An energy-efficient load balance strategy based on virtual machine consolidation in cloud environment

Yao, Wenbin; Wang, Zhuqing; Hou, Yanzhao; Zhu, Xikang; Li, Xiaoyong; Xia, Yamei

doi:10.1016/j.future.2023.04.014

Cited by 12 publications

(1 citation statement)

References 20 publications

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“…We conduct simulated trials in resource contexts that are heterogeneous, bottleneck, and homogeneous. According to experimental results, LBVMC outperforms other tactics in terms of overall performance and a reduction in energy usage and SLA breaches [41]- [44].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison and Evaluation of Energy-Efficient Cloud Computing Techniques With Load Balancing Approaches

Shah,

Jain,

Bandhu

2024

AEJ

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The advent of Cloud Computing has revolutionized the IT landscape by offering computing resources as a service, similar to conventional utilities like electricity. This paradigm shift has made cloud computing a cornerstone of the contemporary digital economy, attracting substantial focus from both academic and industrial sectors. Its unique pay-as-you-go model provides customers with on-demand resource availability, enhancing operational flexibility. However, this convenience is offset by the growing energy demands of cloud data centers, which not only escalate operational expenses but also contribute to environmental degradation through increased carbon footprints. To combat these issues, Green cloud computing has been introduced, striving for energy-efficient and sustainable operations. This involves employing strategies that minimize energy consumption and resource utilization through the application of energy-conscious algorithms. Although numerous algorithms based on server consolidation have been proposed to optimize energy use in cloud environments, they often lack uniform evaluative comparisons and vary in performance due to differing experimental conditions. This variance presents a challenge in selecting the most effective algorithm tailored to specific needs. This study aims to provide a nuanced analysis of existing energy-efficient algorithms, assisting researchers in identifying the algorithm that best suits their requirements. We undertake an exhaustive comparison of various algorithms, examining their architecture, modelling approaches, and performance metrics. These algorithms are then implemented and tested under uniform conditions using the CloudSim toolkit. Our findings offer an in-depth comparative analysis of these algorithms, shedding light on their respective advantages and shortcomings. Additionally, we delve into a thorough discussion of each algorithm's features and their implications for cloud computing environments.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison and Evaluation of Energy-Efficient Cloud Computing Techniques With Load Balancing Approaches

Shah,

Jain,

Bandhu

2024

AEJ

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EBWO‐GE: An innovative approach to dynamic VM consolidation for cloud data centers

Goyal,

Awasthi

2024

Concurrency and Computation

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SummaryCloud data centers (CDCs) have revolutionized global computing by offering extensive storage and processing capabilities. Nevertheless, the environmental impact of these processes, including their substantial energy consumption and carbon emissions, calls for implementing more efficient techniques. Efficient virtual machine (VM) consolidation is crucial in optimizing resource utilization and reducing energy consumption. Current methods for enhancing energy efficiency often lead to issues such as service level agreements (SLAs) violations and quality of services (QoS) degradation. This study presents a novel approach to host selection using a grey‐extreme (GE) machine learning model, which accurately predicts over and underutilized hosts. In addition, a VM placement technique called enhanced black widow optimization (EBWO) utilizes black widow optimization heuristic techniques and a differential evolutionary approach to optimize VM placement. The proposed dynamic VM consolidation technique optimizes energy utilization while meeting strict SLA requirements and enhancing QoS metrics in CDCs. Extensive analyses were conducted using the Cloudsim toolkit to validate the approach's effectiveness. These analyses encompassed conditions such as random workloads in heterogeneous environments. The simulation results showed that GE‐EBWO outperforms other techniques and improves energy efficiency by 12%–15%. In addition, it significantly decreases VM migrations by 11%–14% compared to other advanced methods. The study validates the practicality of the proposed technique in moving towards environmentally friendly CDCs.

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SCRUB: a novel energy-efficient virtual machines selection and migration scheme in cloud data centers

Yekta,

Shahhoseini

2024

Cluster Comput

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An energy-efficient load balance strategy based on virtual machine consolidation in cloud environment

Cited by 12 publications

References 20 publications

Comparison and Evaluation of Energy-Efficient Cloud Computing Techniques With Load Balancing Approaches

Comparison and Evaluation of Energy-Efficient Cloud Computing Techniques With Load Balancing Approaches

EBWO‐GE: An innovative approach to dynamic VM consolidation for cloud data centers

SCRUB: a novel energy-efficient virtual machines selection and migration scheme in cloud data centers

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