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A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems

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arxiv cs/9809099 v1 pith:QITJOGIW submitted 1998-09-24 cs.NI

classification cs.NI
keywords fairnessindexresourceallocationquantitativecomputerdiscriminationmeasure
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Fairness is an important performance criterion in all resource allocation schemes, including those in distributed computer systems. However, it is often specified only qualitatively. The quantitative measures proposed in the literature are either too specific to a particular application, or suffer from some undesirable characteristics. In this paper, we have introduced a quantitative measure called Indiex of FRairness. The index is applicable to any resource sharing or allocation problem. It is independent of the amount of the resource. The fairness index always lies between 0 and 1. This boundedness aids intuitive understanding of the fairness index. For example, a distribution algorithm with a fairness of 0.10 means that it is unfair to 90% of the users. Also, the discrimination index can be defined as 1 - fairness index.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving

    cs.DC 2026-08 conditional novelty 6.0 of 10

    Cascade coordinates request scheduling and multi-tier KV-cache movement through a single per-request latency budget, improving SLO-satisfied goodput by up to 2.4x in simulation.

  2. AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning

    cs.LG 2025-11 unverdicted novelty 6.0 of 10

    AdaFair-MARL enforces workload fairness as an explicit second-order cone constraint in cooperative MARL via adaptive primal-dual optimization, achieving near-perfect constraint satisfaction while preserving team performance.

  3. LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach

    cs.AI 2025-10 conditional novelty 5.0 of 10

    An LLM-based multi-agent reinforcement learning framework that models uplink MAC scheduling as a Stackelberg game reports 77.6% higher throughput and 65.2% better fairness in simulation.

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