A lightweight process migration based computational offloading framework for mobile device augmentation / Abdullah
In recent years, the paradigm of mobile cloud computing has been introduced to extend capabilities of mobile devices, by taking advantage of high-speed wireless communications and high-performance cloud platforms to help gather, store and process data for the mobile devices. In this paradigm, the...
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| Format: | Thesis |
| Published: |
2017
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| Online Access: | http://studentsrepo.um.edu.my/7516/ http://studentsrepo.um.edu.my/7516/1/All.pdf http://studentsrepo.um.edu.my/7516/5/abdullah.pdf |
| Summary: | In recent years, the paradigm of mobile cloud computing has been introduced to
extend capabilities of mobile devices, by taking advantage of high-speed wireless communications
and high-performance cloud platforms to help gather, store and process data
for the mobile devices. In this paradigm, the cloud-based mobile applications usually employ
computational offloading for the augmentation of mobile device capabilities. Mobile
device OS vendors are focused toward native mobile applications lifecycle to improve
battery consumption and application execution performance. For example, Google
has introduced Android Runtime Environment (ART) featuring Ahead of Time (AHOT)
compilation to native instructions in place of Dalvik Virtual Machine (DVM) which consumes
extra time and energy because of the Just in Time (JIT) compilation. 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 lightweight computational offloading framework. The
lightweightedness is measured as the overhead energy consumption and application execution
time added up by the proposed framework. Further, we explain in details the
design and implementation of the proposed prototype framework. The proposed framework
requires infrastructural support from the remote computing platforms such as data
centers or cloudlets to provide Offloading as a Service (OaaS) for a heterogeneous mobile
cloud ecosystem. The proposed framework is evaluated using experimental testbed
and validated using statistical modeling. Numerical results from the testbed revealed that
the proposed framework saves almost 44% of the execution time and 84% of the energy
consumption of the experimental application used. |
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