Context-Aware Mobile Cloud Computing and Its Challenges

Khan, Atta ur Rehman; Othman, Mazliza; Xia, Feng; Khan, Abdul Nasir

doi:10.1109/mcc.2015.62

Cited by 47 publications

(18 citation statements)

References 8 publications

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Section: Related Workmentioning

confidence: 99%

“…From the Cloud Computing standpoint, SDs were initially embraced by the Mobile Cloud Computing (MClC) paradigm, a subset of Cloud Computing. MClC augments devices capabilities by using resources (mainly power and storage) in remote servers to overcome smartphone constraints and enhancing users' experience . A related recent paradigm known as Edge/Fog Computing follows the same idea as MClC but uses nearby servers instead of remote Cloud resources, ie, in the same metropolitan area.…”

Section: Related Workmentioning

confidence: 99%

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DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

et al. 2019

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Summary Dew computing is an emerging computing paradigm, which aims at minimizing the dependency over existing internetwork back‐haul, ie, being dependent on processing resources offered by remote servers. Smartphones and tablets ubiquity and powerful computing hardware motivated researchers to investigate the way of providing Dew computing services by exploiting the aggregated capabilities of devices in a vicinity, a smart device cluster. Consequently, research on resource management is necessary to learn how to scavenge resources from such a cluster, deal with devices heterogeneity, limitations, and dynamic resource availability. Simulation is commonly practiced for studying resource management in other distributed computing research fields, specially due to the complexity involved in the set up of experiments. However, a free‐to‐use purpose specific toolkit for studying smart device clusters do not exist or have been documented. Current simulation efforts do not allow researchers to faithfully represent key singularities of such environment, which are energy depletion and nondedicated nature of computing resources. We propose a trace‐based toolkit built on modular software artifacts to speed up research in resource management techniques in Dew environments. A trace‐driven methodology is adopted to assure practical value of simulated scenarios. The toolkit comprises a device profiler application for Android to capture generic battery and CPU traces from real devices, a profile mixer to create user interaction baseline traces through generic ones, and an extensible engine to simulate the execution of workloads configurable via text files. Verification and validation tests were run to show correctness and reliability of our simulation approach.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

et al. 2019

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show abstract

“…Context-awareness has been associated with mobile application cloud offloading in many research studies to make informed dynamic offloading decisions at runtime since offloading is not always advantageous [29]. For example, Zhou et al [30] proposed a technique that enables making dynamic offloading decisions of an independent mobile application process at run time by selecting a suitable wireless channel and cloud resources that satisfy a set of quality-of-service (QoS) requirements while minimising cost and energy consumption.…”

Section: Context Awarenessmentioning

confidence: 99%

“…Many challenges face the success and adoption of mobile application cloud offloading [29]. For example, the execution time of a process (partition, independent module or an entire application) depends on the mobile specifications and the cloud specifications in addition to the input data size, which complicates the profiling process.…”

Section: F Challenges Of Mobile Application Cloud Offloadingmentioning

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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“…Such interface can, for example change the sound volume to silent automatically when in the operating room in a hospital. Mobile cloud computing [4] is concerned with mobile applications that bene!t from the cloud resources such as Big Data [5] and has been proposed for context-aware mobile applications [6]. Intelligent tutoring systems (ITSs) have been developed over the years for many domains such as database systems [7], genetics problem solving [8], mathematics [9], objectoriented analysis and design [10], and language learning [11].…”

Section: Introductionmentioning

confidence: 99%

Context-aware Cloud-based Mobile Application for Assessment and Training of Visual Cognitive Abilities

Elazhary

2017

Int. J. Interact. Mob. Technol.

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Abstract-Context-aware mobile applications can adapt to di�erent mobile, user and application contexts. Mobile cloud computing has been integrated with those applications to exploit the relatively infinite cloud resources. This paper proposes a context-aware cloud-based MObile application for assessment and training of visual Cognitive Abilities (MOCA). Those abilities, such as the visualization ability of recognizing rotated objects, constitute an integral part of student intelligence. The need to ubiquitously and continuously deliver exercises relevant to a speci!c visual cognitive ability or skill according to the student pro!ciency and context has stimulated proposing MOCA. Integrating cloud computing with MOCA allows creating an extendible repository on the cloud such that the visual material does not affect and is not affected by the relatively limited mobile resources. In MOCA, we propose a hierarchical data structure suitable for the assessment of the various cognitive abilities and skills in terms of related ones. MOCA is also a framework for building applications based on visual cognitive abilities, such as teaching visual science concepts and the visual classi!cation and diagnosis of medical images, and possibly training and assessment systems for other types of cognitive abilities. Two prototype mobile applications have been developed based on MOCA for the visualization ability and for visual classi!cation of science concepts. Empirical evaluation has shown the effectiveness of MOCA in training the students and the satisfaction of the students and teachers with its capabilities.

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Context-Aware Mobile Cloud Computing and Its Challenges

Cited by 47 publications

References 8 publications

DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments

Context-Aware Mobile Application Task Offloading to the Cloud

Context-aware Cloud-based Mobile Application for Assessment and Training of Visual Cognitive Abilities

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