Simulated Annealing based approach for Virtual Machine Live Migration

Katal, Avita; Bajoria, Vinayak; Sethi, Vitesh

doi:10.1109/icscc51209.2021.9528160

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…These hours served as filters for the data and performance of VMs. We forecasted which VMs should be transferred to another host based on a fuzzy model analysis of the total number of records (Katal et al, 2021;Rajakumari et al, 2022). Our analytical approach with datasets and fuzzy model processing is depicted in Figure 3.…”

Section: Research Methods Framework Data Studymentioning

confidence: 99%

Towards Improving 5G Quality of Experience: Fuzzy as a Mathematical Model to Migrate Virtual Machine Server in The Defined Time Frame

Hidayat

Ramli

Mardian

et al. 2023

JAETS

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The industry and government have recently acknowledged and used virtual machines (VM) to promote their businesses. During the process of VM, some problems might occur. The issues, such as a heavy load of memory, a large load of CPU, a massive load of a disk, a high load of network and time-defined migration, might interrupt the business processes. This paper identifies the migration process among hosts for VM to overcome the problem within the defined time frame of migration. The introduction of VMs migration in a timely manner is to detect a problem earlier. There are workload parameters, such as network, CPU, disk and memory as our parameters. To overcome the issue, we have to follow the Model named Fuzzy rule. The rule follows the basic of tree model for decision-making. The application of the fuzzy Model for the study is to determine VMs allocation from busy VMs to vacant VMs for balancing purposes. The result of the study showed that the use of the fuzzy Model to forecast VMs migration based on the defined rule had 2 positive impacts. The positive impacts are (1) Time frame live migration of VMs can reduce workload by 80 %. This aims to reduce failures in performing a live migration of VMs to increase data center performance. (2) In testing, the fuzzy Model can provide results with an accuracy of 90 %, so this model can perform a live migration of VMs precisely in determining the execution time. Next, the workload could be balanced among VMs. This research could be used further to improve 5G Quality of Experience (QoE) shortly.

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Section: Research Methods Framework Data Studymentioning

confidence: 99%

Towards Improving 5G Quality of Experience: Fuzzy as a Mathematical Model to Migrate Virtual Machine Server in The Defined Time Frame

Hidayat

Ramli

Mardian

et al. 2023

JAETS

View full text Add to dashboard Cite

show abstract

“…It may cause an increase in downtime. So the VM page dirty rate and the VM's working set (it is a collection of recently referenced segments or memory pages) size [7,10,12,12,[64][65][66][67][68][69][70][71] are relevant parameters for the pre-copy migration.…”

Section: Algorithm 1 Feature Selectionmentioning

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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A machine learning-based optimization approach for pre-copy live virtual machine migration

et al. 2023

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Organizations widely use cloud computing to outsource their computing needs. One crucial issue of cloud computing is that services must be available to clients at all times. However, the cloud services may be temporarily unavailable due to maintenance of the cloud infrastructure, load balancing of services, defense against cyber attacks, power management, proactive fault tolerance, or resource usage. The unavailability of cloud services impacts negatively on the business model of cloud providers. One solution to tackle the service unavailability is Live Virtual Machine Migration (LVM), that is, moving virtual machines (VMs) from the source host machine to the destination host without disrupting the running application. Pre-copy memory migration is a common LVM approach used in most networked systems such as the cloud. The main difficulty with this approach is the high rate of frequently updating memory pages, referred to as "dirty pages. Transferring these updated or dirty pages during the pre-copy migration approach prolongs the total migration time. After a predefined iteration, the pre-copy approach enters the stop-and-copy phase and transfers the remaining memory pages. If the remaining pages are huge, the downtime or service unavailability will be very high -resulting in a negative impact on the availability of the running services. To minimize such service downtime, it is critical to find an optimal time to migrate a virtual machine in the pre-copy approach. To address the issue, this paper proposes a machine learning-based method to optimize pre-copy migration. It has mainly three stages (i) Feature selection (ii) Model generation and (iii) Application of the proposed model in pre-copy migration. The experiment results show that our proposed model outperforms other machine learning models in terms of prediction accuracy and it significantly reduces downtime or service unavailability during the migration process.

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Simulated Annealing based approach for Virtual Machine Live Migration

Cited by 4 publications

References 14 publications

Towards Improving 5G Quality of Experience: Fuzzy as a Mathematical Model to Migrate Virtual Machine Server in The Defined Time Frame

Towards Improving 5G Quality of Experience: Fuzzy as a Mathematical Model to Migrate Virtual Machine Server in The Defined Time Frame

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

A machine learning-based optimization approach for pre-copy live virtual machine migration

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