BatchBench: Toward a Workload-Aware Benchmark for Autoscaling Policies in Big Data Batch Processing -- A Proposed Framework
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Budigi, Venkata Krishna Prasanth Sirigiri, Siri Chandana |
| author_facet | Budigi, Venkata Krishna Prasanth Sirigiri, Siri Chandana |
| contents | Autoscaling has become a baseline expectation for cloud-native big data processing, and the design space has expanded beyond rule-based heuristics to include learned controllers and, most recently, large language model (LLM) agents. Yet despite a growing body of work spanning these paradigms, the community lacks a shared benchmark for comparing them. Existing evaluations rely on synthetic TPC-style queries, vendor blog posts with proprietary baselines, or narrow trace replays. Each new policy reports favorable numbers against a different baseline, on a different workload, with a different cost model, making cross-paper comparison effectively impossible. This is a position paper. We propose BatchBench, an open benchmarking framework designed to place rule-based, learned, and agentic autoscaling policies on equal experimental footing. The contribution is the design of the framework, not empirical results. We contribute: (1) a workload taxonomy of six batch processing classes synthesized from published autoscaling benchmarks and publicly released cluster traces; (2) the design of a parameterized workload generator with a validation methodology based on two-sample Kolmogorov-Smirnov and earth-mover distance; (3) a five-axis evaluation harness specification covering cost, SLA attainment, scaling responsiveness, scaling thrash, and decision interpretability, with first-class accounting for LLM inference cost; and (4) a standardized agent interface that lets LLM-based and reinforcement-learning autoscalers be evaluated alongside rule-based controllers with a single API. We discuss the expected evaluation surface, identify open research questions the framework is designed to answer, and outline a roadmap for the empirical paper that will follow. BatchBench's reference implementation is in active development and will be released as open source. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12272 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BatchBench: Toward a Workload-Aware Benchmark for Autoscaling Policies in Big Data Batch Processing -- A Proposed Framework Budigi, Venkata Krishna Prasanth Sirigiri, Siri Chandana Information Retrieval Databases H.2.4; H.3.3; I.2.7 Autoscaling has become a baseline expectation for cloud-native big data processing, and the design space has expanded beyond rule-based heuristics to include learned controllers and, most recently, large language model (LLM) agents. Yet despite a growing body of work spanning these paradigms, the community lacks a shared benchmark for comparing them. Existing evaluations rely on synthetic TPC-style queries, vendor blog posts with proprietary baselines, or narrow trace replays. Each new policy reports favorable numbers against a different baseline, on a different workload, with a different cost model, making cross-paper comparison effectively impossible. This is a position paper. We propose BatchBench, an open benchmarking framework designed to place rule-based, learned, and agentic autoscaling policies on equal experimental footing. The contribution is the design of the framework, not empirical results. We contribute: (1) a workload taxonomy of six batch processing classes synthesized from published autoscaling benchmarks and publicly released cluster traces; (2) the design of a parameterized workload generator with a validation methodology based on two-sample Kolmogorov-Smirnov and earth-mover distance; (3) a five-axis evaluation harness specification covering cost, SLA attainment, scaling responsiveness, scaling thrash, and decision interpretability, with first-class accounting for LLM inference cost; and (4) a standardized agent interface that lets LLM-based and reinforcement-learning autoscalers be evaluated alongside rule-based controllers with a single API. We discuss the expected evaluation surface, identify open research questions the framework is designed to answer, and outline a roadmap for the empirical paper that will follow. BatchBench's reference implementation is in active development and will be released as open source. |
| title | BatchBench: Toward a Workload-Aware Benchmark for Autoscaling Policies in Big Data Batch Processing -- A Proposed Framework |
| topic | Information Retrieval Databases H.2.4; H.3.3; I.2.7 |
| url | https://arxiv.org/abs/2605.12272 |