Virtual machine migration implementation in load balancing for Cloud computing

Razali, Rabiatul Addawiyah Mat; Rahman, Ruhani Ab; Zaini, Norliza; Samad, Mustaffa

doi:10.1109/icias.2014.6869540

Cited by 23 publications

(14 citation statements)

References 2 publications

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“…Completion time is compared with Razali et al [12] by configuring similar environment. In both methods the VM with minimum load are migrated.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

“…It provides improved performance of the applications running in virtual machine in terms of response time and distributes the load across the servers. Razali et al [12] describe a strategy for improving overall load balancing performance by implementing the migration of virtual machines across multiple hosts, in which utilization of CPU resources can be optimized. The results provide a minimum migration of virtual machines and efficient utilization of resources.…”

Section: Related Workmentioning

confidence: 99%

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A Combined Bin Packing Vm Allocation and Minimum Loaded Vm Migration Approach for Load Balancing in Iaas Cloud Datacenters

Arya¹

2015

IJRET

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“…Completion time is compared with Razali et al [12] by configuring similar environment. In both methods the VM with minimum load are migrated.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Combined Bin Packing Vm Allocation and Minimum Loaded Vm Migration Approach for Load Balancing in Iaas Cloud Datacenters

Arya¹

2015

IJRET

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“…This type of research primarily improves load balancing and resource utilization, such as in nine previous studies [6], [7], [24]- [30]. One of these studies proposed a two-stage centralized load balancing framework that had good scalability and high availability and a dynamic algorithm for balancing loads based on neural networks [24].…”

Section: Focus On System Performancementioning

confidence: 99%

“…Many research has been devoted to multiobjective optimization, e.g., the constraints of QoS [14], [31]- [33], energy consumption [34], [35], [44], economic costs [3], [7], [14], system performance [8], [11], [13], [37], [38], and all these comprehensive [14], [29], [32], [33], [36], [45].…”

Section: Multiobjective Optimization Schedulingmentioning

confidence: 99%

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A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

Zuo

Dong

Shu

et al. 2018

IEEE Systems Journal

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Abstract-In cloud computing, resources are dynamic, and the demands placed on the resources allocated to a particular task are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called interlacing peak is proposed. First, the resource load information, such as CPU, I/O, and memory usage, is periodically collected and updated, and the task information regarding CPU, I/O, and memory is collected. Second, resources are sorted into three queues according to the loads of the CPU, I/O, and memory: CPU intensive, I/O intensive, and memory intensive, according to their demands for resources. Finally, once the tasks have been scheduled, they need to interlace the resource load peak. Some types of tasks need to be matched with the resources whose loads correspond to a lighter types of tasks. In other words, CPUintensive tasks should be matched with resources with low CPU utilization; I/O-intensive tasks should be matched with resources with shorter I/O wait times; and memory-intensive tasks should be matched with resources that have low memory usage. The effectiveness of this method is proved from the theoretical point of view. It has also been proven to be less complex in regard to time and place. Four experiments were designed to verify the performance of this method. Experiments leverage four metrics: 1) average response time; 2) load balancing; 3) deadline violation rates; and 4) resource utilization. The experimental results show that this method can balance loads and improve the effects of resource allocation and utilization effectively. This is especially true when resources are limited. In this way, many tasks will compete for the same resources. However, this method shows advantage over other similar standard algorithms.

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Deployment of Cloud Using Open-Source Virtualization: Study of VM Migration Methods and Benefits

Rastogi

Narayan²,

Krishan³

et al. 2017

Advances in Intelligent Systems and Computing

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Virtual machine migration implementation in load balancing for Cloud computing

Cited by 23 publications

References 2 publications

A Combined Bin Packing Vm Allocation and Minimum Loaded Vm Migration Approach for Load Balancing in Iaas Cloud Datacenters

A Combined Bin Packing Vm Allocation and Minimum Loaded Vm Migration Approach for Load Balancing in Iaas Cloud Datacenters

A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

Deployment of Cloud Using Open-Source Virtualization: Study of VM Migration Methods and Benefits

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