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ADOMC-NPR Automatic Decision-Making Offloading Framework for Mobile Computation Using Nonlinear Polynomial Regression Model

ADOMC-NPR Automatic Decision-Making Offloading Framework for Mobile Computation Using Nonlinear Polynomial Regression Model

Abdulrahman Elhosuieny, Mofreh Salem, Amr Thabet, Abdelhameed Ibrahim
Copyright: © 2019 |Volume: 16 |Issue: 4 |Pages: 21
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781522564010|DOI: 10.4018/IJWSR.2019100104
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MLA

Elhosuieny, Abdulrahman, et al. "ADOMC-NPR Automatic Decision-Making Offloading Framework for Mobile Computation Using Nonlinear Polynomial Regression Model." IJWSR vol.16, no.4 2019: pp.53-73. http://doi.org/10.4018/IJWSR.2019100104

APA

Elhosuieny, A., Salem, M., Thabet, A., & Ibrahim, A. (2019). ADOMC-NPR Automatic Decision-Making Offloading Framework for Mobile Computation Using Nonlinear Polynomial Regression Model. International Journal of Web Services Research (IJWSR), 16(4), 53-73. http://doi.org/10.4018/IJWSR.2019100104

Chicago

Elhosuieny, Abdulrahman, et al. "ADOMC-NPR Automatic Decision-Making Offloading Framework for Mobile Computation Using Nonlinear Polynomial Regression Model," International Journal of Web Services Research (IJWSR) 16, no.4: 53-73. http://doi.org/10.4018/IJWSR.2019100104

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Abstract

Nowadays, mobile computation applications attract major interest of researchers. Limited processing power and short battery lifetime is an obstacle in executing computationally-intensive applications. This article presents a mobile computation automatic decision-making offloading framework. The proposed framework consists of two phases: adaptive learning, and modeling and runtime computation offloading. In the adaptive phase, curve-fitting (CF) technique based on non-linear polynomial regression (NPR) methodology is used to build an approximate time-predicting model that can estimate the execution time for spending the processing of the detected-intensive applications. The runtime computation phase uses the time predicting model for computing the predicted execution time to decide whether to run the application remotely and perform the offloading process or to run the application locally. Eventually, the RESTful web service is applied to carry out the offloading task in the case of a positive offloading decision. The proposed framework experimentally outperforms a competitive state-of-the-art technique by 73% concerning the time factor. The proposed time-predicting model records minimal deviation of the originally obtained values as it is applied 0.4997, 8.9636, 0.0020, and 0.6797 on the mean squared error metric for matrix-determinant, image-sharpening, matrix-multiplication, and n-queens problems, respectively.

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