5.Single resource fairness:对于单个资源的分配应对简化为最大最小公平。 6.Bottleneck fairness:如果一个资源是按百分比被每个租户所需要的,则该资源分配简化为最大最小公平。 7.Population monotonicity:当用户离开系统并放弃其资源时,剩余用户的分配都不会减少。 8.Resource monotonicity:如果向系统添加了更多的资源,...
DRF（Dominant Resource Fairness）算法在多资源类型分配中展现出了其独特优势。以一个系统拥有9个CPU和18GB RAM为例，假设用户A每个任务消耗1个CPU和4GB内存，用户B每个任务消耗3个CPU和1GB内存。DRF通过计算每个用户对资源的主要需求份额，最终分配给用户A三个任务和用户B两个任务。具体分配为用户A获得2...
"Dominant resource fairness: fair allocation of multiple resource types". In: Proceedings of the 8th USENIX conference on Networked systems design and implementation. NSDI'11. Boston, MA: USENIX Association, 2011, pp. 24-24. url: http://dl.acm.org/citation.cfm?id=1972457.1972490....
Dominant Resource Fairness: Fair Allocation of Multiple Resource Types
We consider the problem of fair resource allocation in a system containing different resource types, where each user may have different demands for each resource. To address this problem, we propose Dominant Resource Fairness (DRF), a generalization of max-min fairness to multiple resource types. ...
.DominantResourceFairness:FairAllocationofMultipleResourceTypesAliGhodsi,MateiZaharia,BenjaminHindman,AndyKonwinski,ScottShenker,IonStoicaUniversityofCalifornia,Berkeley{alig,matei,benh,andyk,shenker,istoica}@cs.berkeley.eduAbstractWeconsidertheproblemoffairresourceallocationinasystemcontainingdifferentresourcetypes,...
We design a multi-resource allocation mechanism, called DRFH, that generalizes the notion of Dominant Resource Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH provides a number of highly desirable properties. With DRFH, no user prefers the allocation of another user; ...
Official implementation of Fair Resource Allocation in Multi-Task Learning. Supervised Learning The performance is evaluated under 3 scenarios: Image-level Classification. The CelebA dataset contains 40 tasks. Regression. The QM9 dataset contains 11 tasks, which can be downloaded automatically from Pytorc...
Optimization theory is used to analyze the multi-constraint resource allocation problem and some heuristic characteristics about the optimal solution are obtained. To deal with the cohesiveness of the necessary conditions, we resort to bargaining theory that has been deeply investigated in game theory. ...
Multiple-input-multiple-output (MIMO) transmit precoding and resource allocation are linked to the underlying proportional-fair scheduling to ensure a good trade-off between cell-average and cell-edge user spectral-efficiency. Due to the coupled interference among mobile stations, the resulting ...