A novel virtual machine deployment algorithm with energy efficiency in cloud computing

Zhou, Zhou; Hu, Zhigang; Song, Tie; Yu, Junyang

doi:10.1007/s11771-015-2608-5

Cited by 55 publications

(27 citation statements)

References 8 publications

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“…In year 2020, cloud computing will be accounted for $241 billion and $250 billion in 2022 according to Verdantix company [136]. Cloud based solution are also used by a large number of Small and Medium Businesses corporations, mostly in North America [9] [10] [11]. Further, physical resource (bare metal as service) based services have been also replaced by virtual services, which makes the environment much more sustainable.…”

Section: Figure 3: Investment On Cloud Computingmentioning

confidence: 99%

A Taxonomy and Future Directions for Sustainable Cloud Computing

2018

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The cloud computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT) based applications the usage of cloud services is increasing exponentially. The next generation of cloud computing must be energy-efficient and sustainable to fulfil the end-user requirements which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The usage of large number of cloud datacenters increases cost as well as carbon footprints, which further effects the sustainability of cloud services. In this paper, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. Further, the current research on sustainable cloud computing is organized into several categories: application design, sustainability metrics, capacity planning, energy management, virtualization, thermal-aware scheduling, cooling management, renewable energy and waste heat utilization. The existing techniques have been compared and categorized based on the common characteristics and properties. A conceptual model for sustainable cloud computing has been proposed along with discussion on future research directions.

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Section: Figure 3: Investment On Cloud Computingmentioning

confidence: 99%

A Taxonomy and Future Directions for Sustainable Cloud Computing

2018

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“…Zhou et al [37] proposed an algorithm (TESA) for energy efficiency [30,31,38] in cloud data servers. This mainly works on relationship between the energy consumption and (processor) resource utilization.…”

Section: Related Workmentioning

confidence: 99%

A Novel Energy Efficient Scheduling for VM Consolidation and Migration in Cloud Data Centers

Yakobu¹,

Chirra²,

Sistla³

2019

ISI

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In data centers, the energy-efficient scheduling of virtual machines (VMs) is critical to the full utilization of physical machines (PMs). Considering the sheer amount of data in cloud environment, this paper puts forward a novel energy-efficient scheduling method for VM consolidation and migration in cloud data centers. The proposed method optimizes the energy consumption at cloud data centers through three algorithms: the first algorithm describes the general migration of VMs among PMs; the second algorithm defines the migration of VMs among PMs; the third algorithm explains how the migration takes place. The effectiveness of our method was demonstrated on CloudSim with 5 PMs and 30 VMs, under the constraints of arrival time and deadline. The results show that our method can balance the load of input jobs and schedule the VMs properly, thus reducing the carbon emissions at the cloud data center.

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“…The proposed approach optimizes the tradeoff between job consolidation and distribution of traffic patterns. Energy efficiency and network awareness individual job performance, and traffic demands CloudSim A novel virtual machine deployment algorithm with energy efficiency in cloud computing [25] To improve the energy efficiency of large-scale data centers, TESA is firstly proposed. Then based on the TESA, five kinds of VM selection policies are presented.…”

Section: Matlabmentioning

confidence: 99%

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Sultanpure¹,

Reddy²

2018

IJECE

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<p>Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads.</p>

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A novel virtual machine deployment algorithm with energy efficiency in cloud computing

Cited by 55 publications

References 8 publications

A Taxonomy and Future Directions for Sustainable Cloud Computing

A Taxonomy and Future Directions for Sustainable Cloud Computing

A Novel Energy Efficient Scheduling for VM Consolidation and Migration in Cloud Data Centers

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

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