Introduction to Virtualization in Cloud Computing

Sharma, Vijay; Singh, Arjun; Jaya, Krishna R.; Bairwa, Amit Kumar; Srivastava, Devesh Kumar

doi:10.1201/9781003185376-1

Cited by 7 publications

(2 citation statements)

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“…Testing has been conducted to enhance the performance of multiple algorithms by influencing either the hardware or the software. Virtualization techniques allow the creation of multiple virtual machines on a single physical machine, reducing the requirement for additional hardware and enhancing efficiency [3]. CC utilizes virtualization technology tailored to user requirements to supply resources, decreasing energy usage.…”

Section: Introductionmentioning

confidence: 99%

Optimized resource allocation in cloud computing for enhanced performance with modified particle swarm optimization

Gogula,

Sridhar,

Arvind

et al. 2024

MATEC Web Conf.

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Cloud Computing (CC) offers abundant resources and diverse services for running a wide range of consumer applications, although it faces specific issues that need attention. Cloud customers aim to choose the most suitable resource that fulfills the requirements of consumers at a fair cost and within an acceptable timeframe; however, at times, they wind up paying more for a shorter duration. Many advanced algorithms focus on optimizing a single variable individually. Hence, an Optimized Resource Allocation in Cloud Computing (ORA-CC) Model is required to achieve equilibrium between opposing aims in Cloud Computing. The ORA-CC study aims to create a task processing structure with the decision-making ability to choose the best resource in real-time for handling diverse and complicated uses on Virtual Computers (VC). It will utilize a Modified Particle Swarm Optimization (MoPSO) method to meet a deadline set by the user. The fitness value is calculated by combining a base value with the enhanced estimation of resources based on the ORA-CC algorithm to create a robust arrangement. The ORA-CC technique's effectiveness is evaluated by contrasting it with a few current multi-objective restrictions applied to machine scheduling strategies utilizing the Cloudsim simulation. The comparison demonstrates that the suggested ORA-CC strategy offers more efficient resource allocation than other techniques.

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Section: Introductionmentioning

confidence: 99%

Optimized resource allocation in cloud computing for enhanced performance with modified particle swarm optimization

Gogula,

Sridhar,

Arvind

et al. 2024

MATEC Web Conf.

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show abstract

“…Virtualization offers many energy management solutions in cloud computing [4], [5]. The virtualization process can integrate several virtual machines (VM) at the data center level by placing some virtual servers on a physical machine (PM) and then shutting down idle PMs to optimize energy consumption [6], [7]. The virtual machines placement (VMP) places the VMs on the physical machines so that the benefit of the cloud providers is maximized [8].…”

Section: Introductionmentioning

confidence: 99%

Virtual machine placement in cloud using artificial bee colony and imperialist competitive algorithm

Javadi-Moghaddam¹,

Dehghani

2023

IJECE

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<span lang="EN-US">Increasing resource efficiency and reducing energy consumption are significant challenges in cloud environments. Placing virtual machines is essential in improving cloud systems’ performance. This paper presents a hybrid method using the artificial bee colony and imperialist competitive algorithm to reduce provider costs and decrease client expenditure. Implementation of the proposed plan in the CloudSim simulation environment indicates the proposed method performs better than the Monarch butterfly optimization and salp swarm algorithms regarding energy consumption and resource usage. Moreover, average central processing unit (CPU) and random-access memory (RAM) usage and the number of host shutdowns show better results for the proposed model.</span>

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Impact of Big Data and Cloud Computing on Data Analysis

Chakraborty¹,

Dey²

2023

Data-Intensive Research

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Introduction to Virtualization in Cloud Computing

Cited by 7 publications

References 0 publications

Optimized resource allocation in cloud computing for enhanced performance with modified particle swarm optimization

Optimized resource allocation in cloud computing for enhanced performance with modified particle swarm optimization

Virtual machine placement in cloud using artificial bee colony and imperialist competitive algorithm

Impact of Big Data and Cloud Computing on Data Analysis

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