Reducing your local footprint with anyrun computing

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• 作者：Alan Ferrari ; ^{alan.ferrari@supsi.ch" class="auth_mail" title="E-mail the corresponding author} ; ^{alan.ferrari@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Silvia Giordano ^{silvia.giordano@supsi.ch" class="auth_mail" title="E-mail the corresponding author} ; Daniele Puccinelli ^{daniele.puccinelli@supsi.ch" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Code offloading ; Computation offloading ; Opportunistic computing ; Bayesian network ; Software profiling
• 刊名：Computer Communications
• 出版年：2016
• 出版时间：1 May 2016
• 年：2016
• 卷：81
• 期：Complete
• 页码：1-11
• 全文大小：846 K

文摘

Computational offloading is the standard approach to running computationally intensive tasks on resource-limited smart devices, while reducing the local footprint, i.e., the local resource consumption. The natural candidate for computational offloading is the cloud, but recent results point out the hidden costs of cloud reliance in terms of latency and energy. Strategies that rely on local computing power have been proposed that enable fine-grained energy-aware code offloading from a mobile device to a nearby piece of infrastructure. Even state-of-the-art cloud-free solutions are centralized and suffer from a lack of flexibility, because computational offloading is tied to the presence of a specific piece of computing infrastructure. We propose AnyRun Computing (ARC), a system to dynamically select the most adequate piece of local computing infrastructure. With ARC, code can run anywhere and be offloaded not only to nearby dedicated devices, as in existing approaches, but also to peer devices. We present a detailed system description and a thorough evaluation of ARC under a wide variety of conditions. We show that ARC matches the performance of the state-of-the-art solution (MAUI), in reducing the local footprint with stationary network topology conditions and outperforms it by up to one order of magnitude under more realistic topological conditions.