A model for power efficiency of mobile devices through lightweight method level computational offloading

Mobile devices have become an integral part of our daily lives. However, the restricted battery timing curtails longer operational hours. To tackle the limited battery timing issue, a technique, computational offloading is used. In computational offloading, the intensive tasks are offloaded from mob...

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Main Author: Ali, Mushtaq
Format: Thesis
Language:English
English
English
Published: 2018
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/23465/
http://umpir.ump.edu.my/id/eprint/23465/
http://umpir.ump.edu.my/id/eprint/23465/1/A%20model%20for%20power%20efficiency%20of%20mobile%20devices%20through%20lightweight%20method%20level%20computational%20offloading%20-%20Table%20of%20contents.pdf
http://umpir.ump.edu.my/id/eprint/23465/2/A%20model%20for%20power%20efficiency%20of%20mobile%20devices%20through%20lightweight%20method%20level%20computational%20offloading%20-%20Abstract.pdf
http://umpir.ump.edu.my/id/eprint/23465/3/A%20model%20for%20power%20efficiency%20of%20mobile%20devices%20through%20lightweight%20method%20level%20computational%20offloading%20-%20References.pdf
id ump-23465
recordtype eprints
spelling ump-234652019-01-02T02:21:37Z http://umpir.ump.edu.my/id/eprint/23465/ A model for power efficiency of mobile devices through lightweight method level computational offloading Ali, Mushtaq QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Mobile devices have become an integral part of our daily lives. However, the restricted battery timing curtails longer operational hours. To tackle the limited battery timing issue, a technique, computational offloading is used. In computational offloading, the intensive tasks are offloaded from mobile devices to remote server in order to execute the task remotely and save battery life. Computational offloading frameworks/models based on VM migration, whole application migration, or traditional method level offloading are resources intensive and time consuming. The dynamic partitioning of application, execution of task at cloud server, service call by Simple Object Access Protocol (SOAP) and no defined mechanism for predefined parameters, make the previous method level computational frameworks/models inefficient for energy saving. In order to address the inefficiencies of previous method level computational offloading frameworks/models, a lightweight method level computational offloading model is proposed. Four distinct components are deployed in the proposed model which eliminates the shortcomings of previously developed frameworks/models. A Representational State Transfer (REST) based technique developed for calling the remote services which is based on JSON instead of XML, and hence is lightweight. REST also reduces the size of communication data at approximately 100% as compared to SAOP service call. Surrogate server is configured at a single hop distance which reduces the RTT and ultimately reduces the power consumption. The application is partitioned at method level by a novel dynamic technique in source code, which counters the inefficiencies of existing partitioning techniques. A mechanism for selection of predefined parameters is defined. These parameters are important to consider before each offload. The predefined parameters consist of battery level, network type, and execution time which affirms the energy saving during offloading. The proposed framework is implemented in the real mobile cloud computing environment. Execution time and energy consumption of both local execution and traditional offloading are benchmarked in order to investigate and validate the performance of the proposed lightweight method level model. The prototype is developed with three components which are REST-Offload, Local Execution and Traditional- Offload and then tested in real mobile cloud environment for Execution Time and Energy Consumption. The result of this research indicates that the proposed solution diminishes resources utilization. The REST-Offload is significantly useful compared to both Local Execution and Traditional Offloading methods. It reduces about 50% Execution Time and approximately 38% Energy Consumption. 2018-02 Thesis NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23465/1/A%20model%20for%20power%20efficiency%20of%20mobile%20devices%20through%20lightweight%20method%20level%20computational%20offloading%20-%20Table%20of%20contents.pdf pdf en http://umpir.ump.edu.my/id/eprint/23465/2/A%20model%20for%20power%20efficiency%20of%20mobile%20devices%20through%20lightweight%20method%20level%20computational%20offloading%20-%20Abstract.pdf pdf en http://umpir.ump.edu.my/id/eprint/23465/3/A%20model%20for%20power%20efficiency%20of%20mobile%20devices%20through%20lightweight%20method%20level%20computational%20offloading%20-%20References.pdf Ali, Mushtaq (2018) A model for power efficiency of mobile devices through lightweight method level computational offloading. PhD thesis, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:104341&theme=UMP2
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
English
topic QA75 Electronic computers. Computer science
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA75 Electronic computers. Computer science
TK Electrical engineering. Electronics Nuclear engineering
Ali, Mushtaq
A model for power efficiency of mobile devices through lightweight method level computational offloading
description Mobile devices have become an integral part of our daily lives. However, the restricted battery timing curtails longer operational hours. To tackle the limited battery timing issue, a technique, computational offloading is used. In computational offloading, the intensive tasks are offloaded from mobile devices to remote server in order to execute the task remotely and save battery life. Computational offloading frameworks/models based on VM migration, whole application migration, or traditional method level offloading are resources intensive and time consuming. The dynamic partitioning of application, execution of task at cloud server, service call by Simple Object Access Protocol (SOAP) and no defined mechanism for predefined parameters, make the previous method level computational frameworks/models inefficient for energy saving. In order to address the inefficiencies of previous method level computational offloading frameworks/models, a lightweight method level computational offloading model is proposed. Four distinct components are deployed in the proposed model which eliminates the shortcomings of previously developed frameworks/models. A Representational State Transfer (REST) based technique developed for calling the remote services which is based on JSON instead of XML, and hence is lightweight. REST also reduces the size of communication data at approximately 100% as compared to SAOP service call. Surrogate server is configured at a single hop distance which reduces the RTT and ultimately reduces the power consumption. The application is partitioned at method level by a novel dynamic technique in source code, which counters the inefficiencies of existing partitioning techniques. A mechanism for selection of predefined parameters is defined. These parameters are important to consider before each offload. The predefined parameters consist of battery level, network type, and execution time which affirms the energy saving during offloading. The proposed framework is implemented in the real mobile cloud computing environment. Execution time and energy consumption of both local execution and traditional offloading are benchmarked in order to investigate and validate the performance of the proposed lightweight method level model. The prototype is developed with three components which are REST-Offload, Local Execution and Traditional- Offload and then tested in real mobile cloud environment for Execution Time and Energy Consumption. The result of this research indicates that the proposed solution diminishes resources utilization. The REST-Offload is significantly useful compared to both Local Execution and Traditional Offloading methods. It reduces about 50% Execution Time and approximately 38% Energy Consumption.
format Thesis
author Ali, Mushtaq
author_facet Ali, Mushtaq
author_sort Ali, Mushtaq
title A model for power efficiency of mobile devices through lightweight method level computational offloading
title_short A model for power efficiency of mobile devices through lightweight method level computational offloading
title_full A model for power efficiency of mobile devices through lightweight method level computational offloading
title_fullStr A model for power efficiency of mobile devices through lightweight method level computational offloading
title_full_unstemmed A model for power efficiency of mobile devices through lightweight method level computational offloading
title_sort model for power efficiency of mobile devices through lightweight method level computational offloading
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/23465/
http://umpir.ump.edu.my/id/eprint/23465/
http://umpir.ump.edu.my/id/eprint/23465/1/A%20model%20for%20power%20efficiency%20of%20mobile%20devices%20through%20lightweight%20method%20level%20computational%20offloading%20-%20Table%20of%20contents.pdf
http://umpir.ump.edu.my/id/eprint/23465/2/A%20model%20for%20power%20efficiency%20of%20mobile%20devices%20through%20lightweight%20method%20level%20computational%20offloading%20-%20Abstract.pdf
http://umpir.ump.edu.my/id/eprint/23465/3/A%20model%20for%20power%20efficiency%20of%20mobile%20devices%20through%20lightweight%20method%20level%20computational%20offloading%20-%20References.pdf
first_indexed 2023-09-18T22:35:08Z
last_indexed 2023-09-18T22:35:08Z
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