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 language||American English|
|Title of host publication||2015 IEEE Conference on Computer Communications, IEEE INFOCOM 2015|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||9|
|State||Published - 21 Aug 2015|
|Event||34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015 - Hong Kong, Hong Kong|
Duration: 26 Apr 2015 → 1 May 2015
|Name||Proceedings - IEEE INFOCOM|
|Conference||34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015|
|Period||26/04/15 → 1/05/15|
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© 2015 IEEE.