Abstract
With the continuous development of Internet technology, a large amount of data with rich value has been generated, and lagging data analysis will affect the timeliness of data, so real-time streaming data processing is becoming increasingly important. Storm is a pure streaming data processing framework, but it uses the polling scheduling algorithm by default. This algorithm ignores the network communication overhead between workers and cluster load balancing. Aiming at Storm’s default scheduling problem, a non-cooperative game-based Storm scheduling algorithm (G-Storm) was proposed. Storm extracts the source data in real time through the component “Spout”, passes it to the logical processing component “Bolt”, and finally loads it into the target warehouse. The experimental results show that the game scheduling algorithm proposed in this paper reduces the system processing delay by 28.6% compared with the default scheduling algorithm.
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