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 |Pages: 21
DOI: 10.4018/IJWSR.2019100104
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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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1. Introduction

There are many interpretations of mobile cloud computing, and various works introduced distinct concepts for the mobile cloud computing. Essentially, the cloud computing is the transfer of the computing services through the Internet. Both software and hardware at distant locations are managed by individuals and businesses that support cloud services such as social networking sites, online file storage, webmail, and online business. Cloud computing models enable the management to computer resources and information from anywhere whenever a network connection is available. Cloud computing offers a group of shared resources that includes data storage, network channels, computer processing, and user applications (Shawish & Salama, 2014). Many quality factors of mobile cloud computing such as the low bandwidth, availability, and heterogeneity are categorized as mobile communications aspects. However, other issues such as the computation offloading through (static/dynamic) environments, as well security issues for which the mobile users or the applications can be considered from the computing side as shown in Figure 1 (Dinh et al., 2011).

Performance and battery lifetime shortages are extremely affected when processing heavy intensive-computation applications on limited resources devices especially portables such as tablets, smartphones, and PDAs. Researchers sought different solutions to get over these limitations. Delivering the intensive computation partitions of the applications to remote rich-resources servers was presented as a solution among many others (Kumar et al., 2013). This strategy which is known as the mobile computation offloading extends the mobile resources extended. Mobile computation offloading can be defined as launching the resource-intensive computation from limited-resource client to reachable rich-resource servers such as the cloud infrastructure. The computation offloading is required when a heavy intensive application is running on the device. Thus, these applications may need the access to resourceful machines instantly through wired or wireless networks. Virtualization which can be considered as the basic attribute of the cloud infrastructure may be used by theses servers (Kumar et al., 2013).

Figure 1.

Mobile cloud computing aspects


Cloud computing offers the services of offloading through flexible resources. Therefore, various applications and their data can be separated and manipulated. Thus, the computation offloading is initialized. Developers keep on upgrading the offloading infrastructures at various grades. Hence, offloading may be applied at different standards of virtual machines, applications, tasks and methods (Kumar et al., 2013; Akherfi et al., 2018). Thus, the offloading framework implements the offloading from the level of one method up to the whole application due to the nature of the application.

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