Energy-Efficient Intra-task DVFS Scheduling Using Linear Programming Formulation

Qin, Yang; Zeng, Gang; Kurachi, Ryo; Li, Yixiao; Matsubara, Y.; Takada, Hiroaki

doi:10.1109/access.2019.2902353

Cited by 31 publications

(11 citation statements)

References 28 publications

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“…erefore, it is necessary to further analyze and design the middle classification of the initial class framework according to the situation. e optimization method is used to optimize the structure [19][20][21]. Task assignment class is the core process of collaborative task scheduling.…”

Section: Complexitymentioning

confidence: 99%

Optimization and Simulation of a Reasonable Scheduling Model under Multiple Tasks in Company Management

Wang

2020

Complexity

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With the rapid development of market economy, the task scheduling model has become the core problem in the field of corporate management. In order to solve the problem that the stability of the model will decline due to the interference of human factors in the process of multitask scheduling in the traditional algorithm, a reasonable scheduling model based on the priority principle is proposed in this paper. This paper expounds the principle of multitask scheduling in company management, constructs a multitask scheduling network model based on priority, performs virtual scheduling on the model, searches the optimal solution in the solution space of the scheduling problem, and obtains the reasonable multitask scheduling method in the company management. Through the analysis of relevant simulation experiments, it can be concluded that the task scheduling algorithm proposed in this paper can not only allocate the corresponding resources for high-priority tasks effectively, but also can save the cost, so as to show better execution efficiency.

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Section: Complexitymentioning

confidence: 99%

Optimization and Simulation of a Reasonable Scheduling Model under Multiple Tasks in Company Management

Wang

2020

Complexity

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“…Zong et al proposed two energy aware job scheduling algorithms (EAD and PEBD) based on job duplication strategy for homogeneous clusters [18]. Chen et al established a hyper-heuristic framework and proposed a quantum-inspired high-level learning strategy to solve performance-constrained energy optimization problem [19] On the other hand, there are many studies that focus on optimizing the energy consumption and the above other objectives [12], [15], [20]. Huang et al focus on co-management of system reliability and energy consumption, and propose an energy-efficient fault-tolerant scheduling algorithm [20].…”

Section: Related Workmentioning

confidence: 99%

“…Tarplee et al adopted linear programming methods to obtain good tradeoff between application makespan and heterogeneous computing systems energy consumption [37]. To guarantee task deadline and reduce energy consumption, Qin et al use integer linear programming to assign individual basic blocks by a proper operational frequencies [12]. These research works demonstrate that the optimal scheduling strategy can effectively reduce system energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, techniques such as dynamic power management, dynamic voltage-frequency scaling (DVS), slack reclamation, resource hibernation, and so on, have been successfully applied in reducing the energy consumption of single computing resource [9]- [11]. These approaches are incorporated into job scheduling strategy to solve heterogeneous computing systems energy consumption optimization problem [12], [13]. However, most of them are not effectively deal with the relationship between resource (CPU-GPU) utilization dynamic variability and energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

Tang

2020

IEEE Access

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Nowadays, heterogeneous computing systems have proven to be a good solution for processing computation intensive high-performance applications. The main challenges for such large-scale systems are energy consumption, computing node CPU-GPU utilization dynamic variability, and so on. In response to these challenges, this study first provides heterogeneous computing systems architecture and parallel application job model. Then, we build system computing node CPU-GPU utilization model and analyze job execution energy consumption. We also deduce the optimal CPU-GPU utilization and job deadline scheduling constraint. Third, we propose a systems CPU-GPU utilization aware and energy-efficient heuristic greedy strategy (UEJS) to solve this job scheduling problem. To improve the algorithm global optimization ability, we design a hybrid particle swarm optimization algorithm (H-PSO), which incorporates the heuristic greedy strategy into the bio-inspired search optimization technique. The rigorous experimental results clearly demonstrate that our proposed H-PSO outperforms heuristic greedy strategy, Max-EAMin, and Genetic Algorithm in terms of the average energy consumption of jobs and system job rejection rate. In particular, H-PSO is better than UEJS by 36.5%, Max-EAMin by 36.3%, and GA by 46.7% in term of the job average energy consumption for heterogeneous system with high workload.

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“…The DV/FS scheduling problem can be used to determine the optimum execution frequency for each core [3]. While greedy techniques predict local optimal Vmin/F levels to each core [4]. In [5], a heuristic approach is introduced which uses the Dynamic Programming (DP) technique to speed up the DV/FS run-time.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Reliability Aware and Energy-Efficiency in Multicore Systems

Nour¹,

Salem²,

Habashy³

2022

Computers, Materials &Amp; Continua

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Recently, Multicore systems use Dynamic Voltage/Frequency Scaling (DV/FS) technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance. In this paper, an effective and reliable hybrid model to reduce the energy and makespan in multicore systems is proposed. The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency (Vmin/F) levels. Then, the allocation process is applied based on the available workloads. The hybrid model consists of three stages. The first stage gets the optimum safe voltage while the second stage sets the level of energy efficiency, and finally, the third is the allocation stage. Experimental results on various benchmarks show that the proposed model can generate optimal solutions to save energy while minimizing the makespan penalty. Comparisons with other competitive algorithms show that the proposed model provides on average 48% improvements in energy-saving and achieves an 18% reduction in computation time while ensuring a high degree of system reliability.

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Energy-Efficient Intra-task DVFS Scheduling Using Linear Programming Formulation

Cited by 31 publications

References 28 publications

Optimization and Simulation of a Reasonable Scheduling Model under Multiple Tasks in Company Management

Optimization and Simulation of a Reasonable Scheduling Model under Multiple Tasks in Company Management

CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

A Hybrid Model for Reliability Aware and Energy-Efficiency in Multicore Systems

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