Solving Energy-Aware Real-Time Tasks Scheduling Problem with Shuffled Frog Leaping Algorithm on Heterogeneous Platforms

Zhang, Weizhe; Bai, Enci; He, Hui; Cheng, Albert M. K.

doi:10.3390/s150613778

Cited by 31 publications

(18 citation statements)

References 34 publications

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“…We used the technique proposed by Balbastre et al [48] to assign the deadlines to each task in the task graph. The real benchmark data was taken from [49][50][51].…”

Section: Resultsmentioning

confidence: 99%

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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“…We used the technique proposed by Balbastre et al [48] to assign the deadlines to each task in the task graph. The real benchmark data was taken from [49][50][51].…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2017

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“…However, scheduling cost grows exponentially with the larger scheduling flexibility. To alleviate this issue, a heuristics approach is proposed [14].…”

Section: Related Workmentioning

confidence: 99%

An Energy-Efficient Task Scheduling for Near Real-Time Systems on Heterogeneous Multicore Processors

Nakada

Yanagihashi

Imai³

et al. 2020

IEICE Trans. Inf. & Syst.

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Near real-time periodic tasks, which are popular in multimedia streaming applications, have deadline periods that are longer than the input intervals thanks to buffering. For such applications, the conventional frame-based schedulings cannot realize the optimal scheduling due to their shortsighted deadline assumptions. To realize globally energy-efficient executions of these applications, we propose a novel task scheduling algorithm, which takes advantage of the long deadline period. We confirm our approach can take advantage of the longer deadline period and reduce the average power consumption by up to 18%.

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“…The key idea of their approach is to duplicate some tasks to reduce the communication energy as well as traffic congestion. Zhang et al [45] propose an ILP-based, energy-aware task mapping algorithm on heterogeneous multi-processors, and an evolutionary algorithm-based, energy-efficient task mapping heuristic. Cai et al [6] propose an energy efficient approach for heterogeneous multi-processor mobile embedded systems.…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Scheduling of Conditional Task Graphs on NoC-Based MPSoCs

Tariq¹,

Ishak²

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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We investigate the problem of scheduling a set of tasks with individual deadlines and conditional precedence constraints on a heterogeneous Network on Chip (NoC)-based Multi-Processor System-on-Chip (MPSoC) such that the total expected energy consumption of all the tasks is minimized, and propose a novel approach. Our approach consists of a scheduling heuristic for constructing a single unified schedule for all the tasks and assigning a frequency to each task and each communication assuming continuous frequencies, an Integer Linear Programming (ILP)-based algorithm and a polynomial time heuristic for assigning discrete frequencies and voltages to tasks and communications. We have performed experiments on 16 synthetic and 4 real-world benchmarks. The experimental results show that compared to the state-of-theart approach, our approach using the ILP-based algorithm and our approach using the polynomial-time heuristic achieve average improvements of 31% and 20%, respectively, in terms of energy reduction. compared to v j and traverse the same links that v j traverses. 4) If v j is a task node, compute its finish time ζ j = r j + t k,j , and insert unconditional directed edges from v j to unscheduled nodes concurrent to v j and mapped on the same processor where v j is mapped. 5) Delete v j from ReadySet and insert all ready nodes in G to ReadySet. Consider the CTG in Figure 1(b) and the MPSoC in Figure 2(a) where all the processors are identical. The execution times of tasks at the maximum processor frequency are t 1,1

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Solving Energy-Aware Real-Time Tasks Scheduling Problem with Shuffled Frog Leaping Algorithm on Heterogeneous Platforms

Cited by 31 publications

References 34 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

An Energy-Efficient Task Scheduling for Near Real-Time Systems on Heterogeneous Multicore Processors

Energy-Aware Scheduling of Conditional Task Graphs on NoC-Based MPSoCs

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