Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. (January 2016)

• Record Type: Journal Article
• Authors: Gai, Keke; Qiu, Meikang; Zhao, Hui; Tao, Lixin; Zong, Ziliang
• Is Part Of: Journal of network and computer applications. Volume 59(2016)
• Page Start: 46
• Page End: 54
• Publication Date: 2016-01
• Subjects: Mobile cloud computing -- Green computing -- Dynamic program -- Energy-aware -- Cloudlets; 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.2015.05.016
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Employing mobile cloud computing (MCC) to enable mobile users to acquire benefits of cloud computing by an environmental friendly method is an efficient strategy for meeting current industrial demands. However, the restrictions of wireless bandwidth and device capacity have brought various obstacles, such as extra energy waste and latency delay, when deploying MCC. Addressing this issue, we propose a dynamic energy-aware cloudlet-based mobile cloud computing model (DECM) focusing on solving the additional energy consumptions during the wireless communications by leveraging dynamic cloudlets (DCL)-based model. In this paper, we examine our model by a simulation of practical scenario and provide solid results for the evaluations. The main contributions of this paper are twofold. First, this paper is the first exploration in solving energy waste problems within the dynamic networking environment. Second, the proposed model provides future research with a guideline and theoretical supports. Abstract : Highlights: The first attempt for extending the functionality of cloudlets. Achieve energy-aware performances in the dynamic networking environment. Provide future research in the field with the theoretical support and exploring directions. The model may be migrated and applied in multiple industries.