An Optimized Approach for Virtual Machine Live Migration in Cloud Computing Environment

Gupta, Ambika; Dimri, Priti; Bhatt, Ritesh

doi:10.1007/978-981-15-5258-8_52

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…This section is devoted to the performance evaluation of the proposed prediction model. To assess its effectiveness, various meta-heuristic-based algorithms have been put forward, and a comparative study has been conducted [42][43][44]. In this context, three distinct sets of datasets representing data from the last hour have been randomly generated.…”

Section: Resultsmentioning

confidence: 99%

Enhanced Linear Regression Models for Resource Usage Prediction in Dynamic Cloud Environments

2024

IJACSA

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In response to the diverse resource utilization patterns observed across enterprises, this study proposes the utilization of adaptable cloud services. A novel system framework is presented, capturing and logging resource consumption at discrete intervals. Subsequently, this recorded data serves as input for a linear regression model, functioning as a machine learning tool to predict resource utilization in forthcoming intervals, leveraging historical data stored within the regression module. To bolster the resilience of the linear regression model, various effective meta-heuristic techniques are integrated alongside the conventional linear regression methodology, facilitating more accurate anticipation of overloaded or under-loaded resource conditions before their occurrence in real-world scenarios. Simulations demonstrate that the hybrid algorithm, named Whale Optimization Algorithmbased Linear Regression (WOA-LR), outperforms Genetic Algorithm-Linear Regression (GA-LR), Particle Swarm Optimization-Linear Regression (PSO-LR), JAYA-LR, and traditional Linear Regression (LR) in achieving desired objective functions and significantly reducing Mean Squared Error (MSE). This approach holds promise for more accurate resource utilization prediction and optimization in dynamic cloud environments.

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Section: Resultsmentioning

confidence: 99%

Enhanced Linear Regression Models for Resource Usage Prediction in Dynamic Cloud Environments

2024

IJACSA

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“…This is not a good option for cloud providers from a business perspective. To minimize downtime, the most commonly used approach is migrating VMs while they are running [3,4,22,23]. The two main performance metrics of live virtual machine migration that we consider in this paper are the following.…”

Section: Live Virtual Machine Migrationmentioning

confidence: 99%

“…An absence of continuous services may have a negative impact on clients. Live virtual machine migration; virtual machines are migrated or relocated from one physical host to another without impacting the running applications [3,4]. The purpose of the live machine migration is to address issues related to fault tolerance, load balancing, maintenance, tackling cyber-attacks, etc.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Optimization Approach for Pre-Copy Live Virtual Machine Migration

Haris¹,

Khan²,

Nhlabatsi³

2022

SSRN Journal

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“…Cloud service providers typically provide a wide range of services, which may include security issues such as throughput, availability, and reliability. Cloud computing has grown significantly as a result of virtualization [12]. This technology enables resource sharing in cloud systems, allowing different functions to operate simultaneously across multiple platforms.…”

Section: Introductionmentioning

confidence: 99%

Incentive approaches for cloud computing: challenges and solutions

Yunlong,

Jie

2024

J. Eng. Appl. Sci.

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Cloud computing enables highly configurable and reliable computing resources on a rentable per-use scheme, facilitating quick and cost-effective provisioning of large-scale applications. Thanks to the fast-paced evolution of cutting-edge technologies and the rapid spread of cloud-based solutions, the cloud computing ecosystem is now part of our everyday lives. Nevertheless, cloud computing relies on highly sophisticated data centers comprising energy-consuming servers and equipment that require much energy. Stimulating cloud services for active participation and network contributions presents several challenges. Strategies based on artificial intelligence (AI), game theory, and blockchain have great potential to create an economically sustainable cloud ecosystem. This paper explores strategies grounded in AI, game theory, and blockchain to foster an economically sustainable cloud ecosystem. Informed by a survey study, our research delves into incentive approaches within cloud computing. Theoretical foundations, motivations, and enabling techniques are comprehensively examined to provide valuable insights for a broad audience. The primary contributions of this work lie in elucidating the application of AI, game theory, and blockchain to address challenges in incentivizing cloud services, paving the way for a more sustainable and efficient cloud computing landscape.

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An Optimized Approach for Virtual Machine Live Migration in Cloud Computing Environment

Cited by 10 publications

References 10 publications

Enhanced Linear Regression Models for Resource Usage Prediction in Dynamic Cloud Environments

Enhanced Linear Regression Models for Resource Usage Prediction in Dynamic Cloud Environments

A Machine Learning-Based Optimization Approach for Pre-Copy Live Virtual Machine Migration

Incentive approaches for cloud computing: challenges and solutions

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