2-Dominant Resource Fairness: Fairness-Efficiency Tradeoffs in Multi-resource Allocation

Abstract

Fair allocation has been studied intensively in both economics and computer science. Many existing mechanisms that consider fairness of resource allocation focus on a single resource. With the advance of cloud computing that centralizes multiple types of resources under one shared platform, multi-resource allocation has come into the spotlight. In fact, fair/efficient multi-resource allocation has become a fundamental problem in any shared computer system. The widely-used solution is to partition resources into bundles that contain fixed amounts of different resources, so that multiple resources are abstracted as a single resource. However, this abstraction cannot satisfy different demands from heterogeneous users, especially on ensuring fairness among users competing for resources with different capacity limits. A promising approach to this problem is dominant resource fairness (DRF), which tries to equalize each user's dominant share (share of a user's most highly demanded resource, i.e., the largest fraction of any resource that the user has required for a task), but this method may still suffer from significant loss of efficiency (i.e., some resources are underused). This paper develops a new allocation mechanism based on DRF aiming to balance fairness and efficiency. We consider fairness not only in terms of a user's dominant resource, but also in another resource dimension which is secondarily desired by this user. We call this allocation mechanism 2-dominant resource fairness (2-DF). Then, we design a non-trivial on-line algorithm to find a 2-DF allocation and extend this concept to $k$ -dominant resource fairness ( $k$ -DF).