Profile-based dynamic application assignment with a repairing genetic algorithm for greener data centers

Vasudevan, Meera; Tian, Yu-Chu; Tang, Maolin; Kozan, Erhan; Zhang, Weizhe

doi:10.1007/s11227-017-1995-9

Cited by 15 publications

(2 citation statements)

References 22 publications

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“…GA is now implemented in multiple layers in the virtual resource management of data centers [32,33]. It is designed for profile-guided application assignment to VMs.…”

Section: Implementation Of Gamentioning

confidence: 99%

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Ding

Tian²,

Wang³

et al. 2022

Neural Comput & Applic

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Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

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“…GA is now implemented in multiple layers in the virtual resource management of data centers [32,33]. It is designed for profile-guided application assignment to VMs.…”

Section: Implementation Of Gamentioning

confidence: 99%

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Ding

Tian²,

Wang³

et al. 2022

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many solutions are being proposed by different researchers to fill this gap especially in the context of green cloud computing, where the solutions are being discourses for having any significant impact on achieving competitive advantage in implementing energy-efficient CDCs. 11 The research proposed in this article attempts to increase the utilization of already installed physical machines in CDCs to support IT industry to overcome some of the power-related issues in Pakistan. The study aims to maximize the performance of CDCs and reduce their power consumption.…”

Section: Introductionmentioning

confidence: 99%

Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

Uddin

Hamdi

Alghamdi

et al. 2021

International Journal of Distributed Sensor Networks

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Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

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Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers

et al. 2020

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Profile-based dynamic application assignment with a repairing genetic algorithm for greener data centers

Cited by 15 publications

References 22 publications

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers

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