Quantised and Simulated Max–min Fairness in Blockchain Ecosystems

Abstract

Blockchain systems heavily rely on user incentive and participation for the success and scope of their operation. For this end, fairness is a key precondition to attract users to join the ecosystem. In order to assure users of the fairness of the ecosystem, the distribution of shared resources must be done according to a fair and transparent policy, and it must be handled by a robust mechanism that implements this policy. The present study addresses this problem and contributes four decentralised and autonomous algorithms that may serve for the fair distribution of shared intrinsic resources. These are the adaptations of Max–min Fairness (MF), a distribution scheme which is well established in the computer science literature as fair, and its weighted version (WMF). Blockchain faucets are shared resource allocation mechanisms which accomplish this task by providing users with fixed amounts of free cryptocurrency. Faucets are employed in academic networks such as Bloxberg, and also in test networks such as Ropsten and Rinkeby We implemented both MF and WMF as blockchain faucets for the demonstration of their operation and measurement of their performance. The first algorithm, we call quantised Max–min Fairness (QMF), operates under the restriction on demand volumes but is scalable to large numbers of users, and the second, we call Simulated Max–min Fairness (SMF), operates under the restriction on the number of users but allows for various weighting policies. The other two are the weighted versions of these algorithms.