KBR: Knowledge Based Reduction Method for Virtual Machine Migration in Cloud Computing

Bhaskar, R; Shylaja, B. S.

doi:10.1016/j.procs.2020.01.026

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Jyothi S et.al. [18] Bhaskar R et.al [19] discussed numerous key challenges in dynamic load management in heterogeneous cloud environments. Authors proposed a heterogeneity-aware dynamic application provisioning model to reduce energy consumption in cloud environments.…”

Section: Related Workmentioning

confidence: 99%

Multi-level Hierarchical Controller Assisted Task Scheduling and Resource Allocation in Large Cloud Infrastructures

Jyothi¹,

Shylaja²

2021

IJACSA

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The high-pace emergence in Cloud Computing technologies demands and alarmed academia-industries to attain Quality-of-Service (QoS) oriented solutions to ensure optimal network performance in terms of Service Level Agreement (SLA) provision as well as Energy-Efficiency. Majority of the at-hand solutions employ Virtual Machine Migration to perform dynamic resource allocation which fails in addressing the key problem of SLA-sensitive scheduling where it demands timely and reliable task-migration solution. Undeniably, VM consolidation may help achieve energy-efficiency along with dynamic resource allocation where the classical heuristic methods which are often criticized for its local minima and premature convergence doesn't guarantee the optimality of the solution, especially over large cloud infrastructures. Considering these key problems as motivation, in this paper a highly robust and improved metaheuristic model based on Ant Colony System is developed to achieve Task Scheduling and Resource Allocation. CloudSim based simulation over different PlanetLab cloud traces exhibited superior performance by the proposed task-scheduling model in terms of negligible SLA violence, minimum downtime, minimum energy-consumption and higher number of migrations over other heuristic variants, which make it suitable towards realistic Cloud Computing purposes.

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Section: Related Workmentioning

confidence: 99%

Multi-level Hierarchical Controller Assisted Task Scheduling and Resource Allocation in Large Cloud Infrastructures

Jyothi¹,

Shylaja²

2021

IJACSA

Self Cite

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“…Rough‐set strategy is a brilliant technique to deal with uncertain, imprecise information, and decision‐making issues. The key thought of using a rough‐set model is computing inferior and superior approximations dependent on VMs explicit attributes [16, 17 ].…”

Section: Machine Learning Based Virtual Machine Migration (Mlvm) Symentioning

confidence: 99%

Rough‐set and machine learning‐based approach for optimised virtual machine utilisation in cloud computing

Shylaja

Bhaskar

2020

IET Networks

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To meet rising demands for computing resources, information technology service providers need to select cloudbased services for their vitality and elasticity. Enormous numbers of data centres are designed to meet customer needs. Burning up energy by data centre is very high with the large-scale deployment of cloud data centres. Virtual machine consolidation strategy implementation reduces the data centre energy consumption and guarantees service level agreements. This study proposes a machine learning-based method in cloud computing for the automated use of virtual machines. Machine learningbased virtual machine selection approach integrates the migration control mechanism that enhances selection strategy efficiency. The experiment is performed with various real machine workload circumstances to provide proof and effectiveness of the proposed method. The exploratory outcome shows that the proposed approach streamlines the utilisation of the virtual machine and diminishes the consumption of energy and improves infringement of service level agreements to accomplish better performance.

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KBR: Knowledge Based Reduction Method for Virtual Machine Migration in Cloud Computing

Cited by 2 publications

References 4 publications

Multi-level Hierarchical Controller Assisted Task Scheduling and Resource Allocation in Large Cloud Infrastructures

Multi-level Hierarchical Controller Assisted Task Scheduling and Resource Allocation in Large Cloud Infrastructures

Rough‐set and machine learning‐based approach for optimised virtual machine utilisation in cloud computing

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