research-article

Weighted proportional allocation

Authors:

Milan VojnovicAuthors Info & Claims

SIGMETRICS '11: Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems

Pages 173 - 184

https://doi.org/10.1145/1993744.1993760

Published: 07 June 2011 Publication History

Abstract

We consider a weighted proportional allocation of resources that allows providers to discriminate usage of resources by users. This framework is a generalization of well-known proportional allocation by accommodating allocation of resources proportional to weighted bids or proportional to submitted bids but with weighted payments.

We study a competition game where everyone is selfish: providers choose user discrimination weights aiming at maximizing their individual revenues while users choose their bids aiming at maximizing their individual payoffs. We analyze revenue and social welfare of this game. We find that the revenue is lower bounded by k/(k+1) times the revenue under standard price discrimination scheme, where a set of k users is excluded. For users with linear utility functions, we find that the social welfare is at least 1/(1+2/√3) of the maximum social welfare (approx. 46%) and that this bound is tight. We extend this efficiency result to a broad class of utility functions and multiple competing providers. We also describe an algorithm for adjusting discrimination weights by providers without a prior knowledge of user utility functions and establish convergence to equilibrium points of the competition game.

Our results show that, in many cases, weighted proportional sharing achieves competitive revenue and social welfare, despite the fact that everyone is selfish.

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cover image ACM Conferences

SIGMETRICS '11: Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems

June 2011

376 pages

ISBN:9781450308144

DOI:10.1145/1993744

General Chair:
Arif Merchant
Google, USA
,
Program Chairs:
Kimberly Keeton
HP Labs, USA
,
Dan Rubenstein
Columbia University, USA

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 June 2011

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SIGMETRICS '11: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

June 7 - 11, 2011

California, San Jose, USA

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Mahenge MKitindi E(2023)Adaptive Cache Server Selection and Resource Allocation Strategy in Mobile Edge ComputingInternational Journal of Information Communication Technologies and Human Development10.4018/IJICTHD.29941214:1(1-16)Online publication date: 31-Mar-2023
https://dl.acm.org/doi/10.4018/IJICTHD.299412
Wang LWu ZLu SQiu HGu W(2023)Discrimination-Based Double Auction for Maximizing Social Welfare in the Electricity and Heating Market With Incomplete InformationIEEE Systems Journal10.1109/JSYST.2023.3310270(1-12)Online publication date: 2023
https://doi.org/10.1109/JSYST.2023.3310270
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