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To enhance resource utilization in data centers, it is common to co-locate latency-sensitive services and batch jobs on the same host, which leads to batch-to-online (B2O) and intra-batch (B-B) interference due to resource contention. Therefore, it is crucial to schedule jobs effectively to alleviate both types of interference. While most prior studies focus on B2O interference, B-B interference is often overlooked. By analyzing production data, we observe three insights that reveal the relationships between job behaviors and the two types of interference. We propose DIJS, a dual interference-aware job scheduling framework. DIJS predicts the resource usage patterns and resource sensitivity of new batch jobs. Based on this, it designs two quantizers using reinforcement learning (RL) and attention mechanisms to assess the impact of B2O and B-B interference. Experiments show that DIJS reduces service tail latency by about \(41.0\%\) and enhances job efficiency by up to \(28.6\%\) .

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DIJS: A Dual Interference-Aware Job Scheduling Framework for Co-located Data Centers

  • 会议论文

  • 发表日期：

    2026年

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  • Qin Hua,
  • Shiyou Qian,
  • Yufeng Deng,
  • Kaixuan Zhang,
  • Dingyu Yang,
  • Jian Cao,
  • Guangtao Xue

摘要

To enhance resource utilization in data centers, it is common to co-locate latency-sensitive services and batch jobs on the same host, which leads to batch-to-online (B2O) and intra-batch (B-B) interference due to resource contention. Therefore, it is crucial to schedule jobs effectively to alleviate both types of interference. While most prior studies focus on B2O interference, B-B interference is often overlooked. By analyzing production data, we observe three insights that reveal the relationships between job behaviors and the two types of interference. We propose DIJS, a dual interference-aware job scheduling framework. DIJS predicts the resource usage patterns and resource sensitivity of new batch jobs. Based on this, it designs two quantizers using reinforcement learning (RL) and attention mechanisms to assess the impact of B2O and B-B interference. Experiments show that DIJS reduces service tail latency by about \(41.0\%\) and enhances job efficiency by up to \(28.6\%\) .