چارچوب مدل سازی و شبیه سازی برای رایانش ابری موبایل
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|74137||2015||17 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Simulation Modelling Practice and Theory, Volume 58, Part 2, November 2015, Pages 140–156
Mobile cloud computing (MCC) is an emerging paradigm for transparent elastic augmentation of mobile devices capabilities, exploiting ubiquitous wireless access to cloud storage and computing resources. MCC aims at increasing the range of resource-intensive tasks supported by mobile devices, while preserving and extending their resources. Its main concerns regard the augmentation of energy efficiency, storage capabilities, processing power and data safety, to improve the experience of mobile users. The design of MCC systems is a challenging task, because both the mobile device and the Cloud have to find energy-time tradeoffs and the choices on one side affect the performance of the other side. The analysis of the MCC literature points out that all existing models focus on mobile devices, considering the Cloud as a system with unlimited resources. Also, to the best of our knowledge, no MCC-specific simulation tool exists. To fill this gap, in this paper, we propose a modeling and simulation framework for the design and analysis of MCC systems, encompassing all their components. The main pillar of the proposed framework is the autonomic strategy consisting of adaptive loops between every mobile devices and the Cloud. The proposed model of the mobile device takes into account online estimations of the actual Cloud performance – not only the nominal values of the performance indicators. At the same time, the model of the Cloud takes into consideration the characteristics of the workload, to adapt its configuration in terms of active virtual machines and task management strategies. Moreover, the developed discrete event simulator is an effective tool for the evaluation of an MCC system as a whole, or single components, considering different classes of parallel jobs.