A Framework for Seamless Execution of Mobile Applications in the Cloud

Lee, Byoung-Dai

doi:10.1007/978-3-642-25766-7_20

Cited by 12 publications

(18 citation statements)

References 12 publications

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“…COMET (Code Offload by Migrating Execution Transparently), a runtime offloading environment towards augmenting smartphones is proposed in [72] to improve computation speed but it consumes more bandwidth. A framework is proposed in [73], to support seamless mobile cloud applications execution that significantly reduces latency and power consumption. Cuckoo, a dynamic runtime system for computation offloading [74] is suitable for compute intensive operations.…”

Section: Comparative Analysis Of Existing Frameworkmentioning

confidence: 99%

Multi-access edge computing: open issues, challenges and future perspectives

Shahzadi¹,

et al. 2017

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Latency minimization is a pivotal aspect in provision of real time services while adhering to Quality of Experience (QoE) parameters for assuring spectral efficiency. Edge Cloud Computing, being a potential research dimension in the realm of 5G networks, targets to enhance the network efficiency by harnessing effectiveness of both cloud computing and mobile devices in user's proximity. Keeping in view the far ranging impact of Edge Cloud Computing in future mobile generations, a comprehensive review of the prevalent Edge Cloud Computing frameworks and approaches is presented with a detailed comparison of its classifications through various QoS metrics (pertinent to network performance and overheads associated with deployment/migration). Considering the knowledge accumulated, procedures analysed and theories discussed, the paper provides a comprehensive overview on sate-of-the-art and future research directions for multi-access mobile edge computing.

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Section: Comparative Analysis Of Existing Frameworkmentioning

confidence: 99%

Multi-access edge computing: open issues, challenges and future perspectives

Shahzadi¹,

et al. 2017

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“…For our experiments, we implemented prototype mobile applications by applying the FREEMA framework [14] that enables seamless execution of Android applications in the cloud. The applications are a face recognition application and a video transcoding application, which are frequently used in mobile environments.…”

Section: Prototype Applications and Test Environmentsmentioning

confidence: 99%

“…Recently, cloud computing has emerged as a solution to the inherent resource limitation of mobile devices. In the cloud-based approaches, applications on the mobile device can offload their computation to the cloud to conserve energy, as well as augment the computation and memory capabilities of the mobile device ( [5], [6], [9], [11], [13], [14], [16]). Computation offloading reduces the workload on the mobile device but it requires wireless communication to migrate the computation to more resource-rich clouds.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Computation Offloading Decision for Energy-Efficient Execution of Mobile Applications in Clouds

Lee

Cheng

Choi

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYIn recent years, computation offloading, through which applications on a mobile device can offload their computations onto more resource-rich clouds, has emerged as a promising technique to reduce battery consumption as well as augment the devices' limited computation and memory capabilities. In order for computation offloading to be energyefficient, an accurate estimate of battery consumption is required to decide between local processing and computation offloading. In this paper, we propose a novel technique for estimating battery consumption without requiring detailed information about the mobile application's internal structure or its execution behavior. In our approach, the relationship is derived between variables that affect battery consumption (i.e., the input to the application, the transmitted data, and resource status) and the actual consumed energy from the application's past run history. We evaluated the performance of the proposed technique using two different types of mobile applications over different wireless network environments such as 3G, WiFi, and LTE. The experimental results show that our technique can provide tolerable estimation accuracy and thus make correct decisions between local processing and computation offloading.

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“…However, owing to the nature of the mobile environment, devices run on batteries, which is a limited energy source. Therefore, as mobile environments are dependent on energy consumption, it limits various services that can be provided in the mobile environment [1][2]. Various studies are under way to overcome this limit on energy [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, as mobile environments are dependent on energy consumption, it limits various services that can be provided in the mobile environment [1][2]. Various studies are under way to overcome this limit on energy [1][2][3][4][5]. Recently, aside from internal approaches to solving energy problems by improved battery technology or energy saving, new studies on mobile cloud computing investigating combinations of mobile devices and the cloud as an external resource have been attempted to improve energy efficiency and overcome resource limitations in mobile devices.…”

Section: Introductionmentioning

confidence: 99%

History-based dynamic estimation of energy consumption for mobile applications

Cheng

Lee

2014

16th International Conference on Advanced Communication Technology

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Abstract-Mobile cloud computing is one of the primary research areas that aims to overcome the problem of limited mobile device energy. For this purpose, computational tasks of mobile applications that consume a high amount of energy are offloaded to the cloud. In order to perform effective computation offloading, the energy consumed by application programs in mobile devices and the cloud needs to be estimated first. Previous studies have proven that energy consumption estimators showed optimal performance with targeted mobile application programs. However, optimal performance has yet to be achieved with other mobile application programs. Thus, this paper proposes a dynamic energy consumption estimator in which a mobile application program selects an appropriate energy consumption estimator for its own benefit. In addition, performance and effectiveness of the proposed dynamic energy consumption estimator are validated through experiments.

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A Framework for Seamless Execution of Mobile Applications in the Cloud

Cited by 12 publications

References 12 publications

Multi-access edge computing: open issues, challenges and future perspectives

Multi-access edge computing: open issues, challenges and future perspectives

An Adaptive Computation Offloading Decision for Energy-Efficient Execution of Mobile Applications in Clouds

History-based dynamic estimation of energy consumption for mobile applications

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