共享感知的基于云的移动外包

C. Mei, Daniel Taylor, Chenyu Wang, A. Chandra, J. Weissman
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引用次数: 19

摘要

移动设备,如智能手机和平板电脑,正在成为在线服务和应用程序的通用接口。然而,这种设备的计算能力和电池寿命有限,这限制了它们执行资源密集型应用程序的能力。计算外包给外部资源已经被提出作为一种技术来缓解这个问题。大多数关于移动外包的现有工作都集中在单个应用程序优化或外包到固定的本地资源上,并假设广域延迟非常高。然而,通过利用多个应用程序之间的关系和优化服务器配置来提高外包性能的机会被忽略了。在本文中,我们提出了一个基于Android/Amazon ec2的移动应用外包框架的设计和实现,利用云来实现可伸缩性、弹性和多用户代码/数据共享。使用这个框架,我们从经验上证明,云不仅是可行的,而且是可取的,作为一个卸载平台的延迟容忍应用程序。我们建议使用数据挖掘技术来检测跨多个应用程序的数据共享,并开发了新的调度算法,利用这种数据共享来提高外包性能。此外,我们的平台被设计为动态扩展,以同时支持大量移动用户。实验表明,我们提出的技术和算法大大提高了应用程序的性能,同时在计算资源和网络使用方面达到了很高的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sharing-Aware Cloud-Based Mobile Outsourcing
Mobile devices, such as smart phones and tablets, are becoming the universal interface to online services and applications. However, such devices have limited computational power and battery life, which limits their ability to execute resource-intensive applications. Computation outsourcing to external resources has been proposed as a technique to alleviate this problem. Most existing work on mobile outsourcing has focused on either single application optimization or outsourcing to fixed, local resources, with the assumption that wide-area latency is prohibitively high. However, the opportunity of improving the outsourcing performance by utilizing the relation among multiple applications and optimizing the server provisioning is neglected. In this paper, we present the design and implementation of an Android/Amazon EC2-based mobile application outsourcing framework, leveraging the cloud for scalability, elasticity, and multi-user code/data sharing. Using this framework, we empirically demonstrate that the cloud is not only feasible but desirable as an offloading platform for latency-tolerant applications. We have proposed to use data mining techniques to detect data sharing across multiple applications, and developed novel scheduling algorithms that exploit such data sharing for better outsourcing performance. Additionally, our platform is designed to dynamically scale to support a large number of mobile users concurrently. Experiments show that our proposed techniques and algorithms substantially improve application performance, while achieving high efficiency in terms of computation resource and network usage.
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