基于动态权衡模型与SDAPC框架的Hadoop生态系统优化:跨层级存储与计算协同及可信调度策略OA

To address the core bottlenecks of high latency and low utilization in Hadoop ecosystem resource scheduling for the Zettabyte(ZB)era,this study proposes a cross-layer collaborative optimization method driven by a dynamic trade-off model.A SDAPC collaborative optimization framework integrating quantum and classical algorithms is constructed.This framework first enhances data locality through storage-computation preprocessing to reduce problem complexity;its core innovation lies in mapping the YARN task allocation problem to an Ising model Hamiltonian minimization problem.Global optimization is achieved via D-Wave quantum annealing,while millisecond-level fine-grained adjustments are enabled by a graph attention network-driven deep reinforcement learning strategy.In TPC-DS benchmarks on a 256-node heterogeneous cluster,the method achieves ultra-low scheduling latency of 12.0±2.0ms and resource utilization of 87.3%±3.2%—reducing latency by 79.3%compared to conventional approaches,with GPU utilization fluctuation(σ)decreasing from 35%to 5%.