A Survey of Mobile Cloud Computing Application Models

Khan, Atta ur Rehman; Othman, Mazliza; Madani, Sajjad A.; Khan, Samee U.

doi:10.1109/surv.2013.062613.00160

Cited by 608 publications

(212 citation statements)

References 75 publications

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“…Apart from its flexibility, cloud computing addresses one of the challenges related to transferring and sharing data among others. Thus solution of Big data problems will resort to cloud computing to achieve high level of development [16].…”

Section: B Cloud Computingmentioning

confidence: 99%

Big Data Analytics: A Perspective View

Pandey¹

2017

IJARCSSE

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Abstract--Big data is a term coined for massive data sets with more varied and complex structure and also having the difficulties of storing, analysing and visualizing it for further usages. The process of diving into large amounts of data to discover patterns and disguised correlations is named as big data analytics. These are certainly useful to companies or organizations by gaining richer and deeper insights to compete in market. Thus big data implementations need to be analysed and executed efficiently as possible. Big data analytics is methodology which automates the gathering, organizing, contextualizing, processing and analysing Big data i.e. large set of data to capture patterns help make better decisions. Big data analytics challenges the situation of the present infrastructure of data storage management and also statistical data estimation. This paper studies the content, scope, methods, advantages and challenges of big data and also discusses privacy issue concern on it. The motive of the proposed study is to provide better and significant insights from research prospects and also lays an overview of data analysis methodologies and tools which are currently being utilized or proposed in literature. This work will be quite useful for the future researchers in this domain and facilitate the development of optimal techniques to address Big data.

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Section: B Cloud Computingmentioning

confidence: 99%

Big Data Analytics: A Perspective View

Pandey¹

2017

IJARCSSE

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“…The mobile specification can also change, for example when the user switches to a different mobile than the one used for profiling. In other words, static offloading decisions can easily fail in many scenarios [3]. Unfortunately, dynamic partitioning and profiling at runtime can consume considerable computational power and needs to be done in a timely fashion.…”

Section: F Challenges Of Mobile Application Cloud Offloadingmentioning

confidence: 99%

“…Mobile cloud computing [3] refers to mobile computing that exploits the theoretically infinite cloud resources to make up for the limited mobile phone resources. One approach involves saving data and especially Big Data on the cloud [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Mobile Application Task Offloading to the Cloud

Elazhary¹,

Aloraini²,

Aljuraid³

2017

ijacsa

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Abstract-One of the benefits of mobile cloud computing is the ability to offload mobile applications to the cloud for many reasons including performance enhancement and reduced resource consumption. This paper is concerned with offloading of context-aware mobile applications, in which actions or tasks are executed in certain contexts and offloading those tasks needs to be itself context-aware to be advantageous. The paper investigates candidate techniques and development models in the literature to identify suitable ones. Accordingly, the paper proposes the practical Context-Aware Mobile applications Offloading (CAMO) development model, which we developed in Java for the Android platform. Programmers can exploit the independency of the tasks of a typical context-aware mobile application and use CAMO to profile each task in isolation on the mobile and the cloud. The paper introduces the concept of a taskoffloading plan in which programmers specify a criterion and/or an objective for offloading a task in a specific context. Offloading criteria allow rapid offloading in case the mobile environment does not change frequently. Based on the profiling results, programmers can use the classes and methods of CAMO to develop one or more custom offloading plans for each task or use pre-specified plans, criterion and objectives. We provide three example tasks with details of their profiling and analysis for developing corresponding offloading plans. CAMO is general and flexible enough for offloading any application partitioned into independent modules. Empirical evaluation shows extreme satisfaction of mobile application developers with its capabilities.

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“…Mobile cloud computing also integrates the edge computing approach [22]. In mobile cloud computing an additional infrastructure layer between the client and the data center cloud infrastructure is proposed, located at the base stations of the mobile phone operators.…”

Section: Introductionmentioning

confidence: 99%

On edge microclouds to provide local container-based services

Baig

Centelles

Freitag

et al. 2017

2017 Global Information Infrastructure and Networking Symposium (GIIS)

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Abstract-Edge computing has been proposed to enable more user-centric cloud-based services. Nowadays, edge computing is operational in industrial and consumer-oriented scenarios. An important limitation of today's solutions, however, is that the used hardware and software platforms are proprietary and closed, and cannot easily be leveraged to perform other services beyond the specific business case. For instance, the interaction among different edge platforms or service extension by third parties is in general not supported. As a consequence, the opportunity for local stakeholders to provide innovative tailored edge service with these platforms face important barriers. In this paper we present edge microclouds deployed on local servers and an implementation using containers for service provision. We show how the adopted container approach facilitates the users to create and share services at the network edge. With the presented approach, third parties can deploy more tailored and customized services at the network edge, enabling to better fulfill specific local needs and constraints. It is also an opportunity for building cloudbased service provision with a pool of local resource-constraint edge devices.

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A Survey of Mobile Cloud Computing Application Models

Cited by 608 publications

References 75 publications

Big Data Analytics: A Perspective View

Big Data Analytics: A Perspective View

Context-Aware Mobile Application Task Offloading to the Cloud

On edge microclouds to provide local container-based services

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