Load balancing in cloud based on live migration of virtual machines

Achar, Raghavendra; Thilagam, P. Santhi; Soans, Nihal; Vikyath, P. V.; Rao, S. Madhusudhana; Vijeth, A. M.

doi:10.1109/indcon.2013.6726147

Cited by 26 publications

(9 citation statements)

References 8 publications

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“…The disk mapping files associated with VM4 are reconfigured in the storage, and the memory pages are transferred from VMH1 to VMH2. With live migration, load balance can be more efficient and reduce the impact on working VMs [20], [21].…”

Section: A Virtualizationmentioning

confidence: 99%

Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods

Hung

Tsai

et al. 2021

IEEE Access

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This paper presents a two-stage genetic mechanism for the migration-based load balance of virtual machine hosts (VMHs) in cloud computing. Previous methods usually assume this issue as a jobassignment optimization problem and only consider the current VMHs' loads; however, without considering loads of VMHs after balancing, these methods can only gain limited effectiveness in real environments. In this study, two genetic-based methods are integrated and presented. First, performance models of virtual machines (VMs) are extracted from their creating parameters and corresponding performance measured in a cloud computing environment. The gene expression programming (GEP) is applied for generating symbolic regression models that describe the performance of VMs and are used for predicting loads of VMHs after load-balance. Secondly, with the VMH loads estimated by GEP, the genetic algorithm considers the current and the future loads of VMHs and decides an optimal VM-VMH assignment for migrating VMs and performing load-balance. The performance of the proposed method is evaluated in a real cloudcomputing environment, Jnet, wherein the aforementioned methods are implemented as a centralized load balancing mechanism. The experimental results show that our method outperforms previous methods, such as heuristics and statistics regression.

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Section: A Virtualizationmentioning

confidence: 99%

Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods

Hung

Tsai

et al. 2021

IEEE Access

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“…Achar et al [21] proposed an algorithm to maintain load balancing using network traffic. This algorithm used Xen technology, enabling dynamic allocation of network resources at the request of users.…”

Section: B Previous Researchmentioning

confidence: 99%

MLMBN mechanism optimizes network load balance using information with multiple controllers

Jeong¹

2020

Ijemr

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Background/Objectives: In this paper, we propose a Deep Learning Mechanism (DLMBN) mechanism (Deep Learning Mechanism on Blockchain) that optimizes the load balance that can occur in the network by deep learning some important information related to the load balance after connecting the information of multiple distributed controllers into the blockchain. Methods/Statistical analysis: The proposed mechanism binds and manages the load of each controller distributed over the network with a blockchain, thus reducing load time while dynamically balancing the load balance. In particular, deep learning technology was used to ensure that each controller classified as a group would not be biased to one side and would maintain a balanced load balance across the entire network. Findings: As a result of the experiment, the proposed mechanism improved the load balance retention time by 14.6% on average compared to the mechanism previously studied, and the efficiency of SDNs processed in multiple groups by 17.3% on average. In addition, the overhead of SDNs for each group was lowered by 7.9%. Improvements/Applications: Based on the results of this study, future studies plan to apply the results to the actual network and test whether the performance analysis results are applicable to heterogeneous networks consisting of heterogeneous devices.

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“…Load balancing is the process of redistribution of workload in a distributed system like cloud computing ensuring no computing machine is overloaded, under-loaded or idle [12,13]. Load balancing tries to speed up different constrained parameters like response time, execution time, system stability etc.…”

Section: Introductionmentioning

confidence: 99%

Load balancing in cloud computing – A hierarchical taxonomical classification

2019

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Load unbalancing problem is a multi-variant, multi-constraint problem that degrades performance and efficiency of computing resources. Load balancing techniques cater the solution for load unbalancing situation for two undesirable facets-overloading and under-loading. In contempt of the importance of load balancing techniques to the best of our knowledge, there is no comprehensive, extensive, systematic and hierarchical classification about the existing load balancing techniques. Further, the factors that cause load unbalancing problem are neither studied nor considered in the literature. This paper presents a detailed encyclopedic review about the load balancing techniques. The advantages and limitations of existing methods are highlighted with crucial challenges being addressed so as to develop efficient load balancing algorithms in future. The paper also suggests new insights towards load balancing in cloud computing.

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Load balancing in cloud based on live migration of virtual machines

Cited by 26 publications

References 8 publications

Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods

Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods

MLMBN mechanism optimizes network load balance using information with multiple controllers

Load balancing in cloud computing – A hierarchical taxonomical classification

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