A Feasibility Study on Sustainability-Driven Infrastructure Management in Cloud Data Centers

Roque, Joseph; Chauvel, Lia; Aloqaily, Moayad; Kantarcı, Burak

doi:10.1109/ccece.2018.8447754

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…In the fourth term, the authors want to expand the framework to tackle aspects of quality of service (QoS) and quality of experience (QoE). Both aspects are very relevant aspects nowadays in the literature of the topic, e.g., [48][49][50][51][52][53][54]. While it is true that these nascent initiatives are more relevant to mobile networks, it is also true that these aspects are quite applicable also in organizational cloud scenarios.…”

Section: Discussion Limitations Conclusion and Future Workmentioning

confidence: 99%

Governance, Risk, and Compliance in Cloud Scenarios

Brandis¹,

Dzombeta²,

Colomo‐Palacios

et al. 2019

Applied Sciences

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Cloud computing is changing the way organizations approach technology and its infrastructure. However, in spite of its attractiveness, cloud computing can be seen as a threat in terms of compliance. Given its intrinsic distributed nature, regulations and laws may differ and customers and cloud providers must find a way to balance increasing compliance pressures with cloud computing benefits. In this paper, the authors present a framework aimed to help organizations to cope with compliance aspects in their cloud-oriented environments. Built upon current literature on the topic and qualitative approaches, the framework has been implemented in two organizations. Results from its contribution are encouraging, leading to adopter organizations to less reported compliance violations and higher contribution of cloud computing to overall quality of service and organizational compliance management.

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Section: Discussion Limitations Conclusion and Future Workmentioning

confidence: 99%

Governance, Risk, and Compliance in Cloud Scenarios

Brandis¹,

Dzombeta²,

Colomo‐Palacios

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The greatest challenge in cloud computing is related to sudden changes in traffic, 23 where the resilient and scalable nature of the cloud can be easily applied to traffic abnormalities. 24 Al-khafajiy et al 25 and Roque et al 26 proposed novel prediction strategies to tackle decision of load balancing among fog nodes. In this way, they aimed to propose a new load balancing method concerning latency of service request's received from things.…”

Section: Motivationmentioning

confidence: 99%

Cloud traffic prediction based on fuzzy ARIMA model with low dependence on historical data

Mehdi

Pooranian

Naranjo

2019

Trans Emerging Tel Tech

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Traffic prediction with high accuracy has become a vital and challenging issue for resource management in cloud computing. It should be noted that one of the prominent factors in resource management is accurate traffic prediction based on a few data points and within a short time period. The autoregressive integrated moving average (ARIMA) model is a suitable model to predict traffic in short time periods. However, it requires a massive amount of historical data to achieve accurate results. On the other hand, the fuzzy regression model is adequate for prediction using less historical data. Aforementioned by these considerations, in this paper, a combination of ARIMA and fuzzy regression called fuzzy autoregressive integrated moving average (FARIMA) is used to forecast traffic in cloud computing. Besides, we adopt the FARIMA model by using the sliding window, called SOFA, concept to determine models with higher prediction accuracy. Accuracy comparison of these models based on the root means square error and coefficient of determination demonstrates that SOFA is about 5.4 and 0.009, respectively, which is the superior model for traffic prediction.

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A Systematic Literature Review on Virtual Machine Consolidation

2021

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Virtual machine consolidation has been a widely explored topic in recent years due to Cloud Data Centers’ effect on global energy consumption. Thus, academia and companies made efforts to achieve green computing, reducing energy consumption to minimize environmental impact. By consolidating Virtual Machines into a fewer number of Physical Machines, resource provisioning mechanisms can shutdown idle Physical Machines to reduce energy consumption and improve resource utilization. However, there is a tradeoff between reducing energy consumption while assuring the Quality of Service established on the Service Level Agreement. This work introduces a Systematic Literature Review of one year of advances in virtual machine consolidation. It provides a discussion on methods used in each step of the virtual machine consolidation, a classification of papers according to their contribution, and a quantitative and qualitative analysis of datasets, scenarios, and metrics.

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A Feasibility Study on Sustainability-Driven Infrastructure Management in Cloud Data Centers

Cited by 3 publications

References 11 publications

Governance, Risk, and Compliance in Cloud Scenarios

Governance, Risk, and Compliance in Cloud Scenarios

Cloud traffic prediction based on fuzzy ARIMA model with low dependence on historical data

A Systematic Literature Review on Virtual Machine Consolidation

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