Green algorithm to reduce the energy consumption in cloud computing data centres

AlIsmail, Shaden M.; Kurdi, Heba

doi:10.1109/sai.2016.7556035

Cited by 11 publications

(1 citation statement)

References 12 publications

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“…The proposed scheduling scheme in [19] utilized the resources and saved energy consumption by optimizing the VM allocation and using a consolidation policy. The results showed energy consumption improvement compared with two benchmarks-DVFS and an Energy-aware Scheduling algorithm using Workload-aware Consolidation Technique (ESWCT).…”

Section: Related Workmentioning

confidence: 99%

Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers

2018

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The cloud-computing concept has emerged as a powerful mechanism for data storage by providing a suitable platform for data centers. Recent studies show that the energy consumption of cloud computing systems is a key issue. Therefore, we should reduce the energy consumption to satisfy performance requirements, minimize power consumption, and maximize resource utilization. This paper introduces a novel algorithm that could allocate resources in a cloud-computing environment based on an energy optimization method called Sharing with Live Migration (SLM). In this scheduler, we used the Cloud-Sim toolkit to manage the usage of virtual machines (VMs) based on a novel algorithm that learns and predicts the similarity between the tasks, and then allocates each of them to a suitable VM. On the other hand, SLM satisfies the Quality of Services (QoS) constraints of the hosted applications by adopting a migration process. The experimental results show that the algorithm exhibits better performance, while saving power and minimizing the processing time. Therefore, the SLM algorithm demonstrates improved virtual machine efficiency and resource utilization compared to an adapted state-of-the-art algorithm for a similar problem.

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Section: Related Workmentioning

confidence: 99%

Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers

2018

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A Walkthrough in Live Migration Strategies for Energy-Aware Resource Management in Cloud

Gupta

Khurana

et al. 2021

Autonomic Computing in Cloud Resource Management in Industry 4.0

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Cloud computing: a walk through in models, challenges, and energy solutions

Gupta

Singh

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

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The paradigm of cloud computing in recent times has engrossed a lot of working populace in its service stack offerings. This considerable rise of human traffic landing onto cloud platform for service extraction has consequently led to exponential rise in energy consumption during dynamic and heterogeneous workload situations encompassing huge cloud infrastructure in form of CPU, Hard Disks, network devices etc. The energy released not only poses costs on its stakeholders but is affecting the environment due to rise in carbon footprints. As per available stats [1], the total power consumption of cloud has significantly arisen from 632 billion kilowatt hours in year 2007 to 163 billion kilowatt hours in 2020 which is adequate to leave a devastating effect on environment. Though research is being carried out in this direction, still efficacious measures are far away from its pragmatic implementations. Therefore, the research study conducted here discusses about existing solutions in the direction of energy management in cloud computing. The latter half of paper presents abrief discussion on gaps and future directions.

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Green algorithm to reduce the energy consumption in cloud computing data centres

Cited by 11 publications

References 12 publications

Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers

Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers

A Walkthrough in Live Migration Strategies for Energy-Aware Resource Management in Cloud

Cloud computing: a walk through in models, challenges, and energy solutions

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