Mixture-of-Schedulers: An Adaptive Scheduling Agent as a Learned Router for Expert Policies

• URL: http://arxiv.org/abs/2511.11628v1
• Date: Fri, 07 Nov 2025 14:16:31 GMT
• Title: Mixture-of-Schedulers: An Adaptive Scheduling Agent as a Learned Router for Expert Policies
• Authors: Xinbo Wang, Shian Jia, Ziyang Huang, Jing Cao, Mingli Song,
• Abstract summary: We present the Adaptive Scheduling Agent (ASA), a lightweight framework that intelligently matches workloads to the most suitable scheduling policy at runtime.<n>ASA's core is a novel, low-overhead offline/online approach.<n>Our evaluation, based on a novel benchmark focused on user-experience metrics, demonstrates that ASA consistently outperforms the default Linux scheduler.
• Score: 45.88210980548133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern operating system schedulers employ a single, static policy, which struggles to deliver optimal performance across the diverse and dynamic workloads of contemporary systems. This "one-policy-fits-all" approach leads to significant compromises in fairness, throughput, and latency, particularly with the rise of heterogeneous hardware and varied application architectures. This paper proposes a new paradigm: dynamically selecting the optimal policy from a portfolio of specialized schedulers rather than designing a single, monolithic one. We present the Adaptive Scheduling Agent (ASA), a lightweight framework that intelligently matches workloads to the most suitable "expert" scheduling policy at runtime. ASA's core is a novel, low-overhead offline/online approach. First, an offline process trains a universal, hardware-agnostic machine learning model to recognize abstract workload patterns from system behaviors. Second, at runtime, ASA continually processes the model's predictions using a time-weighted probability voting algorithm to identify the workload, then makes a scheduling decision by consulting a pre-configured, machine-specific mapping table to switch to the optimal scheduler via Linux's sched_ext framework. This decoupled architecture allows ASA to adapt to new hardware platforms rapidly without expensive retraining of the core recognition model. Our evaluation, based on a novel benchmark focused on user-experience metrics, demonstrates that ASA consistently outperforms the default Linux scheduler (EEVDF), achieving superior results in 86.4% of test scenarios. Furthermore, ASA's selections are near-optimal, ranking among the top three schedulers in 78.6% of all scenarios. This validates our approach as a practical path toward more intelligent, adaptive, and responsive operating system schedulers.

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