EAD and PEBD: Two Energy-Aware Duplication Scheduling Algorithms for Parallel Tasks on Homogeneous Clusters

Zong, Ziliang; Manzanares, Adam; Ruan, Xiaojun; Qin, Xiao

doi:10.1109/tc.2010.216

Cited by 129 publications

(66 citation statements)

References 35 publications

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“…The second category uses compiler or application assisted information to guide DVFS scheduling for energy saving. In [5], the author proposes energy management of application layer by using information provided by the application. In [6], authors propose the compiler assisted DVFS and insert relevant DVFS instruction into the program to realize the purpose of saving energy depending on the detected type of the program that compiler provided.…”

Section: Related Workmentioning

confidence: 99%

A Modeling Approach for Energy Saving Based on GA-BP Neural Network

Li¹,

Guo²,

Shen³

et al. 2016

Journal of Electrical Engineering and Technology

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-To cope with the increasing scale of scientific data and computational complexity of daily data, more and more cores have been integrated into GPU(Graphic Processing Units) and its working frequency is continually upgrading, which makes it being widely used in general computing for assisting CPU to accelerate program. While GPU offers powerful computing capability, the problem of the energy consumption becomes particularly prominently and it has become one of the important issues hindering development of GPU. For the purpose of solving this problem, DVFS (Dynamic Voltage Frequency Scaling) becomes an effective solution. Because the previous works only focus on single component and use linear relationship to do DVFS without considering energy saving of other units in system at software runtime, therefore we propose an energy saving model (CDVFS) of considering the characteristics of both GPU and memory at software runtime based on GA-BP (Genetic Algorithm-Back propagation) neural network to make better use of the relationship between components for energy saving. Firstly, the model assumes that functional relation between the software runtime characteristics of GPU and memory and the appropriate frequency which corresponds to the GPU and memory as nonlinear. Secondly, we extract five characteristics and use GA-BP neural network to fit the nonlinear functional relation. At last, experiments demonstrate the effectiveness of the approach and reasonableness of assumption, and also show that CDVFS can get average energy savings of 17.06% compared with previous works within acceptable performance loss.

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Section: Related Workmentioning

confidence: 99%

A Modeling Approach for Energy Saving Based on GA-BP Neural Network

Li¹,

Guo²,

Shen³

et al. 2016

Journal of Electrical Engineering and Technology

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“…Accordingly, the overall energy consumption would augment. Zong et al [25,26] focused on the energy optimization of task duplication-based scheduling strategies. They proposed two energy-aware duplication-based algorithms, called EAD (Energy-Aware Duplication) scheduling and PEBD (Performance-Energy Balanced Duplication) scheduling, which do not replicate all performance-oriented tasks.…”

Section: Related Workmentioning

confidence: 99%

A Novel, Energy-Aware Task Duplication-Based Scheduling Algorithm of Parallel Tasks on Clusters

Liang

Pang

2016

MCA

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Abstract:Increasing energy has become an important issue in high performance clusters. To balance the energy and performance, we proposed a novel, energy-aware duplication-based scheduling (NEADS). An existing energy-aware duplication-based algorithm replicates all qualified predecessor tasks in a bottom-up manner. Some tasks without direct relation may be replicated to the same processor, which cannot reduce the communication energy. Instead, the computation overhead may be increased. In contrast, the proposed algorithm only replicates the directly correlated predecessor tasks in the energy threshold range without lengthening the schedule length. The proposed algorithm is compared with the non-duplication algorithm and existing duplicated-based algorithm. Extensive experimental results show that the proposed algorithm can effectively reduce energy consumption in various applications. It has advantages over other algorithms on computation-intensive applications.

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“…Zong et al [24] presented two energy efficient scheduling algorithms based on duplication that are Energy-Aware Duplication (EAD) scheduling and Performance-Energy Balanced Duplication (PEBD) scheduling to optimize performance as well as energy efficiency. Authors investigated that power can be saved through power aware duplication based scheduling for both interconnect and processors.…”

Section: Power Management For Homogeneous Clustersmentioning

confidence: 99%

“…Several power management techniques have been proposed for multi-tier clusters [9][10][11][12][13][14]. Most of the researches have focused on homogeneous clusters [22][23][24]. Clusters are invariably different in terms of their capacity, power consumption and performance.…”

Section: Introductionmentioning

confidence: 99%

An Optimizing Technique based on Genetic Algorithm for Power Management in Heterogeneous Multi-Tier Web Clusters

Goyal¹,

Kaur²

2015

IJCA

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The most serious drawback nowadays is the power management because the web applications are becoming more sophisticated and their processing power is gradually increasing. Current cluster are designed to handle peak loads, where all servers are equally utilized. In practice, peak load conditions barely happen and clusters are most of the time underutilized. This paper aims to optimize the performance (in terms of execution time) of a multi-tier system and decrease the power consumption of the servers. To achieve these objectives, an Energy Optimizing Genetic Algorithm (EOGA) technique is applied on a three-tier web cluster system. This technique has proved to be successful in decreasing the power consumption and increasing the performance of the system.

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EAD and PEBD: Two Energy-Aware Duplication Scheduling Algorithms for Parallel Tasks on Homogeneous Clusters

Cited by 129 publications

References 35 publications

A Modeling Approach for Energy Saving Based on GA-BP Neural Network

A Modeling Approach for Energy Saving Based on GA-BP Neural Network

A Novel, Energy-Aware Task Duplication-Based Scheduling Algorithm of Parallel Tasks on Clusters

An Optimizing Technique based on Genetic Algorithm for Power Management in Heterogeneous Multi-Tier Web Clusters

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