Adaptive virtual machine consolidation framework based on performance-to-power ratio in cloud data centers

Ding, Weichao; Luo, Fei; Han, Liangxiu; Gu, Chunhua; Lu, Haifeng; Fuentes, Joel

doi:10.1016/j.future.2020.05.004

Cited by 31 publications

(14 citation statements)

References 30 publications

(56 reference statements)

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“…In Reference 21, Singh et al, had introduced multi‐VM migration based on geometric programming, which considered rates of transfer and compression to every VM for reducing the total time of migration. Ding et al 23 had developed a performance‐to‐power‐based VM consolidation system in cloud data centers. To show the adaptability and unwavering quality of the proposed technique, they performed atrial estimation in both conditions of normal and mimicked.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

Hariharan

Rathinavelayatham

Shanmugaiah

et al. 2022

Concurrency and Computation

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In cloud computing, virtual machine (VM) consolidation and migration are significant schemes to enhance the energy efficiency and costs of the cloud environment. The process of VM consolidation is to execute a more number of tasks with less power consumption. However, due to unreliable physical resources, energy consumption is increased. So, to solve this issue, improved beetle swarm optimization (IBSO) algorithm based on energy-aware VM consolidation is presented in this article. IBSO algorithm joins together beetle swarm optimization (BSO) and particle swarm optimization (PSO). The proposed algorithm is defined with the effective representation of solution and multi-objective functions such as power consumption, resource wastage, and SLA violation. Results of the work are depicted that the proposed approach outperformed different previous methods in terms of different evaluation measures.

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Section: Literature Reviewmentioning

confidence: 99%

Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

Hariharan

Rathinavelayatham

Shanmugaiah

et al. 2022

Concurrency and Computation

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“…Tarahomi et al [8] propose an effective energysaving micro-genetic algorithm for destination host selection in dynamic VM consolidation. Ding et al [9] propose a dynamic VM consolidation framework based on predicted resource utilization and heterogeneous host performance-topower-ratio. Similarly, we also propose a dynamic consolidation algorithm for the VM cloud in a previous study [10].…”

Section: Related Workmentioning

confidence: 99%

An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

Zhang

Wang

et al. 2021

IEEE Access

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“…In the current literature, majority of the studies on green computing have focused on the development of frameworks for green computing technology (Ding et al , 2020), algorithms for green computing implementation (Rahmani et al , 2020), beliefs and personality traits of users (Dalvi-Esfahani et al , 2020) and evaluation metrics (Gong et al , 2020). With green computing being an emerging technological paradigm, it would be useful to analyze its characteristics in parallel with other innovations which already have well-established analytical frameworks.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel hybrid DEMATEL-K-means clustering algorithm for modeling the barriers of green computing adoption in the Philippines

Abellana

Mayol

2021

JM2

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Purpose This paper aims to propose a novel hybrid-decision-making trial and evaluation laboratory-K means clustering algorithm as a decision-making framework for analyzing the barriers of green computing adoption. Design/methodology/approach A literature review is conducted to extract relevant green computing barriers. An expert elicitation process is performed to finalize the barriers and to establish their corresponding interrelationships. Findings The proposed approach offers a comprehensive framework for modeling the barriers of green computing adoption. Research limitations/implications The results of this paper provide insights on how the barriers of green computing adoption facilitate the adoption of stakeholders. Moreover, the paper provides a framework for analyzing the structural relationships that exist between factors in a tractable manner. Originality/value The paper is one of the very first attempts to analyze the barriers of green computing adoption. Furthermore, it is the first to offer lenses in a Philippine perspective. The paper offers a novel algorithm that can be useful in modeling the barriers of innovation, particularly, in green computing adoption.

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Adaptive virtual machine consolidation framework based on performance-to-power ratio in cloud data centers

Cited by 31 publications

References 30 publications

Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

A novel hybrid DEMATEL-K-means clustering algorithm for modeling the barriers of green computing adoption in the Philippines

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