IoTDataBench：扩展TPCx-IoT用于压缩和Scalability
IoTDataBench: Extending TPCx-IoT for Compression and Scalability
摘要：
我们提出了一个破纪录的结果和经验教训，在实践TPCx-IoT基准测试的真实世界的用例。我们发现，更多的系统特性需要进行基准测试，以将其应用于现实世界的用例。我们引入了TPCx-IoT基准测试的扩展，涵盖物联网基础设施时间序列数据管理的基本要求。我们将其描述为数据压缩和系统可扩展性。为了评估物联网数据库的这两个重要特征，我们提出了IoTDataBench，并更新了TPCx-IoT的四个方面，即，数据生成、工作负载、指标和测试程序。初步评估结果表明，未能有效压缩数据或灵活扩展的系统可能会对重新设计的指标产生负面影响，而具有高压缩比和线性可扩展性的系统在最终指标中得到奖励。这样的系统能够按需扩大计算资源，从而可以节省美元成本。
英文摘要：
We present a record-breaking result and lessons learned in practicing TPCx-IoT benchmarking for a real-world use case. We find that more system characteristics need to be benchmarked for its application to real-world use cases. We introduce an extension to the TPCx-IoT benchmark, covering fundamental requirements of time-series data management for IoT infrastructure. We characterize them as data compression and system scalability. To evaluate these two important features of IoT databases, we propose IoTDataBench and update four aspects of TPCx-IoT, i.e., data generation, workloads, metrics and test procedures. Preliminary evaluation results show systems that fail to effectively compress data or flexibly scale can negatively affect the redesigned metrics, while systems with high compression ratios and linear scalability are rewarded in the final metrics. Such systems have the ability to scale up computing resources on demand and can thus save dollar costs.
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