Research of Key Technologies of Distributed Stream Processing Based on <scp>FaaS</scp>

ABSTRACT Serverless computing has emerged as a promising paradigm for cloud‐based stream processing applications characterized by fluctuating workloads and latency sensitivity. While existing Function‐as‐a‐Service (FaaS) implementations primarily focus on homogeneous CPU/memory resource scaling, they fail to address the challenges of heterogeneous resource management and coordinated elasticity in distributed stream processing. This study proposes HFaaS, a novel serverless framework that integrates dataflow programming with heterogeneous resource orchestration for stream processing applications. The key innovations include: (1) a dataflow‐oriented function composition model enabling dynamic scaling of individual processing stages through peer‐to‐point communication mechanisms, (2) a fine‐grained GPU resource allocation strategy achieving 15% + utilization improvement through device sharing and elastic scaling capabilities, and (3) a locality‐aware scheduling algorithm optimizing task placement based on data proximity and heterogeneous resource availability. Experimental results demonstrate that HFaaS effectively coordinates multi‐stage function scaling while maintaining sub‐second latency guarantees. The proposed resource allocation strategy improves GPU utilization by 15.2% compared to conventional static allocation approaches, with network overhead reduced by 31.6% through data‐local scheduling. This work bridges the gap between serverless architectures and modern stream processing requirements, providing a unified platform for building resource‐efficient, latency‐sensitive distributed applications in heterogeneous cloud environments.