Mobile App Acceleration via Fine-Grain Offloading to the Cloud

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Mobile App Acceleration via Fine-Grain Offloading to the Cloud

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Title: Mobile App Acceleration via Fine-Grain Offloading to the Cloud
Author: Lin, Chit-Kwan; Kung, H. T.

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Citation: Lin C-K, and HT Kung. 2014. "Mobile App Acceleration via Fine-Grain Offloading to the Cloud." Presented at the 6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud '14), June 17-18, 2014, Philadelphia, PA.
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Abstract: Mobile device hardware can limit the sophistication of mobile applications. One strategy for side-stepping these constraints is to opportunistically offload computations to the cloud, where more capable hardware can do the heavy lifting. We propose a platform that accomplishes this via compressive offloading, a novel application of compressive sensing in a distributed shared memory setting. Our prototype gives up to an order-of-magnitude acceleration and 60% longer battery life to the end user of an example handwriting recognition app. We argue that offloading is beneficial to both end users and cloud providers—the former experiences a performance boost and the latter receives a steady stream of small computations to fill periods of under-utilization. Such workloads, originating from ARM-based mobile devices, are especially well-suited for offloading to emerging ARM-based data centers.
Published Version: https://www.usenix.org/conference/hotcloud14/workshop-program/presentation/lin
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:12965652
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