Techniques to Minimize State Transfer Costs for Dynamic Execution Offloading in Mobile Cloud Computing

Yang, Su‐Geun; Kwon, Donghyun; Yi, Hayoon; Cho, Yeongpil; Kwon, Yongin; Paek, Yunheung

doi:10.1109/tmc.2014.2307293

Cited by 60 publications

(39 citation statements)

References 9 publications

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“…B.Aggarwal et al [9] proposed the framework for energy in mobile communication by using cloud environment. Seungjun Yang et al [10] analyzed the transfer and cost minimization of the offloading methodology by using cloud. [11]- [16] discussed and analyzed the application performances on computation, storage and energy based analysis by using cloud computing environment.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Cloud Assisted Adaptive Video Streaming to Enhance User QoE

Rajasekaran¹,

Keerthiga²

2018

IJCA

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The drastic increase in the usage of handheld devices like smartphones, tablets, etc., produce huge traffic on the Internet which are mainly due the video streaming services applications. Due to the limitations of resources (Power, Memory, Processing, etc.), numerous design patterns of the mobile devices and heterogeneous users' expectations, it is difficult to handle those video streaming services requests. In order to provide an expected experience to the end user and to reduce the burden of the service providers, cloud computing provides the most easy and viable solution. A novel framework in cloud environment has been proposed in this paper to provide a good trade-off between the service handling as well as the user experience. The framework covers various aspects related to streaming services such as user experience, content distribution and retrieval, power usage, network analysis, video storage, device analysis on static and runtime conditions, parallel service provision and the experience perceived by user. General TermsCloud Based Video Streaming Services

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Section: Related Workmentioning

confidence: 99%

A Framework for Cloud Assisted Adaptive Video Streaming to Enhance User QoE

Rajasekaran¹,

Keerthiga²

2018

IJCA

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“…In our work, the framework manages a number of available offloading service nodes for component offloading. For efficient data transmission over the wireless network, our proposal also considers transmitted data size like Yang's work [23] and extra software module installation. Moreover, in order to eliminate the node selection workload for a client mobile node, the framework selects one or more available offloading service nodes from the separate dispatcher and learner node.…”

Section: Related Workmentioning

confidence: 99%

Predictive Offloading Method and Framework Design Model for Efficient Mobile Cloud Computing

Piao¹,

Kim²

2017

IJCA

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“…These ALVMs require massive modifications to enable thread-state synchronization mechanisms. CloneCloud [10] and its variants [31] as well as COMET [32] exploit the concept of thread migration to improve the overall performance of mobile devices. CloneCloud [10] uses a combination of static analysis and dynamic profiling to partition applications at runtime.…”

Section: Thread-state Migrationmentioning

confidence: 99%

Computational offloading mechanism for native and android runtime based mobile applications

Yousafzai

Gani

Noor

et al. 2016

Journal of Systems and Software

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Mobile cloud computing is a promising approach to augment the computational capabilities of mobile devices for emerging resource-hungry mobile applications. Android-based smartphones have opened real-world venues for mobile cloud applications mainly because of the open source nature of Android. Computational offloading mechanism enables the augmentation of smartphone capabilities. The problem is majority of existing computational offloading solutions for Android-based smartphones heavily depends on Dalvik VM (an application-level VM). Apart from being a discontinued product, Dalvik VM consumes extra time and energy because of the just-intime (JIT) compilation of bytecode into machine instructions. With regard to this problem, Google has introduced Android Runtime (ART) featuring ahead-of-time (AHOT) compilation to native instructions in place of Dalvik VM. However, current state-of-the-art offloading solutions do not consider AHOT compilations to native binaries in the ART environment. To address the issue in offloading ART-based mobile applications, we propose a computational offloading framework. The proposed framework requires infras- * Corresponding author tructural support from cloud data centers to provide offloading as a service for heterogeneous mobile devices. Numerical results from proof-of-concept implementation revealed that the proposed framework improves the execution time of the experimental application by 76% and reduces its energy consumption by 70%.

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Techniques to Minimize State Transfer Costs for Dynamic Execution Offloading in Mobile Cloud Computing

Cited by 60 publications

References 9 publications

A Framework for Cloud Assisted Adaptive Video Streaming to Enhance User QoE

A Framework for Cloud Assisted Adaptive Video Streaming to Enhance User QoE

Predictive Offloading Method and Framework Design Model for Efficient Mobile Cloud Computing

Computational offloading mechanism for native and android runtime based mobile applications

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