Cloud Computing: A Survey on Cloud Computing

Beri, Rydhm; Behal, Veerawali

doi:10.5120/19622-1385

Cited by 16 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Even though, data storage, regarding the large amount of data, represents the advantage of Cloud Computing, the yearly expenses of data storage represent the obstacle for implementation of this solution. The expenses increase even more if the amount of data increased and if data analysis and processing is one of the paid requirements, besides data storage [6], [53], [54]. Noticeably, the Edge Computing solutions are not inexpensive itself.…”

Section: Challenges Of Cloud Computing and Opportunities Of Edge Computing In Data Analytics Perspectivementioning

confidence: 99%

Edge Computing vs. Cloud Computing: Challenges and Opportunities in Industry 4.0

Bajić¹,

Ćosić²,

Katalinic³

et al. 2019

DAAAM Proceedings

View full text Add to dashboard Cite

With the technological development of advanced technologies and the use of the Internet of Things (IoT), the number of connected devices is increasing in manufacturing processes. As devices become more and more incorporated using more processing power, the big data is generated. However, increasing the generation of big data leads to problems related to processing and analysis. The current tendency of solving the problems of processing and analysis is via Cloud Computing technologies. However, more attention is dedicated of performing computations as close to the device as possible, relying on Edge Computing technologies. Motivated by these facts, this paper provides a comparative analysis of the roles of edge computing and cloud computing, summarizing challenges and opportunities of these technologies and providing their application in Industry 4.0.

show abstract

Section: Challenges Of Cloud Computing and Opportunities Of Edge Computing In Data Analytics Perspectivementioning

confidence: 99%

Edge Computing vs. Cloud Computing: Challenges and Opportunities in Industry 4.0

Bajić¹,

Ćosić²,

Katalinic³

et al. 2019

DAAAM Proceedings

View full text Add to dashboard Cite

show abstract

“…In simple terms, cloud computing delivers computing services to users at the time, location, and quantity they want, and at a cost that is based only on what is used [2], [15], [16]. It provides ubiquitous, convenient, and on-demand access to a shared pool of massively scaled resources that can be rapidly provisioned [2], [17].…”

Section: Cloud Computingmentioning

confidence: 99%

“…National Institute of Standards and Technology (NIST) provides a definition of cloud computing that is cited and re-used by others [2], [15]- [17]. It describes cloud computing as having the five essential characteristics of "on-demand self-service," "broad network access," "resource pooling," "rapid elasticity," and "measured service" [18, p. 2].…”

Section: Cloud Computingmentioning

confidence: 99%

Latent Effects of Cloud Computing on IT Capacity Management Structures

Bauer¹,

Bellamy²

2017

IJCCE

View full text Add to dashboard Cite

Cloud computing is a disruptive technology providing the occasion for change in the sociotechnical structures of IT capacity management, but what are the latent effects of this? With interviews from ten case study organizations, this qualitative research generates a model that describes a spectrum of IT capacity-management structures, from classic to cloud, and describes the patterned differences that were discovered. Among the ten organizations studied, the latent, or unintended consequences of IT capacitymanagement trying to "stay relevant" during a transition to cloud computing adoption appears to lead to its own obsolescence. This analysis can be used as a platform for more targeted hypothesis testing to provide evidence for or against the generalization and external validity of this exploratory research.

show abstract

“…Cloud provides us three different services based on three delivery structures. These are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) [2].…”

Section: Introductionmentioning

confidence: 99%

BHyPreC: A Novel Bi-LSTM Based Hybrid Recurrent Neural Network Model to Predict the CPU Workload of Cloud Virtual Machine

Karim

Maswood²,

Das

et al. 2021

IEEE Access

View full text Add to dashboard Cite

With the advancement of cloud computing technologies, there is an ever-increasing demand for the maximum utilization of cloud resources. It increases the computing power consumption of the cloud's systems. Consolidation of cloud's Virtual Machines (VMs) provides a pragmatic approach to reduce the energy consumption of cloud Data Centers (DC). Effective VM consolidation and VM migration without breaching Service Level Agreement (SLA) can be attained by taking proactive decisions based on cloud's future workload prediction. Effective task scheduling, another major issue of cloud computing also relies on accurate forecasting of resource usage. Cloud workload traces exhibit both periodic and non-periodic patterns with the sudden peak of load. As a result, it is very challenging for the prediction models to precisely forecast future workload. This prompted us to propose a hybrid Recurrent Neural Network (RNN) based prediction model named BHyPreC. BHyPreC architecture includes Bidirectional Long Short-Term Memory (Bi-LSTM) on top of the stacked Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). Here, BHyPreC is used to predict future CPU usage workload of cloud's VM. Our proposed model enhances the non-linear data analysis capability of Bi-LSTM, LSTM, and GRU models separately and demonstrates better accuracy compared to other statistical models. The effect of variation of historical window size and training-testing data size on these models is observed. The experimental result shows that our model gives higher accuracy and performs better in comparison to Autoregressive Integrated Moving Average (ARIMA), LSTM, GRU, and Bi-LSTM model for both short-term ahead and long-term ahead prediction.INDEX TERMS Cloud Computing, deep learning, recurrent neural network, time series analysis, virtual machine, workload prediction.

show abstract

Cloud Computing: A Survey on Cloud Computing

Cited by 16 publications

References 7 publications

Edge Computing vs. Cloud Computing: Challenges and Opportunities in Industry 4.0

Edge Computing vs. Cloud Computing: Challenges and Opportunities in Industry 4.0

Latent Effects of Cloud Computing on IT Capacity Management Structures

BHyPreC: A Novel Bi-LSTM Based Hybrid Recurrent Neural Network Model to Predict the CPU Workload of Cloud Virtual Machine

Contact Info

Product

Resources

About