Enhanced First-Fit Decreasing Algorithm for Energy-Aware Job Scheduling in Cloud

Alahmadi, Abdulrahman; Alnowiser, Abdulaziz; Zhu, Mengxia; Che, Dunren; Ghodous, Parisa

doi:10.1109/csci.2014.97

Cited by 44 publications

(21 citation statements)

References 13 publications

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“…2, our EATS-FFD effectively reduces energy consumption by 20% compared with EFFD (Recall that in [16], we have shown that EFFD reduced energy consumption by about 10% compared with the traditional FFD). Clearly, exploitation of DFVS helps further reduce energy consumption pretty significantly.…”

Section: A Basic Setting Of Experimentsmentioning

confidence: 78%

“…The third case (row 3 in Table II) decreased the number of nodes to 15, the number of VMs to 30, and the number of tasks to 100. Previously, our group had proposed two other related algorithms, EFFD [16] and EWRR (both are enhanced version of their respective base algorithm). The former extended the traditional FFD scheduling with VM Reuse and migration, and the latter extended the traditional WRR with DVFS.…”

Section: A Basic Setting Of Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

An Innovative Energy-Aware Cloud Task Scheduling Framework

Alahmadi

Che²,

Khaleel³

et al. 2015

2015 IEEE 8th International Conference on Cloud Computing

Self Cite

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With the increased popularity of cloud computing, the number and scales of cloud data centers have kept growing at unprecedented speeds. In the meanwhile, the energy consumption by the data centers has kept commensurately increasing as well. Therefore, the focus of cloud resource management and scheduling has relatively shifted from mere performance to also energy efficiency. In this paper, we present a novel, Energy-Aware Task Scheduling framework that makes integrated exploitation of the two well-known energy saving techniques, DVFS and VM Reuse, on cloud task scheduling in a data center. We present our scheduling approach and framework via a specific algorithm, called EATS-FFD, that assumes FFD as its base scheduling policy. With minor modification, the presented framework can be made to work with a different base scheduling policy, resulting in a correspondingly different scheduling algorithm. Our approach achieves better energyefficiency without sacrificing system QoS. The effectiveness of our approach is evaluated under various experimental scenarios using the CloudReport tool running on the open source CloudSim platform.

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Section: A Basic Setting Of Experimentsmentioning

confidence: 78%

Section: A Basic Setting Of Experimentsmentioning

confidence: 99%

An Innovative Energy-Aware Cloud Task Scheduling Framework

Alahmadi

Che²,

Khaleel³

et al. 2015

2015 IEEE 8th International Conference on Cloud Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The drawback is that it may reduce the QoS of user. Alahmadi et al proposed a novel approach for scheduling, sharing, and migrating virtual machines (VMs) for a bag of cloud tasks to reduce energy consumption with guaranteed certain execution time and high system throughput [19]. However, it does not take into account the additional energy caused by dynamical monitor cloud systems.…”

Section: Related Workmentioning

confidence: 99%

A Novel Resource Deployment Approach to Mobile Microlearning: From Energy-Saving Perspective

Zheng

Zhu

Zhang

et al. 2019

Wireless Communications and Mobile Computing

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Mobile Microlearning, a novel fusion form of the mobile Internet, cloud computing, and microlearning, becomes more prevalent in recent years. However, its high deployment and operational costs make energy saving in cloud become a concerning issue. In this paper, to save energy consumption, a resource deployment approach to cloud service provision for Mobile Microlearning is proposed. Chinese Lexical Analysis System and Dynamic Term Frequency-Inverse Document Frequency (D-TF-IDF) are adopted to implement resource classification. Resources are deployed to the 2-tier cloud architecture according to the classification results. Grey Wolf Optimization (GWO) algorithm is used to forecast real-time energy consumption per byte. The simulation results show that, compared to traditional algorithm, the classification accuracy of small sample categories was significantly improved; the forecast energy consumption value and the standard values are 7.67% in private cloud and 2.93% in public cloud; the energy saving reaches 2.22% to 16.23% in 3G and 7.35% to 20.74% in Wi-Fi.

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“…In recent years, VM placement has received extensive attention [2][3][4][5][6][7][8][9][10]. Researchers have studied various VM placement models and algorithms to optimize the mapping between VMs and PMs to obtain a higher resource utilization of PMs, better load balance among all running hosts, and lower energy consumption of the data center.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have studied various VM placement models and algorithms to optimize the mapping between VMs and PMs to obtain a higher resource utilization of PMs, better load balance among all running hosts, and lower energy consumption of the data center. In [2][3][4], VM placement is regarded as a bin packing problem. Algorithms such as first fit decreasing (FFD) [2] and power-aware bestfit decreasing (PABFD) [4] were proposed for VM placement.…”

Section: Introductionmentioning

confidence: 99%

Evidence-Efficient Affinity Propagation Scheme for Virtual Machine Placement in Data Center

Guo

et al. 2020

IEEE Access

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In cloud data center, without efficient virtual machine placement, the overload of any types of resources on physical machines (PM) can easily cause the waste of other types of resources, and frequent costly virtual machine (VM) migration, which further negatively affects quality of service (QoS). To address this problem, in this paper we propose an evidence-efficient affinity propagation scheme for VM placement (EEAP-VMP), which is capable of balancing the workload across various types of resources on the running PMs. Our approach models the problem of searching the desirable destination hosts for the live VM migration as the propagation of responsibility and availability. The sum of responsibility and availability represent the accumulated evidence for the selection of candidate destination hosts for the VMs to be migrated. Further, in combination with the presented selection criteria for destination hosts. Extensive experiments are conducted to compare our EEAP-VMP method with the previous VM placement methods. The experimental results demonstrate that the EEAP-VMP method is highly effective on reducing VM migrations and energy consumption of data centers and in balancing the workload of PMs.

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Enhanced First-Fit Decreasing Algorithm for Energy-Aware Job Scheduling in Cloud

Cited by 44 publications

References 13 publications

An Innovative Energy-Aware Cloud Task Scheduling Framework

An Innovative Energy-Aware Cloud Task Scheduling Framework

A Novel Resource Deployment Approach to Mobile Microlearning: From Energy-Saving Perspective

Evidence-Efficient Affinity Propagation Scheme for Virtual Machine Placement in Data Center

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