Cloud and IoT-based emerging services systems

Sharma, Shweta; Chang, Victor; Tim, U. Sunday; Wong, Johnny; Gadia, Shashi K.

doi:10.1007/s10586-018-2821-8

Cited by 43 publications

(21 citation statements)

References 41 publications

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“…Cloud computing is the provision of computational, networking, and storage resources to lessen the operational and financial burden of maintaining large-scale computing systems. Cloud computing service providers offer a plethora of services that enable enterprises to deploy business applications and benefit from large scale powerful data centers (Chang, 2014;Sharma et al, 2016). The typical infrastructure of a cloud computing system has three layers: i) infrastructure, ii) platform, and iii) application layer (Chang, Walters, & Wills, 2013).…”

Section: Cloud Computing For Enterprisesmentioning

confidence: 99%

Big data reduction framework for value creation in sustainable enterprises

Rehman

Chang

Batool

et al. 2016

International Journal of Information Management

Self Cite

221

146

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Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as a) lowering the service utilization cost, b) enhancing the trust between customers and enterprises, c) preserving privacy of customers, d) enabling secure data sharing, and e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-toend data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications.

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Section: Cloud Computing For Enterprisesmentioning

confidence: 99%

Big data reduction framework for value creation in sustainable enterprises

Rehman

Chang

Batool

et al. 2016

International Journal of Information Management

Self Cite

221

146

View full text Add to dashboard Cite

show abstract

“…For example in [18], the authors presented a new approach for horizontally scaling cloud resources, and in [19] for load balancing, and resources scheduling [20] in cloud. In [21] the authors presented the cloud concept and its emerged services that deal with the IoT trends, and they notice also that the applications with complex data-intensive computations are the best candidate to take advantages of cloud computing. Therefore, by applying the parallel and distributed simulation on cloud, the performance of these applications depends on the applied synchronization algorithms [22].…”

Section: B Resultsmentioning

confidence: 99%

Toward a New Massively Distributed Virtual Machine based Cloud Micro-Services Team Model for HPC: SPMD Applications

Benchara¹,

Youssfi²,

Bouattane³

et al. 2017

ijacsa

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Abstract-This paper aims to propose a new massively distributed virtual machine with scalable and efficient parallel computing models for High Performance Computing (HPC). The message passing paradigm of the Processing Units has a significant impact on HPC with high communication cost that penalizes the performance of these models. Accordingly, the proposed micro-services model allows the HPC applications to enhance the processing power with low communication cost. Thus, the model based Micro-services Virtual Processing Units (MsVPUs) cooperate using asynchronous communication mechanism through the Advanced Message Queuing Protocol (AMQP) protocol in order to maintain the scalability of the Single Program Multiple Data (SPMD) applications. Additionally, this mechanism enhances also the efficiency of the model based load balancing service with time optimized load balancing strategy. The proposed virtual machine is tested and validated through an application of fine grained parallel programs for big data classification. Experimental results present reduced execution time compared to the virtual machine based mobile agent's model.

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“…It enhances the efficiency and perfectness with cost optimality. The next level intelligence and its actionable insight gained using enabling technologies in the healthcare of asset management, cloud, IoT and Big Data (Sharma 2016(Sharma , 2015Sharma et al, 2015aSharma et al, , 2016. To reach such kind of new level of efficiency, perfectness and patient safety.…”

Section: Usability In Healthcare Systemmentioning

confidence: 99%

Histogram-based adaptive learning for background modelling: moving object detection in video surveillance

Sharma

Yadav

2017

IJTMCP

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The detection of moving object in the presence of complex or cluttered background is a very critical challenge. The moving object may be a person, patient, vehicle, animal or any tissue inside body in medical domain. In this context, this work has proposed a robust background subtraction method for resolving illumination variation and motion-based problems. Initially, this work has developed a background modelling method using initial few frames in training stage. In testing stage, a foreground modelling method is investigated that is able to detect moving object in video frames. In testing stage, this work classify moving pixel with a suitable threshold and update the background using appropriate learning rate. The learning rate is updated through histogram of classified resulting frame and background model. Finally, morphological filters and image processing techniques are applied to improve the detection quality. The employed method also demonstrates how it can be improved using adaptive learning rate-based controlling scheme and the incorporation of feedback-based model updating scheme. It clearly depicts strength of proposed method in handling illumination variation problems and also eliminating moving environmental effects. This method presents significant performance in comparison with considered state-of-the-art methods.

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Cloud and IoT-based emerging services systems

Cited by 43 publications

References 41 publications

Big data reduction framework for value creation in sustainable enterprises

Big data reduction framework for value creation in sustainable enterprises

Toward a New Massively Distributed Virtual Machine based Cloud Micro-Services Team Model for HPC: SPMD Applications

Histogram-based adaptive learning for background modelling: moving object detection in video surveillance

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