithms have been developed that can provide mixed-criticality timing guarantees. However, most existing algorithms have two important limitations: they do not guarantee strong isolation among the high-criticality tasks, and they offer poor real-time performance for the low-criticality tasks. Disciplines Computer Engineering | Computer SciencesIn this paper, we present a partitioned scheduling scheme for mixed-criticality tasks on multiprocessor platforms that addresses both issues. Our scheduling scheme consists of (i) a task-toprocessor packing algorithm that takes into account the demands of tasks with respect to their criticality levels, and (ii) a mixedcriticality uniprocessor scheduling strategy that is based on task grouping. Our strategy associates each high-criticality task with a subset of the low-criticality tasks and encapsulates them in a task group, which is scheduled with the other task groups under the Earliest Deadline First (EDF) policy. Within each task group, the low-criticality task and the high-criticality tasks are scheduled using a server-based strategy, so as to enable more of the former to meet their deadlines without affecting the latter. We present a schedulability analysis for our scheduling strategy, and we show how tasks can be grouped using Mixed Integer Nonlinear Programming. Our evaluation shows that our proposed scheme significantly outperforms existing partitioned mixed-criticality scheduling algorithms, in terms of both the fraction of schedulable task sets and its ability to schedule low-criticality tasks.

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Isolation scheduling on multicores: model and scheduling approaches

et al. 2017

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Deploying real-time applications on multicores is challenging because tasks that are executed concurrently on different cores can interfere on shared resources, severely complicating worst-case timing analysis. To tackle this challenge, we propose a new scheduling model called isolation scheduling (IS): IS provides a framework to exploit multicores for real-time applications where tasks are grouped into classes. IS enforces mutually exclusive execution among different task classes, thus eliminating inter-class interference by construction. We assume that interference due to the statefulness of shared resources is either negligible or accounted for in the worstcase execution time of tasks. Mixed-criticality systems provide an example where IS is applicable. We propose and analyze two novel approaches for isolation scheduling: a global approach based on fluid scheduling and a partitioned approach based on hierarchical server scheduling, each with extensions to mixed-criticality applications. Through extensive simulations, we compare the two approaches in terms of B Georgia Giannopoulou

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Partitioned mixed-criticality scheduling on multiprocessor platforms

Cited by 13 publications

References 0 publications

Criticality-cognizant Clustering-based Task Scheduling on Multicore Processors in the Avionics Domain

Criticality-cognizant Clustering-based Task Scheduling on Multicore Processors in the Avionics Domain

Mixed-Criticality Scheduling on Multiprocessors Using Task Grouping

Isolation scheduling on multicores: model and scheduling approaches

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