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

By Raj Jain, D. Chiu, W. Hawe. 1998

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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Citations in the corpus

Fair Resource Allocation in Federated Learning (2019, background): “For this reason several popular fairness measurements have been proposed to balance between fairness and total throughput, including Jain’s index [19], entropy [33], max-min/min-max fairness [31], and proportional fairness [20].”
Optimal Joint Power and Subcarrier Allocation for Full-Duplex Multicarrier Non-Orthogonal Multiple Access Systems (2016, methodology): “To evaluate the fairness of the proposed schemes and the baseline schemes, we adopt Jain’s fairness index [36] as a performance metric to quantify the notion of fairness.”
Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective (2017, methodology): “Here, we compute the Jain’s index based on the proportion of served traffic load for each network using J (lo) = ( O o=1 lo) 2 O· o=1 l(2)o , where lo is the proportion of served traffic load for network o and O is number of networks [46].”
Idle sense: an optimal access method for high throughput and fairness in rate diverse wireless LANs (2005, methodology): “11 DCF with CW equal for all hosts by using the sliding window method that observes the patterns of transmissions to compute the average Jain fairness index in a window of an increasing size(1) [14, 18].”
A Survey on the Fairness of Recommender Systems (2022, methodology): “Jain’s index [41] is commonly used to measure unfairness in network engineering.”
Jellyfish: Networking Data Centers Randomly (2011, background): “Both the topologies have similarly good fairness; Jain’s fairness index [25] over these flow throughput values for both topologies: 0.”
BlocHIE: A BLOCkchain-Based Platform for Healthcare Information Exchange (2018, background): “introduced Jain’s fairness index to evaluate the fairness in allocation of a resource to a set of users/devices [21].”
Energy Collectives: A Community and Fairness Based Approach to Future Electricity Markets (2019, methodology): “QoS indicators are normally used to assess allocation fairness by means of Jain’s index [23], defined as”
Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey (2016, methodology): “An optimization problem was formulated to achieve the maximum fairness which is measured by the Jain’s index [138] for all participants.”
SOAR: Simple Opportunistic Adaptive Routing Protocol for Wireless Mesh Networks (2009, methodology): “We quantify the performance using two metrics: 1) the average end-to-end goodput of all flows, where all flows are routed using either shortest path or SOAR and 2) fairness using the traditional Jain’s fairness index [15].”
Z-MAC: a hybrid MAC for wireless sensor networks (2005, methodology): “We measure the fairness index [12] of delivered packets of all the senders.”
Uplink non-orthogonal multiple access for 5G wireless networks (2014, background): “The Jain’s fairness index is given by [14] Jain’s fairness index = ( P Jain’s fairness index is bounded between 0 and 1 with the maximum achieved by equal users’ rates.”
A Survey on Security Threats and Detection Techniques in Cognitive Radio Networks (2013, background): “The results show that with a given topology, Jain’s fairness index [104], in terms of throughout, can drop by 20% when the percentage of selfish nodes”
SEDA: an architecture for well-conditioned, scalable internet services (2001, background): “Also shown is the Jain fairness index [27] of the number of requests completed by each client.”