Partitioning and offloading in smart mobile devices for mobile cloud computing: State of the art and future directions. (1st October 2018)

• Record Type: Journal Article
• Authors: Gu, Fei; Niu, Jianwei; Qi, Zhiping; Atiquzzaman, Mohammed
• Is Part Of: Journal of network and computer applications. Volume 119(2018)
• Page Start: 83
• Page End: 96
• Publication Date: 2018-10-01
• Subjects: Mobile cloud computing -- Partitioning -- Offloading -- Computation and communication overheads -- Energy consumption; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2018.06.009
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Mobile applications, such as augment reality, natural language processing, object recognition and multimedia-based services, are becoming increasingly ubiquitous and can provide better user experience on mobile devices. However, such applications are draining increasingly more computational resources and energy of mobile devices. Partitioning and offloading some parts of mobile applications is a promising method of saving time and energy for mobile devices with con-strained resources. Most of current researches have focused on designing effective offloading systems to save time and energy while giving limited consideration to enabling techniques. Moreover, researchers have seldom paid attention to different computation resources in offloading frameworks. In this paper, we present the state-of-the-art enabling techniques and different computational resources used for partitioning and offloading. We also investigate mobile cloud computing and relevant paradigms from the perspective of partitioning and offloading enabling techniques. Then we propose a taxonomy of these partitioning and offloading techniques and survey some well-established offloading frameworks. Based on our taxonomy, we compare these offloading frameworks and show some offloading schemes. Furthermore, we discuss open research issues and challenges to partitioning and offloading.