Thousands of network cameras stream real-time visual data for different environments, such as streets, shopping malls, and natural scenes. The big visual data from these cameras can be useful for many applications, but analyzing the large quantities of data requires significant amounts of resources. These resources can be obtained from cloud vendors offering cloud instances (referred to as instances in this paper) with different capabilities and hourly costs. It is a challenging problem to manage cloud resources to reduce the cost for analyzing the big real-time visual data from network cameras while meeting the performance requirements. That is because the problem is affected by many factors related to the analysis programs, the cameras, and the instances. This paper proposes a cloud resource manager (referred to as manager in this paper) that aims at solving this problem. The manager estimates the resource requirements of analyzing the data stream from each camera, formulates the resource allocation problem as a 2D vector bin packing problem, and solves it using a heuristic algorithm. The resource manager monitors the allocated instances; it allocates more instances if needed and deallocates existing instances to reduce the cost if possible. The experiments show that the resource manager is able to reduce up to 60 percent of the overall cost. The experiments use multiple analysis programs, such as moving objects detection, feature tracking, and human detection. One experiment analyzes more than 97 million images (3.3 TB of data) from 5,310 cameras simultaneously over 24 hours using 15 Amazon EC2 instances costing $188.

中文翻译：

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用于分析来自网络摄像机的大实时视觉数据的云资源管理

数以千计的网络摄像机针对不同环境（例如街道、购物中心和自然场景）传输实时视觉数据。来自这些相机的大视觉数据可用于许多应用程序，但分析大量数据需要大量资源。这些资源可以从提供不同功能和每小时成本的云实例（本文中称为实例）的云供应商处获得。如何在满足性能要求的同时降低分析来自网络摄像机的大实时视觉数据的成本，管理云资源是一个具有挑战性的问题。那是因为问题受到许多与分析程序、相机和实例相关的因素的影响。本文提出了一种旨在解决这个问题的云资源管理器（本文简称为管理器）。管理器估计分析来自每个摄像头的数据流的资源需求，将资源分配问题表述为二维向量装箱问题，并使用启发式算法解决该问题。资源管理器监控分配的实例；它会根据需要分配更多实例，并在可能的情况下取消分配现有实例以降低成本。实验表明，资源管理器能够减少高达 60% 的总成本。实验使用多种分析程序，例如运动物体检测、特征跟踪和人体检测。一项实验分析了来自 5 个国家的超过 9700 万张图像（3.3 TB 数据），