A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications
Jiaqi Lei, Akhil Singla, Sanjay Mehrotra

TL;DR
This paper proposes a distributionally robust stochastic fairness framework for multi-resource allocation in cloud computing, using rough estimates of resource requirements to ensure fairness properties and improve performance under limited information.
Contribution
It introduces a novel SA-DR model for stochastic fairness that converges to the DR model and performs well with partial information in cloud resource allocation.
Findings
The SA-DR model achieves fairness properties like stochastic envy-freeness.
It converges to the distributionally robust model with increasing samples.
The partial-information model performs closer to full-information than worst-case models.
Abstract
Allocation of limited resources under uncertain requirements often necessitates fairness considerations, with applications in computer systems, health systems, and humanitarian logistics. This paper introduces a distributionally robust (DR) stochastic fairness framework for multi-resource allocation, leveraging rough estimates of the mean and variance of resource requirement distributions. The framework employs a sampled approximation DR (SA-DR) model to develop the concept of stochastic fairness, satisfying key properties such as stochastic Pareto efficiency, stochastic sharing incentive, and stochastic envy-freeness under suitable conditions. We show the convergence of the SA-DR model to the DR model and propose a finitely convergent algorithm to solve the SA-DR model. We empirically evaluate the performance of our moment-based SA-DR model -- which uses only rough estimates of the…
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Taxonomy
TopicsBlockchain Technology Applications and Security
