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

2025-11-07

AI Summary

Modern OS schedulers rely on static, monolithic policies, struggling to simultaneously ensure fairness, throughput, and latency under heterogeneous hardware and dynamic, diverse workloads. This paper proposes the Adaptive Scheduling Agent (ASA) paradigm: it decouples offline modeling from online decision-making, employs lightweight ML models to identify abstract workload patterns, and dynamically selects the optimal specialized scheduler via time-weighted probabilistic voting and precomputed mapping tables. Implemented atop Linux’s sched_ext framework, ASA enables real-time, zero-training cross-platform policy switching. It introduces the first hybrid scheduling architecture that composes multiple expert schedulers on demand. Experiments show ASA outperforms EEVDF in 86.4% of user-perceived benchmark scenarios and selects a top-3 optimal scheduler in 78.6%—significantly enhancing overall scheduling efficacy.

Technology Category

• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Static OS schedulers struggle with diverse dynamic workloads
• Single policy approach compromises fairness throughput latency
• Heterogeneous hardware requires adaptive scheduling solutions

Innovation

• Dynamically selects optimal policy from specialized scheduler portfolio
• Uses machine learning model to recognize abstract workload patterns
• Employs runtime probability voting and mapping for scheduler switching