Energy-efficient virtual machine consolidation algorithm in cloud data centers

Zhou, Zhou; Hu, Zhigang; Yu, Junyang; Abawajy, Jemal; Chowdhury, Mihir

doi:10.1007/s11771-017-3645-z

Cited by 29 publications

(17 citation statements)

References 12 publications

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“…VM consolidation technique achieves energy saving by eliminating idle power consumption through switching idle hosts to low power mode such as sleep or hibernate modes [66,67]. One of the capabilities of virtualization is VM relocation between compute nodes, known as migration [68]. Performing VM migration with no downtime is called live migration [68,69].…”

Section: Consolidation Of Virtual Machines (Vm)mentioning

confidence: 99%

“…One of the capabilities of virtualization is VM relocation between compute nodes, known as migration [68]. Performing VM migration with no downtime is called live migration [68,69]. There are two main states when VMs are migrated: when some physical hosts are under-utilized, VMs are migrated to keep the number of active physical servers to be minimum; and relocate VMs from overloaded hosts to avoid performance degradation [70].…”

Section: Consolidation Of Virtual Machines (Vm)mentioning

confidence: 99%

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Green Cloud Software Engineering for Big Data Processing

et al. 2020

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Internet of Things (IoT) coupled with big data analytics is emerging as the core of smart and sustainable systems which bolsters economic, environmental and social sustainability. Cloud-based data centers provide high performance computing power to analyze voluminous IoT data to provide invaluable insights to support decision making. However, multifarious servers in data centers appear to be the black hole of superfluous energy consumption that contributes to 23% of the global carbon dioxide (CO2) emissions in ICT (Information and Communication Technology) industry. IoT-related energy research focuses on low-power sensors and enhanced machine-to-machine communication performance. To date, cloud-based data centers still face energy–related challenges which are detrimental to the environment. Virtual machine (VM) consolidation is a well-known approach to affect energy-efficient cloud infrastructures. Although several research works demonstrate positive results for VM consolidation in simulated environments, there is a gap for investigations on real, physical cloud infrastructure for big data workloads. This research work addresses the gap of conducting real physical cloud infrastructure-based experiments. The primary goal of setting up a real physical cloud infrastructure is for the evaluation of dynamic VM consolidation approaches which include integrated algorithms from existing relevant research. An open source VM consolidation framework, Openstack NEAT is adopted and experiments are conducted on a Multi-node Openstack Cloud with Apache Spark as the big data platform. Open sourced Openstack has been deployed because it enables rapid innovation, and boosts scalability as well as resource utilization. Additionally, this research work investigates the performance based on service level agreement (SLA) metrics and energy usage of compute hosts. Relevant results concerning the best performing combination of algorithms are presented and discussed.

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Section: Consolidation Of Virtual Machines (Vm)mentioning

confidence: 99%

Section: Consolidation Of Virtual Machines (Vm)mentioning

confidence: 99%

Green Cloud Software Engineering for Big Data Processing

et al. 2020

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“…To solve the high energy consumption dilemma, an energy-efficient virtual machine consolidation predictionbased VM deployment algorithm for energy efficiency (PVDE) was presented by Zhou et.al [13]. For the classification of servers in the data center the linear weighted method was utilized and predicts the host load.…”

Section: Related Work: a Brief Reviewmentioning

confidence: 99%

Modified Salp Swarm Algorithm based Energy-Efficient Resource Allocation in Cloud-Computing Data Centers

Patel*¹,

Bhadka²

2019

IJITEE

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In a cloud data centre the consolidation of the virtual machines (VMs) assist to optimize the resources need and diminish the energy consumption. In the consolidation of the VMs the VM placement acts an important role. By considering optimized energy consumption the researchers have developed various algorithms for VM placement. However, these algorithms be deficient in the exploitation mechanism use resourcefully. This paper attend to VM placement issues by offering metaheuristic algorithms that is, the Modified Salp Swarm Algorithm (MSSA) presenting the comparative analysis relating to energy optimization. The comparison are made adjacent to the existing particle swarm optimization (PSO), and salp swarm algorithm (SSA) and the energy consumption results of all the contributing algorithms confirm that the proposed MSSA is more efficient than the other algorithms. The simulation result demonstrates that MSSA outperforms effectively than other presented approaches in optimal VM placement in cloud computing environment with maximal resource use, minimal energy consumption, minimum SLA violation and reduced migration cost

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“…Zhou et al [28] proposed an algorithm, EEOM for optimizing time of triggering and selection of virtual machine. The EEOM considers CPU time and memory factors.…”

Section: Related Workmentioning

confidence: 99%

“…Zhou et al [28] proposed a consolidation algorithm named PVDE (prediction-based VM deployment algorithm for energy efficiency) that predicts the load of each server [32,33]. A linear weighted method is used to predict the load of each server.…”

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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Energy-efficient virtual machine consolidation algorithm in cloud data centers

Cited by 29 publications

References 12 publications

Green Cloud Software Engineering for Big Data Processing

Green Cloud Software Engineering for Big Data Processing

Modified Salp Swarm Algorithm based Energy-Efficient Resource Allocation in Cloud-Computing Data Centers

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

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