Cloud computing migration and IT resources rationalization

Sefraoui, Omar; Aissaoui, Mohammed; Eleuldj, Mohsine

doi:10.1109/icmcs.2014.6911300

Cited by 10 publications

(13 citation statements)

References 5 publications

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“…The cloud computing is considered as a trending paradigm, it is mainly described as on-demand outsourcing model that brings together various technologies, namely the service-oriented architecture, the grid and utility computing (Sefraoui et al, 2014). It is considered as a hot topic for market analysts, enterprises and experts thanks to its ability to provide IT resources which are dynamically scalable, virtualised and exposed as a service on the internet (Sefraoui et al, 2014).…”

Section: Cloud Computing As a Promising Environment For Business Procmentioning

confidence: 99%

An end-to-end system to enhance business process performance through new outsourcing to the cloud strategies

Rekik¹,

Boukadi²,

Ben-Abdallah

2019

IJBSR

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This paper proposes an end-to-end system entitled business process outsourcing for performance enhancement (BPO4PE). It presents a deep analysis and research of the BPO to the cloud life cycle starting from the identification of the business process outsourcing (BPO) criteria with until the selection of business process activities to be outsourced as well as the most suitable infrastructure as a service (IaaS) offering to support their execution. We evaluate the system through different experiments that prove its good performance.

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Section: Cloud Computing As a Promising Environment For Business Procmentioning

confidence: 99%

An end-to-end system to enhance business process performance through new outsourcing to the cloud strategies

Rekik¹,

Boukadi²,

Ben-Abdallah

2019

IJBSR

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“…Some key players include Microsoft Windows Azure and Google Apps engine. The main benefits of these services include focusing on high‐value software rather than infrastructure, leveraging economies of scale, and providing scalable go‐to‐market capability . Software as a service provides clients the capability to use provider application executing on a cloud infrastructure. An entire application is available remotely and accessible from multiple client devices through thin client interfaces such as web browsers.…”

Section: Problem Definitionmentioning

confidence: 99%

Failover strategy for fault tolerance in cloud computing environment

et al. 2017

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Summary Cloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected system failure or malfunction, a robust fault‐tolerant design may allow the cloud to continue functioning correctly possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the application execution, and hardware performance, various fault‐tolerant techniques exist for building self‐autonomous cloud systems. In comparison with current approaches, this paper proposes a more robust and reliable architecture using optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using pass rates and virtualized mechanisms, the proposed smart failover strategy (SFS) scheme uses components such as cloud fault manager, cloud controller, cloud load balancer, and a selection mechanism, providing fault tolerance via redundancy, optimized selection, and checkpointing. In our approach, the cloud fault manager repairs faults generated before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and backward recovery using diverse software tools. Compared with existing approaches, preliminary experiment of the SFS algorithm indicates an increase in pass rates and a consequent decrease in failure rates, showing an overall good performance in task allocations. We present these results using experimental validation tools with comparison with other techniques, laying a foundation for a fully fault‐tolerant infrastructure as a service cloud environment. Copyright © 2017 John Wiley & Sons, Ltd.

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“…Some key players include Microsoft Windows Azure and Google Apps engine GoDaddy, Windows Azure, Apprenda, Google App Engine, Amazon Web Services, WordPress. The main benefit of these services include focus on high value software rather than infrastructure, leverage economies of scale and provide scalable go-to-market capability [15]. -SaaS provides clients the capability to use provider application executing on a cloud infrastructure.…”

Section: Introductionmentioning

confidence: 99%

“…Cloud user do not manage or control the underlying cloud infrastructure [2] but providers install and operate the application software. Example providers for this service include Salesforce, Facebook and Google Apps, Amazon EC2, Rackspace, Microsoft Azure, Google Compute Engine and Amazon Web Services [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Failure prediction using machine learning in a virtualised HPC system and application

et al. 2019

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Failure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular checkpointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the Support Vector Machine (SVM), Random Forest(RF), k-Nearest Neighbors (KNN), Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting fail

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Cloud computing migration and IT resources rationalization

Cited by 10 publications

References 5 publications

An end-to-end system to enhance business process performance through new outsourcing to the cloud strategies

An end-to-end system to enhance business process performance through new outsourcing to the cloud strategies

Failover strategy for fault tolerance in cloud computing environment

Failure prediction using machine learning in a virtualised HPC system and application

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