A machine learning model for improving virtual machine migration in cloud computing

Belgacem, Ali; Mahmoudi, Saïd; Ferrag, Mohamed Amine

doi:10.1007/s11227-022-05031-z

Cited by 16 publications

(8 citation statements)

References 37 publications

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“…The research primarily underscores the imperative of optimizing energy consumption in environments housing multiple servers that operate continuously, resulting in substantial energy consumption. Furthermore, a substantial portion of researchers characterizes overloaded servers based on elevated CPU utilization [42,43], overall resource consumption at the individual server level [44], and across an entire cluster [45]. The majority of the summarized papers outlined in Table 1 predominantly focus on identifying overloaded servers through high resource and energy utilization, primarily relying on large-scale data processing of consumption rates.…”

Section: Summary Of Recent Selected Work On Vm Migrationmentioning

confidence: 99%

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Kushchazli,

Safargalieva,

Kochetkova

et al. 2024

Mathematics

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The advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area of research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis of VM migration processes within cloud infrastructures, examining various migration types, server load assessment methods, VM selection strategies, ideal migration timing, and target server determination criteria. We introduce a queuing theory-based model to scrutinize VM migration dynamics between servers in a cloud environment. By reinterpreting resource-centric migration mechanisms into a task-processing paradigm, we accommodate the stochastic nature of resource demands, characterized by random task arrivals and variable processing times. The model is specifically tailored to scenarios with two servers and three VMs. Through numerical examples, we elucidate several performance metrics: task blocking probability, average tasks processed by VMs, and average tasks managed by servers. Additionally, we examine the influence of task arrival rates and average task duration on these performance measures.

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Section: Summary Of Recent Selected Work On Vm Migrationmentioning

confidence: 99%

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Kushchazli,

Safargalieva,

Kochetkova

et al. 2024

Mathematics

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“…The author concludes that the proposed model has improved energy consumption and fast convergence to learn and provide acceptable accuracy compared with traditional models. [5] presented a machine learning model for VM migration prediction model using a K-fold learning algorithm by taking a good learning dataset and concluding that the machine learning model's accuracy is very high. Similarly, we also proposed a machine learning-based VM load prediction method that helps reduce SLA violations but cannot directly help reduce energy consumption.…”

Section: Machine-learning Modelsmentioning

confidence: 99%

“…The threshold value parameter is calculated using the cannibalism rating (CR), where CR indicates the number of survivors needed in the next round. The value of CR is a dynamic value that is calculated using eq (5). This is called sibling cannibalism in BWO.…”

Section: Std Deviation Utilisation (P M I) M Aximum Utilisation Capacitymentioning

confidence: 99%

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

Goyal

Awasthi

2023

Preprint

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Cloud data centers remove the hardware barriers for the world with enormous storage and computing capabilities and provide access from anywhere over the internet with the concept of virtual machines (VM) and physical machines (PM). These cloud centers raise the problem of lack of resource utilisation and energy waste. These drawbacks have led many researchers to propose VM placement techniques that improve resource utilization and energy consumption. The research proposes VM migration/consolidation and host selection techniques to improve the resource utilisation of cloud centers. This research suggests hybrid black widow optimization (HBWO) and a combined machine learning model to mitigate the problem of cloud centers. HBWO optimizes the VM placement sub-process using an evolutionary approach and a combined machine-learning-based model to predict the over and under-utilised hosts. The effectiveness of the proposed approach was analysed and compared with a Power-Aware Best-Fit Decrease (PABFD) approach and a Particle Swarm Optimization (PSO) approach. The proposed approach improves energy utilisation by 30-40\% over PABFD and 20-15\% over PSO. Moreover, the combined approach of HBWO and machine learning improves the overall performance by 40-50\% compared to PABFD and 20-30\% compared to PSO.

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“…Nevertheless, virtual machine (VM) migration using virtualization technology can adversely affect cloud performance, making it a major concern. The uneven distribution of VMs during resource allocation and their frequent movement from one server to another can lead to increased energy consumption and network overhead [3,6].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Investigating the effect of virtual machine migration accounting on reliability using a cluster model

et al. 2023

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The purpose of the article is to develop and verify with the help of mathematical modeling a software method of deploying a fault-tolerant computing cluster with a virtual machine, which consists of two physical servers (main and backup), on which a distributed data storage system with synchronous data replication from the source server to the backup server is deployed. For this purpose, the task is to conduct a computational experiment on a model of a fault-tolerant cluster, which neglects costs during recovery for the migration of virtual machines by means of the mathematical application Mathcad. Combining computing resources into clusters is a way to ensure high reliability, fault tolerance, and continuity of the computing process of computer systems. This is achieved through virtualization, which enables the movement of virtual resources, services, or applications between physical servers while maintaining the continuity of computing processes. The focus of this study is on a failover cluster, which is composed of two physical servers (primary and backup) connected through a switch, and each server has a local hard disk. A distributed storage system with synchronous data replication from the source server to the backup server is deployed on the local disks of the servers, and a virtual machine is running on the cluster. Markovian processes, flows of podias, and Kolmogorov's systems of differential equations are built into the mathematical tools of the model of a water cluster. To ensure the continuity of the computing process in case of a failure of the main server, a shadow copy of the virtual machine is launched on the backup server. The reliability of the failover cluster is measured by the coefficient of non-stationary readiness. A Markov model is proposed to assess the reliability of the failover cluster, taking into account the costs of migrating virtual machines and mechanisms that ensure the continuity of the computing process in the cluster in case of a failure of one physical server. The memory migration process maintains two copies of the virtual machine on different physical servers, enabling them to continue working on the other in the event of failure. A simplified model of the failover cluster neglects the cost of migrating virtual machines and provides an upper estimate of reliability. The study shows that the reliability of a failover cluster, as measured by the non-stationary availability factor, is significantly impacted by the virtual machine migration process. The findings of this study can be used to inform decisions about the technology chosen to ensure the failure stability and continuity of the computing process of computer systems with cluster architecture. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculations allow us to draw a conclusion about the significant impact of virtual machine migration accounting on reliability. The calculation was performed under the following failure rates of the server, disk, and switch: λ0 = 1,115×10-5 1/h, λ1 = 3,425×10-6 1/h, λ2 = 2,3×10-6 1/h recovery respectively: μ0 = 0,33 1/h, μ1 = 0,171/h, μ2 = 0,33 1/h. The intensity of synchronization of the distributed storage system: μ3 = 1 1/h, μ4 = 2 1/h. The difference of non-stationary cluster availability coefficients is d = К2(t) – К1(t) = 2.7×10-10

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A machine learning model for improving virtual machine migration in cloud computing

Cited by 16 publications

References 37 publications

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

Investigating the effect of virtual machine migration accounting on reliability using a cluster model

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