Cloud Computing Virtualization: A Comprehensive Survey

Rista, Amarildo; Ajdari, Jaumin; Zenuni, Xhemal

doi:10.23919/mipro48935.2020.9245124

Cited by 11 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The challenge with this framework is that performance increases the service cost. 82 Kundu et al 74 Efficient resources allocation to dynamic requests for revenue maximization. MaxGain, MaxLoss, and MaxRevenue algorithms were used to select the best economical VMs.…”

Section: Major Contribution Limitationsmentioning

confidence: 99%

Revenue maximization approaches in IaaS clouds: Research challenges and opportunities

Badshah

Ghani

Daud

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

This study critically reviews the IaaS clouds development on revenue maximization since 2012 to answer these research queries; (i) What are the main influential factors towards revenue maximization in the cloud market? (ii) What are the main challenges and resistance towards revenue maximization in cloud computing? and (iii) What are the possible solutions and potentials to these hurdles in cloud computing? The data was analyzed and the influencing factors of revenue maximization were classified into seven distinct categories, that is, the performance of the services, service level agreement and penalties management, resources scalability, resources utilization and scheduling, customers' satisfaction, cost, and pricing management, as well as advertisement and auction. These parameters are investigated in detail and new dynamics for researchers in the field of the cloud are discovered. These studies are compared against each other for the seven distinct categories and solutions are proposed for the clouds' obstacles to revenue maximization. Furthermore, in the light of the findings and revenue maximization categories, the main limitations, challenges, true potential, and new directions towards revenue maximization are explored.

show abstract

Section: Major Contribution Limitationsmentioning

confidence: 99%

Revenue maximization approaches in IaaS clouds: Research challenges and opportunities

Badshah

Ghani

Daud

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Using ( 27), the expected overall TR time, E R O S , for the semiperfect virtualization mode is given by where α is given by ( 1), E[R L ] by (9), and E R R S by (21).…”

Section: Semiperfect Virtualization (Uniform Service Time)mentioning

confidence: 99%

“…One of the most vital topics in fog/cloud computing is virtualization [9]. It allows making maximum use of existing hardware, by sharing existing assets, leading to decreased capital cost and increased network efficiency [10].…”

Section: Introductionmentioning

confidence: 99%

Characterization of task response time in fog enabled networks using queueing theory under different virtualization modes

et al. 2022

View full text Add to dashboard Cite

Much research has focused on task offloading in fog-enabled IoT networks. However, there is an important offloading issue that has hardly been addressed—the impact of different virtualization modes on task response (TR) time. In the present article, we bridge this gap, introducing three virtualization modes, and characterizing the TR time under each. In each mode the virtual machines (VM) at the fog are customized differently, leveraging VM elasticity. In the perfect virtualization mode, the VM is customized to match exactly the computational load of the incoming task. This ensures that each task, regardless of which VM it goes to, will have the same service time. In the semiperfect virtualization mode, a less stringent, thus more practical, alternative, the VM is customized to match roughly the computational load of the incoming task. This results in a uniformly distributed task service time. Finally, in the baseline virtualization mode, the VM is customized to just be fast, with no regard to the computational load of the incoming task. This mode, which just re-scales the processing time of the task, is the default in existing research, and is re-introduced here for only comparison purposes. We characterize the TR time for the three modes leveraging M/G/1 and M/G/m queueing models, with the queueing stability condition identified for each mode. The obtained analytical results are successfully validated by discrete event Monte Carlo simulation. The numerical results show that the first mode results in the shortest TR time, followed by the second mode, then the third mode. That is, if virtualization is managed adequately, significant improvement in TR time can be gained.

show abstract

“…The integration of virtualisation technologies in industry, such as those of cloud computing and mobile edge computing, is indispensable when adapting to the evolving demands of modern industrial landscapes. This leads to the next generation of distributed systems, where there is a clear symbiosis between hardware and software with impacts on key factors such as scalability, accessibility, and real-time data processing [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios

Gama Garcia,

Alcaraz Calero,

Mora Mora

et al. 2024

Sensors

View full text Add to dashboard Cite

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.

show abstract

Cloud Computing Virtualization: A Comprehensive Survey

Cited by 11 publications

References 42 publications

Revenue maximization approaches in IaaS clouds: Research challenges and opportunities

Revenue maximization approaches in IaaS clouds: Research challenges and opportunities

Characterization of task response time in fog enabled networks using queueing theory under different virtualization modes

Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios

Contact Info

Product

Resources

About