Browsing by Author "Mei, Chonglei"

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    Dynamic Outsourcing Mobile Computation to the Cloud
    (2011-03-14) Mei, Chonglei; Shimek, James; Wang, Chenyu; Chandra, Abhishek; Weissman, Jon
    Mobile devices are becoming the universal interface to online services and cloud computing applications. Since mobile phones have limited computing power and battery life, there is a potential to migrate computation intensive application components to external computing resources. The Cloud is an attractive platform for offloading due to elastic resource provisioning and the ability to support large scale service deployment. In this paper,we discuss the potential for offloading mobile computation for different computation patterns and analyze their tradeoffs. Experiments show that the we can achieve a 27 fold speedup for an image manipulation application and 1.47 fold speedup for a face recognition application. In addition, this outsourcing model can also result in power saving depending on the computation pattern, offloading configuration, and execution environment.
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    Mobilizing the Cloud: Enabling Multi-User Mobile Outsourcing in the Cloud
    (2011-11-21) Mei, Chonglei; Taylor, Daniel; Wang, Chenyu; Chandra, Abhishek; Weissman, Jon
    Mobile devices, such as smartphones 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 rich, resource-intensive applications. Mobile computation outsourcing to external resources has been proposed as a technique to alleviate this problem. Most existing work on mobile outsourcing has focused either on single application optimization, or outsourcing to fixed, local resources, with the assumption that wide-area latency is prohibitively high. In this paper, we present the design and implementation of an Android/Amazon EC2-based mobile application outsourcing platform, leveraging the cloud for scalability, elasticity, and multi-user code/data sharing. Using this platform, we empirically demonstrate that the cloud is not only feasible but desirable as an offloading platform for latency tolerant applications despite wide-area latencies. Our platform is designed to dynamically scale to support a large number of mobile users concurrently by utilizing the elastic provisioning capabilities of the cloud, as well as by allowing reuse of common code components across multiple users. Additionally, we have developed techniques for detecting data sharing across multiple applications, and proposed novel scheduling algorithms that exploit such data sharing for better scalability and user performance. Experiments with our offloading platform show that our proposed techniques and algorithms substantially improve application performance, while achieving high efficiency in terms of resource and network usage.