Dominant Resource Fairness (DRF) in Cluster Resource Management

Dominant Resource Fairness (DRF)

Multi-Resource Allocation

• DRF is a fairness policy for allocating multiple resources (e.g., CPU, memory, I/O) in a shared cluster environment
• It aims to provide a fair share of each resource to each user or group, based on their dominant resource (i.e., the resource that is most limiting for their workload)
• DRF is designed to handle heterogeneous workloads with varying resource demands

Key Concepts

• Dominant resource: the resource that is most limiting for a workload, e.g., CPU-bound or memory-bound
• Resource share: the fraction of each resource allocated to a user or group
• Fairness: each user or group receives a fair share of each resource, based on their dominant resource

Job Scheduling

• DRF is used in job scheduling systems to allocate resources to jobs (e.g., tasks, applications)
• The scheduler assigns a resource share to each job based on its dominant resource and available cluster resources
• DRF ensures that jobs are executed efficiently, without wasting resources or causing starvation

Cluster Resource Management

• DRF is used in cluster resource management systems to manage resource allocation across multiple nodes
• It enables efficient use of cluster resources, by allocating resources to jobs based on their dominant resource
• DRF helps to:
  • Prevent resource waste and underutilization
  • Improve job throughput and response time
  • Enhance cluster utilization and scalability

Benefits

• Fairness: ensures that each user or group receives a fair share of resources, based on their dominant resource
• Efficiency: optimizes resource allocation, reducing waste and improving cluster utilization
• Scalability: enables large-scale clusters to efficiently manage multiple resources and jobs