Efficient resource utilization in Flink stream processing with accuracy awareness and load shedding strategies

Apache Flink and other stream processing frameworks have made large-scale, trustworthy data processing possible in real-time. An open-source distributed stream processor, Flink is built to club data from multiple sources and events with parallel processing. It is ideal for stateful, fault-tolerant stream processing applications. This paper presents an overview of Flink's architecture, which encompasses its dataflow paradigm, modular state backends, and distributed snapshots. It showcases Flink's adaptability in handling real-time analytics, uninterrupted streams, and batch workloads. The research technique suggests enhancing Flink with features including dynamic task scheduling, reinforcement learning for adaptive management, threshold-based load shedding for dynamic resource allocation, and fault tolerance through checkpoints. The use of the central processing unit (CPU) reaches its highest point of approximately 80% at 400 seconds, after which it begins to decrease. On the other hand, the utilization of the random-access memory (RAM) heap gradually climbs to 14 bytes at 900 seconds, with some slight oscillations. Apache Flink is capable of processing large amounts of streaming data effectively while making efficient use of its resources, including throughput, latency, CPU, and memory.