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Auto-configuration System and Algorithms for Big Data-Enabled Internet-of-Things Platforms

机译：支持大数据的物联网平台的自动配置系统和算法

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摘要

Internet of Things (IoT) platforms that handle Big Data might perform poorly or not according to the goals of their operator (in terms of costs, database utilization, data quality, energy-efficiency, throughput) if they are not configured properly. The latter configuration refers mainly to system parameters of the data-collecting gateways, e.g., polling intervals, capture intervals, encryption schemes, used protocols etc. However, re-configuring the platform appropriately upon changes of the system context or the operator targets is currently not taking place. This happens because of the complexity or unawareness of the synergies between system configurations and various aspects of the Big Data-handling IoT platform, but also because of the human resources that an efficient re-configuration would require. This paper presents an auto-configuration solution based on interpretable configuration suggestions, focusing on the algorithms for computing the mentioned suggested configurations. Five such algorithms are contributed, while a thorough evaluation reveals which of these algorithms should be used in different operation scenarios in order to achieve high fulfillment of the operator's targets.
机译：如果未正确配置，则处理大数据的物联网（IoT）平台可能会根据其运营商的目标（在成本，数据库利用率，数据质量，能效，吞吐量方面）表现不佳或表现不佳。后一种配置主要是指数据收集网关的系统参数，例如轮询间隔，捕获间隔，加密方案，使用的协议等。但是，当前，根据系统上下文或操作员目标的更改适当地重新配置平台没有发生。之所以发生这种情况，是因为系统配置与大数据处理IoT平台各个方面之间的协同作用非常复杂，或者根本没有意识到，而且还因为需要有效的重新配置而需要的人力资源。本文提出了一种基于可解释配置建议的自动配置解决方案，重点是用于计算所建议配置的算法。贡献了五种这样的算法，同时进行了全面评估，发现应在不同的操作场景中使用哪种算法，以实现操作员目标的高度实现。

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