移动设备的云增强：动机、分类和开放性挑战
Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges
摘要：
最近，基于云的移动增强（CMA）方法已经从学术界和工业界获得了显着的基础。CMA是最先进的移动增强模型，它采用资源丰富的云来增加、增强和优化移动设备的计算能力，旨在执行资源密集型移动应用程序。增强型移动设备设想以最少的占用空间和脆弱性执行广泛的计算并存储超出其固有能力的大数据。研究人员利用各种基于云的计算资源（例如，远程云和附近的移动节点）以满足移动用户的各种计算需求。然而，采用基于云的计算资源并不是一个简单的灵丹妙药。理解影响增强过程的关键因素和基于云的资源类型的最佳选择是阻碍CMA适应性的一些挑战。本文全面调查移动增强领域，并提出了CMA方法的分类。本研究的目的是突出远程资源对增强过程的质量和可靠性的影响，并讨论在增强移动设备中采用各种基于云的资源的挑战和机遇。我们提出了增强的定义，动机和增强类型的分类，包括传统的和基于云的。我们批判性地分析了最先进的CMA方法，并将其分为四组：远距离固定，近距离固定，近距离移动和混合，以提出一个分类。重要的决策和性能限制因素，采用CMA方法的影响和未来CMA方法的决策流程图的示例。讨论了CMA方法对移动计算的影响，并指出了未来的研究方向。
英文摘要：
Recently, Cloud-based Mobile Augmentation (CMA) approaches have gained remarkable ground from academia and industry. CMA is the state-of-the-art mobile augmentation model that employs resource-rich clouds to increase, enhance, and optimize computing capabilities of mobile devices aiming at execution of resource-intensive mobile applications. Augmented mobile devices envision to perform extensive computations and to store big data beyond their intrinsic capabilities with least footprint and vulnerability. Researchers utilize varied cloud-based computing resources (e.g., distant clouds and nearby mobile nodes) to meet various computing requirements of mobile users. However, employing cloud-based computing resources is not a straightforward panacea. Comprehending critical factors that impact on augmentation process and optimum selection of cloud-based resource types are some challenges that hinder CMA adaptability. This paper comprehensively surveys the mobile augmentation domain and presents taxonomy of CMA approaches. The objectives of this study is to highlight the effects of remote resources on the quality and reliability of augmentation processes and discuss the challenges and opportunities of employing varied cloud-based resources in augmenting mobile devices. We present augmentation definition, motivation, and taxonomy of augmentation types, including traditional and cloud-based. We critically analyze the state-of-the-art CMA approaches and classify them into four groups of distant fixed, proximate fixed, proximate mobile, and hybrid to present a taxonomy. Vital decision making and performance limitation factors that influence on the adoption of CMA approaches are introduced and an exemplary decision making flowchart for future CMA approaches are presented. Impacts of CMA approaches on mobile computing is discussed and open challenges are presented as the future research directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
