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REF: resource elasticity fairness with sharing incentives for multiprocessors

Published: 24 February 2014 Publication History

Abstract

With the democratization of cloud and datacenter computing, users increasingly share large hardware platforms. In this setting, architects encounter two challenges: sharing fairly and sharing multiple resources. Drawing on economic game-theory, we rethink fairness in computer architecture. A fair allocation must provide sharing incentives (SI), envy-freeness (EF), and Pareto efficiency (PE).
We show that Cobb-Douglas utility functions are well suited to modeling user preferences for cache capacity and memory bandwidth. And we present an allocation mechanism that uses Cobb-Douglas preferences to determine each user's fair share of the hardware. This mechanism provably guarantees SI, EF, and PE, as well as strategy-proofness in the large (SPL). And it does so with modest performance penalties, less than 10\% throughput loss, relative to an unfair mechanism.

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cover image ACM Conferences
ASPLOS '14: Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
February 2014
780 pages
ISBN:9781450323055
DOI:10.1145/2541940
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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Publication History

Published: 24 February 2014

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Author Tags

  1. economic mechanisms
  2. fair sharing
  3. game theory
  4. multiprocessor architectures

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ASPLOS '14 Paper Acceptance Rate 49 of 217 submissions, 23%;
Overall Acceptance Rate 535 of 2,713 submissions, 20%

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  • (2022)Fairness-Efficiency Scheduling for Cloud Computing With Soft Fairness GuaranteesIEEE Transactions on Cloud Computing10.1109/TCC.2020.302108410:3(1806-1818)Online publication date: 1-Jul-2022
  • (2022)Online Scheduling of Distributed Machine Learning Jobs for Incentivizing Sharing in Multi-Tenant SystemsIEEE Transactions on Computers10.1109/TC.2022.3174566(1-1)Online publication date: 2022
  • (2020)A Carbon-Aware Incentive Mechanism for Greening Colocation Data CentersIEEE Transactions on Cloud Computing10.1109/TCC.2017.27670438:1(4-16)Online publication date: 1-Jan-2020
  • (2020)Mediating Power Struggles on a Shared Server2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS48437.2020.00030(149-159)Online publication date: Aug-2020
  • (2019)Pliant: Leveraging Approximation to Improve Datacenter Resource Efficiency2019 IEEE International Symposium on High Performance Computer Architecture (HPCA)10.1109/HPCA.2019.00035(159-171)Online publication date: Feb-2019
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