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arXiv 1602.03350cs.NI
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物联网的查询处理:设备能耗与云基础设施计费的耦合

Query Processing For The Internet-of-Things: Coupling Of Device Energy Consumption And Cloud Infrastructure Billing

Francesco Renna, Joseph Doyle, Vasileios Giotsas, Yiannis Andreopoulos

摘要:

音频/视觉识别和检索应用最近在面向物联网(IoT)的服务中获得了极大的关注,因为即使在低端嵌入式系统中,视频摄像机和音频处理芯片组也无处不在。在这种服务的最典型场景中,每个设备提取音频/视觉特征并将其压缩成特征描述符,特征描述符包括媒体查询。这些查询被上传到远程云计算服务,该服务执行用于分类或检索应用的内容匹配。这些服务的两个最关键的方面是:(i)在使用服务时控制设备能耗;(ii)减少云基础设施提供商产生的计费成本。在本文中,我们推导出设备能耗和所产生的云基础设施计费之间的最佳耦合的分析条件。我们的框架包含:产生和传输音频/视频查询的能耗、云基础设施的计费率、同时连接到同一云服务器的设备数量以及每个设备的查询数据产生量的统计。我们的分析结果通过以下部署进行了验证:(i)设备端,包括在Beaglebone Linux嵌入式平台上计算的紧凑图像描述符(查询),并传输到Amazon Web Services(AWS)Simple Storage Service;(ii)云侧通过AWS Elastic Compute Cloud(EC2)spot实例进行图像相似性检测,AWS Auto Scaling用于根据需求控制实例数量。

英文摘要:

Audio/visual recognition and retrieval applications have recently garnered significant attention within Internet-of-Things (IoT) oriented services, given that video cameras and audio processing chipsets are now ubiquitous even in low-end embedded systems. In the most typical scenario for such services, each device extracts audio/visual features and compacts them into feature descriptors, which comprise media queries. These queries are uploaded to a remote cloud computing service that performs content matching for classification or retrieval applications. Two of the most crucial aspects for such services are: (i) controlling the device energy consumption when using the service; (ii) reducing the billing cost incurred from the cloud infrastructure provider. In this paper we derive analytic conditions for the optimal coupling between the device energy consumption and the incurred cloud infrastructure billing. Our framework encapsulates: the energy consumption to produce and transmit audio/visual queries, the billing rates of the cloud infrastructure, the number of devices concurrently connected to the same cloud server, and the statistics of the query data production volume per device. Our analytic results are validated via a deployment with: (i) the device side comprising compact image descriptors (queries) computed on Beaglebone Linux embedded platforms and transmitted to Amazon Web Services (AWS) Simple Storage Service; (ii) the cloud side carrying out image similarity detection via AWS Elastic Compute Cloud (EC2) spot instances, with the AWS Auto Scaling being used to control the number of instances according to the demand.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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