Scalable Edge Computing Framework for Real-Time Data Processing in Fintech Applications
Keywords:
Real-Time Financial Systems, Distributed Computing, Data Synchronization, Fintech Applications, Network OptimizationAbstract
The research investigates an edge-computing architecture designed to meet the stringent latency and scalability needs of financial technology applications. By positioning edge nodes close to end users for local data processing, the system effectively reduces network transmission delays, a key factor in real-time financial transactions. Using predictive caching algorithms informed by Markov models, frequently accessed data is pre-stored at these nodes, significantly improving retrieval times. Our findings show a notable 38% reduction in overall latency compared to traditional centralized architectures, with edge processing delays consistently below 120 ms in high-frequency transaction environments. Moreover, the architecture’s scalability was tested under varying load conditions, demonstrating robust performance and effective data synchronization with a two-phase protocol to maintain consistency across distributed nodes. This edge-based approach offers a promising solution for enhancing responsiveness in fintech systems, laying the groundwork for financial services capable of meeting modern performance demands.
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