Context-Aware Prediction Model for Offloading Mobile Application Tasks to Mobile Cloud Environments

Context-Aware Prediction Model for Offloading Mobile Application Tasks to Mobile Cloud Environments

Hamid A. Jadad (SQU, Muscat, Oman), Abederezak Touzene (SQU, Muscat, Oman), Khaled Day (SQU, Muscat, Oman), Nasser Alziedi (SQU, Muscat, Oman) and Bassel Arafeh (University of Louisville, Louisville, USA)
Copyright: © 2019 |Pages: 17
DOI: 10.4018/IJCAC.2019070104

Abstract

Offloading intensive computation parts of the mobile application code to the cloud computing is a promising way to enhance the performance of the mobile device and save the battery consumption. Recent works on mobile cloud computing mainly focus on making a decision of which parts of application may be executed remotely, assuming that mobile and server processors have no other loads, mobile battery always full of charge, and have static network bandwidth. However, the mobile cloud environment parameters changes continuously. In this paper, the authors propose a new offloading approach which uses cost models to decide at runtime either to offload execution of the code to the remote cloud or not. This article considers the dynamic changes of the mobile cloud environment in the system cost models. Moreover, this article enhances the offloading process by considering parallel execution of application independent tasks in the cloud. The evaluation results show that the approach reduces the execution time and battery consumption by 75% and 55%, respectively, compared with existing offloading approaches.
Article Preview

Introduction

The rapid increase in the usage of mobile devices can be shown by the total number of mobile subscriptions which reached 4.7 billion at the end of 2015(David, Jan, Mike, & Pau, 2015). Nowadays, smartphones have become more popular, and are close to everyone due to improved computing services and mobility. This popularity of smartphones has increased rapidly the demand on mobile applications. For example, in July 2015, Google Play stored around 2.2 million mobile apps, while Apple Store apps reached 2 million apps (Statista, 2016). These mobile apps vary between many categories such as entertainments, business, health care, and education. On the other hand, applications like multimedia processing, video gaming, speech recognition, and natural language processing which demand a high level of computing power are restricted by the limitations of mobile processing power and battery life of the mobile devices.

Recently, cloud computing has changed the way of delivering computing services. Where cloud providers can offer Infrastructure, Platform, and Software as services to the end user at lower cost. Cloud characteristics such as flexibility, reliability, and cost effectiveness are the major benefits to move to the cloud. Recent research on cloud computing issues and challenges are discussed in (Gupta, Gupta, & Chaudhary, 2017; Jararweh, Al-Ayyoub, Fakirah, Alawneh, & Gupta, 2017; Li, Gupta, & Metere, 2018; Nagar & Suman, 2016; Negi, Mishra, & Gupta, 2013; Ratten, 2015; Stergiou, Psannis, Kim, & Gupta, 2018).

Mobile cloud computing is a new paradigm that appeared from merging cloud computing and mobility. It allows the mobile users to utilize the cloud services on demand. It is envisioned that this paradigm will help overcoming the limitations of the mobile device hardware. Many research studies have explored how to utilize the cloud to overcome the limitations of mobile resources (Chun, Ihm, & Maniatis, 2011; Cuervo et al., 2010; Fernando, Loke, & Rahayu, 2013; Kosta, Aucinas, Hui, Mortier, & Zhang, 2012). In(Fernando et al., 2013), the authors have proposed a taxonomy of mobile cloud computing based on the key issues and how they have been tackled in research. One of the key issues is job offloading which consists of migration of jobs (data or code) that takes place from the resource constrained mobile device to the cloud. Issues related to the transfer through a wireless communication channel and the storage of the user jobs in the cloud opens new problems of privacy and security (Ibtihal, Hassan, & others, 2017; Zkik, Orhanou, & El Hajji, 2017). Cryptography tools might be used to solve user job’s confidentiality (B. Gupta, Agrawal, & Yamaguchi, 2016; Ustimenko & Touzene, 2004).

Code offloading and code partitioning are common solutions used to overcome mobile hardware limitations. Code partitioning is more popular in current mobile cloud applications (e.g. Google Translate, Gmail, and Google Voice). In this solution, the application is partitioned at design phase where all or most of the computational operations are designed to run in the cloud side, while the mobile device runs as a thin-client that displays received data from the cloud (Flores, Srirama, & Buyya, 2014). This approach extends mobile capabilities, but cannot work without connection to the cloud. Many works have been done based on the code offloading approach (Chun et al., 2011; Cuervo et al., 2010; Kosta et al., 2012) due to the capability of an application to either run locally on a mobile device or to offload some of its computationally expensive code to be executed outside the mobile device when a connection is available. It is argued that, it is not convenient to offload the entire application code because some parts of the code need local sensors (e.g. GPS, camera). Moreover, the offloading cost (e.g. battery consumption, delay time) may outweigh the offloading benefits. Thus, deciding which part of the code to offload, where, when, and how are important questions for the code offloading approach.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 10: 4 Issues (2020): Forthcoming, Available for Pre-Order
Volume 9: 4 Issues (2019): 3 Released, 1 Forthcoming
Volume 8: 4 Issues (2018)
Volume 7: 4 Issues (2017)
Volume 6: 4 Issues (2016)
Volume 5: 4 Issues (2015)
Volume 4: 4 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing