PaTGen: Temporal Similarity-Driven Proxy Benchmark Generation Method for Cloud Workloads

The rapid expansion of cloud computing has made precise performance evaluation a critical necessity. However, conventional cloud benchmarks often face significant limitations in simulation environments—necessary for scalable and cost-effective testing—due to the complexity of technology stacks and substantial runtime overheads. Proxy benchmarking has thus emerged as a practical alternative. Existing methods primarily focus on the global similarity of micro-architectural metrics between proxy benchmarks and real workloads but neglect their temporal similarity, leading to inaccurate performance evaluations, flawed cache behavior simulations, and misguided architectural optimization decisions. To address this, we present PaTGen , a phase-aware method for generating proxy benchmarks that accurately reflect both global and temporal similarity. By partitioning workloads into phases and formulating proxy generation as nonlinear optimization problems, PaTGen further refines intra-phase execution patterns via the delay-based temporal similarity optimization (DTSO) technique. Evaluations on 15 real-world workloads show PaTGen achieves over 97% global similarity in key metrics while significantly outperforming state-of-the-art methods in temporal similarity. Ablation studies confirm the efficacy of phase division and DTSO. Further experiments confirm its scalability and generalizability across architectures. Moreover, the effectiveness observed in downstream tasks provides empirical evidence that preserving temporal similarity is a fundamental requirement for proxy benchmarks to faithfully capture real workload behavior.