Online Self-Reconfiguration with Performance Guarantee for Energy-Efficient Large-Scale Cloud Computing Data Centers

Mi, Haibo; Wang, Huaimin; Yin, Gang; Zhou, Yangfan; Shi, Dianxi; Lin, Yaping

doi:10.1109/scc.2010.69

Cited by 145 publications

(80 citation statements)

References 15 publications

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“…An approach of dynamic resource allocation for large Internet-oriented data centers bases on queuing theory and Erlang's loss formula represented in [4]. On the other hand it is proposed to use a genetic algorithm based approach, namely GABA, to adaptively self-reconfigure the VMs (Virtual Machines) in large-scale data centers [5]. All the models proposed focuses on the server virtualization not the desktop virtualization.…”

Section: Methodsmentioning

confidence: 99%

Server hardware resources optimization for virtual desktop infrastructure implementation

Makoviy¹,

Proskurin²,

Khitskova³

et al. 2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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Section: Methodsmentioning

confidence: 99%

Server hardware resources optimization for virtual desktop infrastructure implementation

Makoviy¹,

Proskurin²,

Khitskova³

et al. 2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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show abstract

“…1) Post-copy migration: Here VM's memory contents are migrated towards destination hosts only when VM's processor state is migrated to the destination host [9].…”

Section: Related Workmentioning

confidence: 99%

NDynamic Framework for Secure VM Migration over Cloud Computing

Rathod¹,

Reddy²

2017

J Inf Process Syst

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In the centralized cloud controlled environment, the decision-making and monitoring play crucial role where in the host controller (HC) manages the resources across hosts in data center (DC). HC does virtual machine (VM) and physical hosts management. The VM management includes VM creation, monitoring, and migration. If HC down,the services hosted by various hosts in DC can't be accessed outside the DC. Decentralized VM management avoids centralized failure by considering one of the hosts from DC as HC that helps in maintaining DC in running state. Each host in DC has many VM's with the threshold limit beyond which it can't provide service.To maintain threshold , the host's in DC does VM migration across various hosts. The data in migration is in the form of plaintext, the intruder can analyze packet movement and can control hosts traffic. The incorporation of security mechanism on hosts in DC helps protecting data in migration. This paper discusses an approach for dynamic HC selection, VM selection and secure VM migration over cloud environment.

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“…This approach restricts the number of VMs in a single physical host ensuring that some VMs are assigned to different physical hosts and the total number of migrations will be limited. A genetic algorithm was proposed to adaptively self-reconfigure the VMs in cloud data centers that hold heterogeneous servers [30]. The Multi-objective Grouping Genetic Algorithm (MGGA) was proposed in [10] for combining possibly conflicting objectives when searching the solution space.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Power Consumption and Temperature in CloudComputing

Alsini¹

2017

IOSR JEN

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-Cloud computing resources are utilized in forms of different virtual machines that provide largescale computation for complicated tasks. The allocation process of virtual machines on physical machines is a critical portion of cloud computing trends. Efficient allocation of the virtual machines on available physical servers is necessary to exploit the high-performance resource utilization and improving power efficiency. In this paper, virtual machine allocation problem is handled by particle swarm optimization with fuzzy multi-objective method. The main goal of the proposed method is to efficiently obtain a near-optimal solution that minimizes power consumption, total processing resource wastage and the peak temperature among the servers. The proposed method has been compared with well-known algorithms for virtual machine allocation problem existed in the literature. The comparison results prove that the proposed algorithm significantly outperforms the compared methods on the basis of power consumption, processing resource wastage and temperature metrics.

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Online Self-Reconfiguration with Performance Guarantee for Energy-Efficient Large-Scale Cloud Computing Data Centers

Cited by 145 publications

References 15 publications

Server hardware resources optimization for virtual desktop infrastructure implementation

Server hardware resources optimization for virtual desktop infrastructure implementation

NDynamic Framework for Secure VM Migration over Cloud Computing

Minimizing Power Consumption and Temperature in CloudComputing

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