Genetic-algorithm-based real-time task scheduling with multiple goals

Oh, Jae‐Won; Wu, Chisu

doi:10.1016/s0164-1212(02)00147-4

Cited by 67 publications

(52 citation statements)

References 24 publications

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“…The first row of a chromosome represents the task number, the second row represents the processor number on which a task is to be scheduled, and the third row indicates the corresponding voltage level, as shown in Figure 4. In order to ensure that a schedule satisfies the precedence constraint, we schedule the tasks by their topological order [46]. For this purpose, we use the algorithm given in Figure 5 to generate the initial population.…”

Section: Solution Encoding and Generation Of The Initial Populationmentioning

confidence: 99%

“…Figure 6 shows two chromosomes generated by the algorithm for the given DAG (the vertical dotted lines show the segment boundaries). In order to ensure that a schedule satisfies the precedence constraint, we schedule the tasks by their topological order [46]. For this purpose, we use the algorithm given in Figure 5 to generate the initial population.…”

Section: Solution Encoding and Generation Of The Initial Populationmentioning

confidence: 99%

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Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm

et al. 2017

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Abstract:Minimizing power consumption to prolong battery life has become an important design issue for portable battery-operated devices such as smartphones and personal digital assistants (PDAs). On a Dynamic Voltage Scaling (DVS) enabled processor, power consumption can be reduced by scaling down the operating frequency of the processor whenever the full processing speed is not required. Real-time task scheduling is a complex and challenging problem for DVS-enabled multiprocessor systems. This paper first formulates the real-time task scheduling for DVS-enabled multiprocessor systems as a combinatorial optimization problem. It then proposes a genetic algorithm that is hybridized with the stochastic evolution algorithm to allocate and schedule real-time tasks with precedence constraints. It presents specialized crossover and perturb operations as well as a topology preserving algorithm to generate the initial population. A comprehensive simulation study has been done using synthetic and real benchmark data to evaluate the performance of the proposed Hybrid Genetic Algorithm (HGA) in terms of solution quality and efficiency. The performance of the proposed HGA has been compared with the genetic algorithm, particle swarm optimization, cuckoo search, and ant colony optimization. The simulation results show that HGA outperforms the other algorithms in terms of solution quality.

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Section: Solution Encoding and Generation Of The Initial Populationmentioning

confidence: 99%

Section: Solution Encoding and Generation Of The Initial Populationmentioning

confidence: 99%

Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm

et al. 2017

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“…However crossover can also result in the creation of schedules which are invalid. To convert an invalid schedule into a feasible solution we apply an adjustment after crossover has completed [21]. Mutation involves randomly altering the bit string altering aspects of a chromosome.…”

Section: A Genetic Algorithmsmentioning

confidence: 99%

A Learning Architecture for Scheduling Workflow Applications in the Cloud

Barrett

Howley

Duggan

2011

2011 IEEE Ninth European Conference on Web Services

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Abstract-The scheduling of workflow applications involves the mapping of individual workflow tasks to computational resources, based on a range of functional and non-functional quality of service requirements. Workflow applications require extensive computational requirements, and often involve the processing of significant amounts of data. Furthermore, dependencies that exist amongst tasks require that schedules must be generated strictly in accordance with defined precedence constraints. The emergence of cloud computing has introduced a utility-type market model, where computational resources of varying capacities can be procured on demand, in a pay-per-use fashion. In general the two most important objectives of workflow schedulers are the minimisation of both cost and makespan. As well as computational costs incurred from processing individual tasks, workflow schedulers must also plan for data transmission costs where potentially large amounts of data must be transferred between compute and storage sites. This paper proposes a novel cloud workflow scheduling approach which employs a Markov Decision Process to optimally guide the workflow execution process depending on environmental state. In addition the system employs a genetic algorithm to evolve workflow schedules. The overall architecture is presented, and initial results indicate the potential of this approach for developing viable workflow schedules on the Cloud.

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“…The task of scheduling tasks on a multiprocessor/distributed environment is NP-hard [12]. For this reason, various heuristics such as iterative improvement algorithms [7], and the probabilistic optimization as simulated annealing algorithms [10] and genetic algorithms [11] have been proposed. A middleware distributed real-time scheduling method is shown in [2].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Static Real-Time Scheduling Algorithms Using Graph Partitioning

Erciyes

Soysert

2005

Lecture Notes in Computer Science

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Abstract. We propose static task allocation algorithms for the periodic tasks of a distributed real-time system. The cyclic task consists of task threads which may communicate and share resources. A graph partitioning process and a thread sequencing algorithm are applied to these threads to yield local schedules. The exact analysis is then obtained and further refinements are performed if the worst case response time of a task is greater than its deadline.

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Genetic-algorithm-based real-time task scheduling with multiple goals

Cited by 67 publications

References 24 publications

Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm

Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm

A Learning Architecture for Scheduling Workflow Applications in the Cloud

Multilevel Static Real-Time Scheduling Algorithms Using Graph Partitioning

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