In the Internet of Things, edge computing decreases the latency between things and the central computing service commonly deployed on the cloud. However, as edge devices are located at the edge and closer to things, the type and scale of hardware capabilities are below the commodity hardware. This resulted in limited, in terms of computational power, deployments of Time-Series Databases (TSDB). Consequently, these TSDB do not have enough buffer space to handle an increase in data ingestion rates, causing a high loss rate of data points and sometimes database crashes. In this work, we introduce a lightweight Time-Series Rate Limiter (TSRL) deployed between the clients and the TSDB to boost its scalability and decrease the loss rate due to higher ingestion rates. A buffering of requests and a re-buffering of failed requests increase the chances of data points being successfully inserted. The proposed TSRL is modular, meaning it can be deployed without any changes to the existing TSDB engine. To evaluate the proposed solution, the IoTBenchmark tool is used to compare the performance of InfluxDB with and without a TSRL. The results demonstrate significant performance improvements across multiple metrics. It shows an increase in successfully ingested points and overall throughput, alongside a decrease in both median latency and peak memory usage. The system's scalability also improved with more allocated TSDB memory and with a higher number of concurrent clients. Additionally, an enhancement in query performance is observed.

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