Capturing resource tradeoffs in fair multi-resource allocation

Abstract

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.