A Practical Dynamic Frequency Scaling Scheduling Algorithm for General Purpose Embedded Operating System

Chen, Tianzhou; Huang, Jiangwei; Liangxiang, Xiang; Zhenwei, Zheng

doi:10.1109/fgcn.2008.133

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…There have been extensive works contributing to the problem in different fields. Works based on DVFS technique have been applied in various systems, including mobile [12] [16], embedded [17], single processor or server [9][15] and also on cluster of servers [4][8] and so on. In high performance computing fields, Saurabh Kumar Garg et al [8] use DVFS techniques to reduce energy consumption among geographically distributed cloud data centers.…”

Section: Related Workmentioning

confidence: 99%

Towards Energy Efficient Scheduling for Online Tasks in Cloud Data Centers Based on DVFS

Huai

Huang

Jin

et al. 2015

2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing

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Energy efficient task scheduling is an important issue in cloud data centers. Dynamic Voltage Frequency Scaling (DVFS), which can make the processors work at suitable frequency, is an effective method to achieve power saving since the frequency could be automatically adjusted dynamically. However, the existing DVFS-oriented performance model does not suit many applications' computing paradigm in the cloud data centers. Meanwhile, the existing DVFS-oriented power consumption model have a lack of accuracy, and this situation makes it inefficient to achieve power saving. In this paper, we conduct extensive experiments in a real cluster testbed, and propose new DVFS-oriented performance and power consumption models, while taking into account both the frequency and utilization of the processors. Based on the models, we present a Power-aware Threshold Unit (PTU) algorithm to schedule the online tasks dynamically in cloud data center. The PTU algorithm is based on the fact that data centers are organized by rack-sized unit. The basic idea is to make a tradeoff between the power consumption and set up time of serves under a designated granularity. To the best of our knowledge, we are the first to propose the new characterization models and problem. We carry out extensive real experiments on a cluster which consists of several multicore servers, and the results show that the new DVFS-oriented performance and power consumption models are accurate. The experiment results show that our PTU algorithm can achieve considerable energy saving.

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Section: Related Workmentioning

confidence: 99%

Towards Energy Efficient Scheduling for Online Tasks in Cloud Data Centers Based on DVFS

Huai

Huang

Jin

et al. 2015

2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing

View full text Add to dashboard Cite

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“…The first strategy is scaling voltage and frequency at task slack time. When a processor serves a task, the operating frequency is multiplied by the rate between the worst-case execution time (WCET) and the deadline of the task [ 28 ] to reduce power consumption, as shown in Figure 1 . Shin et al [ 29 ] combined offline and online components to satisfy the time constraints and reduce energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…To extend the system lifetime for energy-limited devices, one of the possible ways is to use DVFS-based technology [ 28 , 29 , 31 , 32 ] to save energy. Because the requirements will change when the real-time system is being used, other studies [ 27 , 42 , 43 ] have combined DVFS technology and a real-time scheduler to meet the time constraint while reducing energy consumption.…”

Section: Related Workmentioning

confidence: 99%

A High Performance Load Balance Strategy for Real-Time Multicore Systems

Cho

Tsai

Chiu

et al. 2014

The Scientific World Journal

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Finding ways to distribute workloads to each processor core and efficiently reduce power consumption is of vital importance, especially for real-time systems. In this paper, a novel scheduling algorithm is proposed for real-time multicore systems to balance the computation loads and save power. The developed algorithm simultaneously considers multiple criteria, a novel factor, and task deadline, and is called power and deadline-aware multicore scheduling (PDAMS). Experiment results show that the proposed algorithm can greatly reduce energy consumption by up to 54.2% and the deadline times missed, as compared to the other scheduling algorithms outlined in this paper.

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“…In the realm of industrial and engineering, time-delay as presented [10][11] and nonlinear as presented [12] are always encountered such as robotic hands with flexible nexus component, biology procedure and nuclear reactor control, etc An example is the control of density, temperature, current and momentum spatial profiles in tokamak plasmas, whose dynamics are ruled by a set of nonlinear parabolic PDEs as presented [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Singular Distributed Parameter System Iterative Learning Control with Forgetting Factor with Time- Delay

Zhang¹,

Li²,

Su³

2016

IJUNESST

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A Practical Dynamic Frequency Scaling Scheduling Algorithm for General Purpose Embedded Operating System

Cited by 3 publications

References 9 publications

Towards Energy Efficient Scheduling for Online Tasks in Cloud Data Centers Based on DVFS

Towards Energy Efficient Scheduling for Online Tasks in Cloud Data Centers Based on DVFS

A High Performance Load Balance Strategy for Real-Time Multicore Systems

Singular Distributed Parameter System Iterative Learning Control with Forgetting Factor with Time- Delay

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