Optimizing Batch Workloads on Kubernetes: A Custom Scheduler Approach

Introduction

Kubernetes has evolved from a platform designed for stateless applications to a central infrastructure for managing batch workloads such as ETL pipelines, machine learning training, and high-performance computing (HPC). However, its default scheduler lacks the specialized capabilities required to efficiently handle these workloads, particularly in multi-tenant environments. This article explores how custom schedulers like Volcano and Unicorn address these challenges, enabling optimized resource utilization, reduced carbon footprint, and improved batch job execution.

Key Challenges with Default Kubernetes Scheduling

The default Kubernetes scheduler is not inherently suited for batch workloads due to several limitations:

1. Lack of Application Awareness: It treats all workloads uniformly, failing to differentiate between frameworks like Apache Spark, Ray, or Trino, leading to suboptimal resource allocation.
2. No Gang Scheduling Support: Batch tasks often require all Pods to start simultaneously (e.g., Spark jobs), but the default scheduler cannot guarantee this, risking delays and resource contention.
3. Limited Multi-Tenancy Management: It struggles to enforce resource isolation and priority between teams or applications, causing resource contention and inefficiencies.
4. Static Resource Allocation: The scheduler lacks dynamic adjustment mechanisms, making it difficult to prioritize high-criticality tasks or optimize resource usage for cost and sustainability.

Custom Schedulers: Volcano and Unicorn

Volcano: A Foundation for Batch Workloads

Volcano is a custom Kubernetes scheduler designed specifically for batch workloads, offering the following core features:

• Gang Scheduling: Ensures all Pods of a batch job start simultaneously, critical for frameworks like Spark that rely on coordinated execution.
• Multi-Tenant Resource Isolation: Implements resource quotas and priority-based scheduling to isolate workloads across teams or applications.
• Dynamic Resource Allocation: Supports GPU and other specialized hardware, enabling flexible resource sharing in multi-tenant environments.
• Hierarchical Queue Management: Mimics YARN’s queue system, allowing structured resource allocation based on priorities and organizational needs.

Use Cases: Volcano is ideal for large-scale multi-tenant clusters, HPC workloads, and scenarios requiring high resource utilization and carbon footprint optimization.

Unicorn: Enhancing Volcano’s Capabilities

Unicorn builds on Volcano, introducing additional features to simplify resource management:

• Visual UI for Scheduling Policies: Provides a user interface to configure and monitor scheduling strategies, reducing complexity for administrators.
• Dynamic Resource Demand Handling: Supports workloads with variable resource requirements, such as interactive sessions or real-time data processing.
• Simplified Resource Abstraction: Abstracts Kubernetes-native scheduling logic, making it easier to manage complex resource allocation rules.

Advantages: Unicorn reduces the overhead of custom scheduling while offering finer-grained control over resource allocation and prioritization.

Technical Focus: Efficiency and Scalability

Resource Efficiency and Carbon Footprint

Custom schedulers optimize resource utilization by minimizing idle time and ensuring workloads run on the most appropriate hardware. This reduces energy consumption and aligns with sustainability goals.

Dynamic Resource Allocation

Support for GPU and other specialized hardware enables efficient sharing and allocation, critical for workloads requiring high computational power.

Priority and Queue Management

Hierarchical queues and priority-based scheduling ensure high-criticality tasks (e.g., real-time analytics) can preempt lower-priority jobs, maintaining system responsiveness.

Framework Compatibility

Both Volcano and Unicorn are designed to handle frameworks like Apache Spark, Ray, and Trino, addressing the limitations of the default scheduler in managing their specific execution requirements.

Practical Applications and Best Practices

Use Cases

• High-Resource Utilization Jobs: ETL pipelines, machine learning training, and HPC simulations benefit from efficient scheduling and resource isolation.
• Multi-Tenant Environments: Teams sharing a cluster can define resource quotas and priorities to avoid contention.
• Interactive Workloads: Unicorn’s UI and dynamic resource handling support interactive sessions (e.g., Jupyter Notebook) connected to batch clusters.

Best Practices

• Define Clear Resource Requirements: Specify CPU, memory, and GPU needs for each workload to avoid over-provisioning or underutilization.
• Implement Hierarchical Queues: Structure queues based on team, project, or priority to ensure fair resource distribution.
• Monitor and Optimize: Regularly evaluate scheduling performance, resource usage, and carbon footprint metrics to refine strategies.

Conclusion

Custom schedulers like Volcano and Unicorn address the limitations of Kubernetes’ default scheduler, enabling efficient management of batch workloads in complex, multi-tenant environments. By prioritizing resource optimization, dynamic allocation, and framework compatibility, these tools enhance the performance and sustainability of Kubernetes-based data processing pipelines. Success depends on aligning scheduling strategies with specific use cases and continuously refining resource management practices to maximize efficiency and reduce environmental impact.