A lightweight mobile cloud computing framework for resource-intensive mobile application / Saeid Abolfazli Torghabeh
Resource-intensive Mobile Application (RMA) execution is inhibited by mobile de- vice constrained resources, particularly CPU, RAM, storage, and battery. However, Mo- bile Cloud Computing (MCC) as the state-of-the-art mobile computing paradigm is aiming to augment computing capabilities of mobile...
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| 格式: | Thesis |
| 出版: |
2014
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| 主題: | |
| 在線閱讀: | http://studentsrepo.um.edu.my/4643/1/thesis.pdf http://studentsrepo.um.edu.my/4643/ |
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| 總結: | Resource-intensive Mobile Application (RMA) execution is inhibited by mobile de-
vice constrained resources, particularly CPU, RAM, storage, and battery. However, Mo-
bile Cloud Computing (MCC) as the state-of-the-art mobile computing paradigm is aiming
to augment computing capabilities of mobile devices, mitigate their resource-deficiency,
and realize efficient execution of RMA. MCC solutions dominantly perform remote ex-
ecution of resource-intensive RMAs’ components using resources-rich Distant Immobile
Cloud (DIC), particularly public cloud. Although DICs feature high availability and elas-
tic scalability, they are characterized by high communication latency and lack of mobility.
Therefore, performance gains of mobile augmentation using DIC are mitigated and RMA
execution efficiency is remarkably degraded. In this study, we aim to achieve efficient
execution of RMAs by proposing a lightweight MCC framework. We verify the problem
significance by analyzing time and energy overheads of exploiting DICs for augmenting
resource-constraint mobile devices. Results of our analysis unveil that communication
latency of utilizing DICs due to manifold intermediate hops between mobile device and
DICs significantly prolongs application execution time and expedites energy dissipation
in resource-constraint mobile devices. To address the problem, we propose a lightweight
MCC framework that enables usage of multitude of proximate resource-rich mobile de-
vices that can provide computing services to the mobile users in vicinity. The proposed
framework is evaluated using benchmarking experiments and validated using statistical
modeling. The evaluation results advocate that leveraging our proposed framework can
substantially reduce RMAs’ execution time up to 91:4% and conserve energy of resource-
constraint mobile device as significant as 81%. |
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