Dependency-Aware Dominant Resource Fairness for Multi-Tenant Multi-Resource Systems

2026-06-24

AI Summary

This work addresses the inefficiencies of traditional Dominant Resource Fairness (DRF) in multi-tenant, multi-resource systems, where fixed resource proportion assumptions lead to significant waste and suboptimal performance under resource overcommitment and inter-resource dependencies. To overcome these limitations, the authors propose Dependency-aware Dominant Resource Fairness (DDRF), a novel allocation mechanism that explicitly models real-world resource dependencies and dynamically equalizes dominant shares among active tenants on congested resources, thereby relaxing DRF’s rigid proportionality constraints. DDRF preserves Pareto efficiency while substantially mitigating resource fragmentation caused by low-demand tenants. Implemented within a centralized orchestration framework, DDRF demonstrates substantial improvements over baseline approaches in both EC2 and vRAN scenarios, achieving up to 80% higher user satisfaction, 60% less resource waste, and a greater than 15% increase in the Jain fairness index.

Abstract

Multi-resource allocation in network-congested, multi-tenant systems in which demand exceeds available capacity is challenging, as there is no straightforward way to determine how much of each resource to assign, especially when resources are interdependent. Classical approaches such as Dominant Resource Fairness (DRF), which generalizes Max-Min Fairness (MMF) to multiple resources, assume linear proportional dependencies across resources, requiring allocations to follow fixed proportions implied by tenants demands. However, this assumption may lead to inefficient allocations and resource waste, with allocated resources that go unused in practice. In this paper, we consider a multi-resource orchestrator and propose the Dependency-aware Dominant Resource Fairness (DDRF) policy, a centralized generalization of DRF that considers inter-resource dependencies: it equalizes active dominant shares of congested resources, preserving DRFs desirable properties, while avoiding its inefficiency with low-demand tenants. We prove that DDRF always saturates at least one congested resource, ensuring Pareto efficiency and eliminating resource waste. We evaluate DDRF using Amazon EC2 traces and a virtualized radio access network (vRAN) use case while considering real resource dependencies. The results show that DDRF improves effective user satisfaction by up to 80% and reduces resource waste by up to 60% compared to dependency-agnostic baselines, while improving Jain's fairness index by more than 15% compared to the utilitarian policy.

Problem

• multi-resource allocation
• resource dependency
• multi-tenant systems
• fairness
• resource waste

Innovation

• Dependency-aware Dominant Resource Fairness
• multi-resource allocation
• resource dependency
• Pareto efficiency
• multi-tenant systems