Energy-efficient task scheduling for multi-core platforms with per-core DVFS

Lin, Ching-Chi; Syu, You-Cheng; Chang, Chao-Jui; Wu, Jan‐Jan; Liu, Pangfeng; Cheng, Po-Wen; Hsu, Wei-Feng

doi:10.1016/j.jpdc.2015.08.004

Cited by 30 publications

(11 citation statements)

References 13 publications

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“…It changes the status of the processor according to the remainder energy, the processor operates on max frequency if there are enough energy, otherwise, lower frequency will be used to save energy. A variable-aware DVFS method was proposed in [9], in which the status of the processor was changed according to the variables of voltage, temperature, process parameter and so on, rather than using frequency threshold and greedy policy. In [10] the models were made for both homogeneous and heterogenous processors, the authors tried to reduce the dynamic power of the processor, and compared the dynamic energy consumption for processing tasks on the two kinds of processors.…”

Section: Related Workmentioning

confidence: 99%

“…A cost aware scheduling algorithm was proposed in [7], the cost of the server for processing a task contains the energy cost and temporal cost. Two task execution modes, the batch mode and the online mode, are considered, and heuristics are developed.…”

Section: Related Workmentioning

confidence: 99%

“…Once the tasks have been allocated, we should sort the tasks on each active core with non-decreasing WCET, and the frequency of each core can be computed according to Eq. (7) and Eq. (8).…”

Section: ( )mentioning

confidence: 99%

“…But both the energy cost and the time cost should be reduced simultaneously for the service provider to gain higher profit. A cost-aware scheduling method was proposed in [7], but it only suits for the processor in which each core operates on independent frequency.…”

Section: Introductionmentioning

confidence: 99%

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Cost-Aware Scheduling of Computation-Intensive Tasks on Multi-Core Server

2018

TIIS

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Energy-efficient task scheduling on multi-core server is a fundamental issue in green cloud computing. Multi-core processors are widely used in mobile devices, personal computers, and servers. Existing energy efficient task scheduling methods chiefly focus on reducing the energy consumption of the processor itself, and assume that the cores of the processor are controlled independently. However, the cores of some processors in the market are divided into several voltage islands, in each of which the cores must operate on the same status, and the cost of the server includes not only energy cost of the processor but also the energy of other components of the server and the cost of user waiting time. In this paper, we propose a cost-aware scheduling algorithm ICAS for computation intensive tasks on multi-core server. Tasks are first allocated to cores, and optimal frequency of each core is computed, and the frequency of each voltage island is finally determined. The experiments' results show the cost of ICAS is much lower than the existing method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Once the tasks have been allocated, we should sort the tasks on each active core with non-decreasing WCET, and the frequency of each core can be computed according to Eq. (7) and Eq. (8).…”

Section: ( )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cost-Aware Scheduling of Computation-Intensive Tasks on Multi-Core Server

2018

TIIS

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“…Lin et al applied DVFS in core level to optimize energy consumption. Dynamic voltage and frequency scaling is applied in private Clouds by Kim, Buyya, and Kim where they devised mapping policies for executing tasks with deadline in private Clouds to improve power consumption and task acceptance rate.…”

Section: Related Workmentioning

confidence: 99%

Energy‐efficient adaptive virtual machine migration mechanism for private Clouds

Sohrabi

Yang

Moser

et al. 2017

Concurrency and Computation

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Summary Private Cloud provides Cloud services with its relatively limited resources compared to public Clouds. Resources in private Clouds should be used energy efficiently. The resource utilization is determined by the assignment of virtual machines (VMs) to hosts. Because of the frequent changes in the resource requests on VMs, the system might become imbalanced, some hosts are overloaded/underloaded. Virtual machine migration is a solution to ease the imbalance problem. Virtual machine migration includes the selection of a VM for migration and decision upon where it should be taken to (mapped). In this work, VM selection and VM mapping are integrated and aimed to ease the imbalance problem for energy efficiency. Moreover, selection and mapping have become adaptive to ease imbalance, while optimizing energy consumption and adaptively responding to changes in the system. Our proposed adaptive mechanism applies Bayesian inference to estimate the likelihood of a VM migration decision, both VM selection for migration and VM mapping, optimizing energy consumption. The proposed mechanism is evaluated on CloudSim using PlanetLab workload on a heterogeneous Cloud. It is demonstrated to reduce energy consumption significantly, (on average) by 116%, while its total execution time is also, (on average) 5.39 times, shorter than the competing state‐of‐the‐art policies.

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Energy-Aware Task Scheduling Approach Using DVFS and Particle Swarm Optimization for Heterogeneous Multicore Processors

Jayaramaiah²

2021

Emerging Research in Computing, Information, Communication and Applications

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Energy-efficient task scheduling for multi-core platforms with per-core DVFS

Cited by 30 publications

References 13 publications

Cost-Aware Scheduling of Computation-Intensive Tasks on Multi-Core Server

Cost-Aware Scheduling of Computation-Intensive Tasks on Multi-Core Server

Energy‐efficient adaptive virtual machine migration mechanism for private Clouds

Energy-Aware Task Scheduling Approach Using DVFS and Particle Swarm Optimization for Heterogeneous Multicore Processors

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