Capturing resource tradeoffs in fair multi-resource allocation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Cloud computing platforms provide computational resources (CPU, storage, etc.) for running users' applications. Often, the same application can be implemented in various ways, each with different resource requirements. Taking advantage of this flexibility when allocating resources to users can both greatly benefit users and lead to much better global resource utilization. We develop a framework for fair resource allocation that captures such implementation tradeoffs by allowing users to submit multiple 'resource demands'. We present and analyze two mechanisms for fairly allocating resources in such environments: the Lexicographically-Max-Min-Fair (LMMF) mechanism and the Nash-Bargaining (NB) mechanism. We prove that NB has many desirable properties, including Pareto optimality and envy freeness, in a broad variety of environments whereas the seemingly less appealing LMMF fares better, and is even immune to manipulations, in restricted settings of interest.

Original languageAmerican English
Title of host publication2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages9
ISBN (Electronic)9781479983810
StatePublished - 21 Aug 2015
Event34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015 - Hong Kong, Hong Kong
Duration: 26 Apr 20151 May 2015

Publication series

NameProceedings - IEEE INFOCOM


Conference34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015
Country/TerritoryHong Kong
CityHong Kong

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Electrical and Electronic Engineering


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