Introduction
In the rapidly evolving landscape of AI/ML workloads, efficient resource management has become critical for optimizing performance and reducing costs. Traditional Kubernetes schedulers often fall short due to their lack of topology awareness, leading to resource fragmentation and suboptimal task scheduling. This article explores the Q Scheduler's topology-optimized approach, addressing these challenges through advanced scheduling strategies and fair resource allocation mechanisms.
Core Concepts and Features
What is Q Scheduler?
The Q Scheduler is an advanced Kubernetes scheduler designed to enhance AI/ML workload performance by incorporating topology-aware scheduling. It introduces features like fair sharing and hierarchical cohorts to ensure equitable resource distribution across different workload types, including GPU and TPU clusters.
Key Functionalities
- Topology-Aware Scheduling: Utilizes node labels (e.g.,
block-name/rack-name/node-name) to model physical infrastructure, enabling precise resource placement.
- Resource Provisioning Types: Supports reservation, spot, and on-demand resource allocation to cater to diverse workload requirements.
- Queue Management: Implements fair sharing and hierarchical cohorts to manage task execution priorities and prevent resource overloading.
- Rank-Based Scheduling: Optimizes communication patterns (e.g., ring communication) by distributing AI training pods based on rank values, minimizing cross-node latency.
Implementation Details
Topology Modeling
The scheduler constructs a topology tree with four hierarchical layers: Zone, Block, Rack, and Node. This structure allows for flexible customization, supporting cloud provider-specific labels while abstracting them into a unified topology API.
Resource Capacity Calculation
- Nominal Capacity: Derived from
status.allocatable of nodes.
- Adjusted Capacity: Accounts for committed tasks and non-Q workloads (e.g., DaemonSets) to ensure accurate resource availability.
Scheduling Policies
- Required Topology: Enforces strict placement within specified topology domains.
- Preferred Topology: Prioritizes same-domain placement with overflow to adjacent domains.
- Unconstrained Topology: Freely allocates resources for tasks without inter-node communication needs.
Scheduling Workflow
- Topology Snapshot: Generates a cluster topology tree and node capacity snapshot during each scheduling cycle.
- Bidirectional BFS Traversal: Bottom-up identifies suitable topology domains; top-down maps domains to specific nodes.
- Pod Creation and Scheduling Gates: Sets scheduling gates during Pod creation, deferring node selector assignment until later stages.
Performance Optimization and Results
AI Training Efficiency
Testing with the GPT2 model on a 32-node GPU cluster demonstrated a 15% reduction in training time. Tight resource placement and rank-based scheduling significantly lowered cross-node communication latency.
Supported Workloads
- Training: Kubernetes Jobs, JobSets, Ray Jobs, CubeRay Jobs.
- Inference: DeploymentSets, WorkerSets.
- General: App Wrappers, Plain Pods.
Challenges and Future Directions
Current Limitations
- Manual Label Configuration: Requires manual setup of topology labels, increasing operational complexity.
- Static Topology: Lacks dynamic discovery mechanisms for real-time topology updates.
Planned Improvements
- Algorithm Flexibility: Introduce configurable scheduling algorithms to balance utilization and fragmentation.
- Cost Optimization: Enhance resource utilization by addressing independent Pod scheduling within the same workload.
- Integration with Cube Scheduler: Deepen integration to improve scheduling accuracy.
- Automated Topology Discovery: Explore integration with tools like Cisco CDP for dynamic topology labeling.
Q 0.11 Version Enhancements
- Auto Mode: Automates topology label assignment, reducing manual intervention.
- Full Queue Management: Fully supports fair sharing and hierarchical cohorts for queue-based task management.
Conclusion
The Q Scheduler represents a significant advancement in topology-aware scheduling for AI/ML workloads. By addressing resource fragmentation, optimizing communication patterns, and supporting diverse workload types, it enhances both performance and user experience. For organizations leveraging Kubernetes, adopting Q Scheduler can lead to more efficient resource utilization and faster training/inference cycles. As the technology evolves, integrating automated topology discovery and advanced scheduling algorithms will further solidify its role in modern cloud-native environments.