Task Parameter Impacts in Fuzzy Real Time Scheduling

Blej, Mohammed; Azizi, Mostafa

doi:10.1007/978-3-030-02155-9_6

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…Literature on fuzzy logic application to solve the RT scheduling problem is relatively new. (Saini, 2005;Vijayakumar & Aparna, 2010;Blej & Azizi, 2016).…”

Section: Fuzzy Logicmentioning

confidence: 99%

AI-Based Methods to Resolve Real-Time Scheduling for Embedded Systems

Boutekkouk

2022

International Journal of Cognitive Informatics and Natural Intelligence

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Artificial Intelligence is becoming more attractive to resolve nontrivial problems including the well known real time scheduling (RTS) problem for Embedded Systems (ES). The latter is considered as a hard multi-objective optimization problem because it must optimize at the same time three key conflictual objectives that are tasks deadlines guarantee, energy consumption reduction and reliability enhancement. In this paper, we firstly present the necessary background to well understand the problematic of RTS in the context of ES, then we present our enriched taxonomies for real time, energy and faults tolerance aware scheduling algorithms for ES. After that, we survey the most pertinent existing works of literature targeting the application of AI methods to resolve the RTS problem for ES notably Constraint Programming, Game theory, Machine learning, Fuzzy logic, Artificial Immune Systems, Cellular Automata, Evolutionary algorithms, Multi-agent Systems and Swarm Intelligence. We end this survey by a discussion putting the light on the main challenges and the future directions.

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“…Literature on fuzzy logic application to solve the RT scheduling problem is relatively new. (Saini, 2005;Vijayakumar & Aparna, 2010;Blej & Azizi, 2016).…”

Section: Fuzzy Logicmentioning

confidence: 99%

AI-Based Methods to Resolve Real-Time Scheduling for Embedded Systems

Boutekkouk

2022

International Journal of Cognitive Informatics and Natural Intelligence

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“…It is a hybrid scheduling approach in which a set of processing nodes represents a cluster and tasks assigned to the cluster can migrate and execute among the same cluster nodes (Sharma et al, 2020). Parallel and distributed computing is extensively used for applications with high computing and storage demands as mentioned in a recent publication (Blej and Azizi, 2019). Many technologies such as cloud computing, the Internet of Things, fog computing, Big data, machine learning used distributed and parallel computers for processing real-time data (Leng et al, 2020).…”

Section: Prior Workmentioning

confidence: 99%

Cluster Based Real Time Scheduling for Distributed System

Talmale¹,

Shrawankar²

2021

ADCAIJ

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Real time tasks scheduling on a distributed system is a complex problem. The existing real time tasks scheduling techniques are primarily based on partitioned and global scheduling. In partitioned based scheduling the tasks are assigned on a dedicated processor. The advantages of partitioned based approach is existing uni-processor scheduling techniques can be used; no migration overheads but task assignment is NP hard problem and optimal utilization of processing nodes is not possible. In global scheduling all tasks are maintained in a single tasks queue and allocated to multiple processing nodes. The advantage of global scheduling is optimal utilization of processing nodes but suffer from high migration and preemption overheads. This paper proposed cluster based real time tasks scheduling on a distributed system which is a hybrid scheduling approach where processing nodes group into cluster and scheduling using global scheduling. The simulation result shows that the proposed scheduling increases the tasks acceptance ratio, resource utilization as compared to partitioned and global scheduling and reduces migration as well as preemption overheads.

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“…In the task, the assignment decides the processor on which tasks get executes. The scheduling problem decides which tasks at what time will execute on the processor, that is, priority of tasks 4 . Task assignment problem further divided into two type's static and dynamic task assignment.…”

Section: Introductionmentioning

confidence: 99%

Cluster formation techniques for hierarchical real time tasks allocation on multiprocessor system

Talmale

Shrawankar

2021

Concurrency and Computation

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Summary Real‐time systems are nowadays extensively used in most time‐critical embedded applications. The existing multiprocessor real‐time scheduling algorithms are based on partitioned and global scheduling approaches. The partitioned‐based algorithms suffer poor utilization bound, load balancing, not compatible for an open system environment, and the global scheduling approach faces problems like high scheduling, migration overhead. Cluster scheduling represents a hybrid scheduling approach which consists of a set of processors as clusters and tasks scheduled to each processor of clusters using a global scheduling approach. The different cluster formation heuristics are investigated for homogenous clusters. The main objective of this article is to propose task utilization‐based cluster formation and harmonic period aware task allocation for a hierarchical real‐time system on a multiprocessor platform. Tasks with high utilization, cluster size tune to a smaller value to achieve high utilization. Tasks with low utilization, the size of cluster tune to larger to reduce migration and scheduling overheads. Experimentation performed on multiprocessor real‐time simulator with a different set of tasks, multiprocessor system, different tasks utilization, and cluster size. Simulation results show that cluster base approach increases the number of tasks scheduled on a multiprocessor system, system utilization, and reduces response time, migration, and preemption overheads.

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Task Parameter Impacts in Fuzzy Real Time Scheduling

Cited by 8 publications

References 22 publications

AI-Based Methods to Resolve Real-Time Scheduling for Embedded Systems

AI-Based Methods to Resolve Real-Time Scheduling for Embedded Systems

Cluster Based Real Time Scheduling for Distributed System

Cluster formation techniques for hierarchical real time tasks allocation on multiprocessor system

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