Strategyproof Multi-Resource Allocation in Cloud Computing via Adaptive-Speed Fairness

2026-09-29

AI Summary

This study addresses the challenge in multi-resource allocation for cloud computing, where traditional mechanisms struggle to simultaneously achieve fairness and social efficiency. Focusing on resource allocation under Leontief utilities, this work proposes an adaptive speed fairness mechanism framework. By introducing a fairness ratio benchmark and constructing a unified parameterized model, the proposed approach overcomes classical approximation bounds. Grounded in mechanism design theory and game-theoretic analysis, the mechanism achieves an asymptotic fairness ratio of approximately 1.094, significantly improving upon existing results while closely approaching the theoretical lower bound. Ultimately, this research establishes a new paradigm for efficient and equitable resource allocation in cloud environments.

Abstract

We study fair and strategy-proof allocation of multiple divisible resources with Leontief utilities, motivated by cloud computing. The canonical mechanism, Dominant Resource Fairness (DRF), satisfies sharing incentive (SI), envy-freeness (EF), strategy-proofness (SP), and Pareto optimality (PO), but can be highly inefficient in terms of utilitarian social welfare. Under the classical approximation benchmark, no mechanism satisfying even one of SI, EF, and SP can improve on the trivial worst-case guarantee. We therefore adopt the recently introduced \emph{fair-ratio} benchmark, which compares a mechanism only with the welfare-maximizing allocation that itself satisfies SI and EF. For two resources, we introduce Adaptive-Speed Fairness (ASF), a unified parametric framework that captures previous mechanisms as special or boundary cases. Every ASF mechanism satisfies SI, EF, and PO, and we derive a general sufficient condition that guarantees SP. Optimizing within this framework yields a strategy-proof mechanism with asymptotic fair-ratio $2/(2\sqrt2-1)\approx1.09384$, substantially improving the previous best guarantee $3-\sqrt{3} \approx 1.268$. We complement this upper bound with a lower bound of $1.07894$ for all ASF mechanisms, showing that our best mechanism is close to optimal within this framework. Experiments on synthetic and Google trace-generated instances support the theory and demonstrate strong empirical performance. Finally, we establish a sharp dimensional boundary. For the general setting with $m\ge3$ resources, every mechanism satisfying SI and SP has fair-ratio exactly $m$. The same factor-$m$ lower bound continues to hold for randomized mechanisms satisfying ex-post SI and truthfulness in expectation.

Problem

• Multi-Resource Allocation
• Strategyproofness
• Fairness
• Cloud Computing
• Social Welfare

Innovation

• Adaptive-Speed Fairness
• Strategyproof Mechanism
• Multi-Resource Allocation
• Fair-Ratio Benchmark
• Dominant Resource Fairness