Co-Cache: Inertial-Driven Infrastructure-less Collaborative Approximate Caching
Co-Cache: Inertial-Driven Infrastructure-less Collaborative Approximate Caching
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DOI：
10.1109/secon55815.2022.9918552
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
James Mariani;Yongqi Han;Li Xiao
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文献类型：
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作者：
James Mariani;Yongqi Han;Li Xiao
许多新兴的多媒体移动的应用严重依赖于静态图像和实况视频流两者的图像识别。图像识别通常使用深度神经网络（DNN）来实现，深度神经网络可以实现高精度，但也会在资源受限的智能手机上产生显著的计算延迟和能耗。最近解决这些问题的努力包括云卸载和降低DNN的复杂性，然而，这会增加网络延迟或降低准确性。内存缓存也被用来评估图像的相似性，而不是精确匹配。然而，这种近似缓存系统通常将设备视为静态节点，并且在没有外部基础设施的情况下不能充分利用智能电话的移动的和协作性质。将节点视为静态的另一个后果是，高速缓存大小必须大于单个移动的应用程序的可行大小。在本文中，我们介绍了Co-Cache，一个内存缓存范例，支持智能手机图像识别中的无基础设施的协作计算重用。Co-Cache利用智能手机的惯性运动、视频流中固有的局部性以及来自附近对等设备的信息，最大限度地提高移动的图像识别中的计算重用机会。与其他缓存系统相比，我们的广泛评估表明，Co-Cache可以将所需的缓存条目数量减少50- 70%，同时将标准图像识别应用程序的平均延迟降低高达94%，识别准确性损失最小。
Many emerging multimedia mobile applications rely heavily upon image recognition of both static images and live video streams. Image recognition is commonly achieved using deep neural networks (DNNs) which can achieve high accuracy but also incur significant computation latency and energy con-sumption on resource-constrained smartphones. Recent efforts addressing these issues include cloud offloading and reducing the complexity of the DNNs, which, however, introduce increased network latency or reduced accuracy. In-memory caching has also been explored to assess the similarity of images as opposed to exact matching. However, such approximate caching systems often treat devices as static nodes, and do not fully utilize the mobile and collaborative nature of smartphones without outside infrastructure. Another consequence of treating nodes as static is the necessity of cache sizes larger than what is feasible for individual mobile applications. In this paper we introduce Co-Cache, a in-memory caching paradigm that supports infrastructure-less collaborative compu-tation reuse in smartphone image recognition. Co-Cache utilizes the inertial movement of smartphones, the locality inherent in video streams, as well as information from nearby, peer-to-peer devices to maximize the computation reuse opportunities in mobile image recognition. Compared to other caching systems, our extensive evaluation shows that Co-Cache can reduce the required number of cache entries by 50–70 % while lowering the average latency of standard image recognition applications by up to 94 % with minimal loss of recognition accuracy.