Lightweight Stream Processing Framework Based on Distributed Power Terminals

Distributed energy sources are widely connected to the power system, and the massive amount of data they generate poses a challenge to the computation capability of the power system. Many distributed energy sources have adopted edge computing to process data locally. Currently, edge computing often performs batch processing, which requires a certain amount of storage space and has a relatively high computational delay. This paper proposes a lightweight stream processing framework, which can be built on the edge computing terminals to improve the data processing efficiency. In addition, a task allocation algorithm for the lightweight edge stream processing framework is proposed, which effectively improves the resource utilization of each computing node in the stream computing framework. Finally, the effectiveness of the proposed algorithm is verified on Huawei LiteOS emulator.