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.Populationmonotonicity: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 monotonicit
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...
Multi-resource allocationFairnessEfficiencyGeneralized asset fairnessFair and efficient allocation of multiple types of resources is a fundamental goal in cloud computing systems. Due to the heterogeneous resource requirements of a user's jobs on CPU, memory, etc., fairness of allocation is difficult ...
In this paper we consider the problem of supporting resource allocation decisions affecting multiple beneficiaries. Such problems inherently involve efficiency-fairness trade-offs. We introduce a new approach based on the paradigm of maximizing efficiency subject to constraints to ensure that the decision ...
We developed a fair-diverse scarce resource allocation optimization framework. • We considered a trade-off between Geographical Diversity and Social group Fairness. • We proposed a tuning approach to find an optimum range for the trade-off parameter. • We evaluated the performance of our pr...
arXiv上刚刚挂的一篇文章Fair Resource Allocation in Federated Learning,作者是CMU的AP Virginia Smith组的,搜了一下主页,居然是一个超级年轻的小姐姐~ 这篇文章思路很straight-forward,逻辑也很清楚,唯一有点缺点的是可能时间比较赶,第一版上传的还是draft,文章还没修改完 Motivation 之前横向联邦学习一般... 查看...
最后作者还指出q-FFL可以迁移到联邦学习外的场景——元学习。元学习场景下,我们要学习一个能够后续适应不同新任务的初始化模型,这一个个新任务就相当于device。 参考文献 Li, Tian, et al. "Fair resource allocation in federated learning."arXiv preprint arXiv:1905.10497(2019). ...
本文提出q-FFL优化算法,在模型准确率不变的情况下,引入q参数化的权重,使损失大的参与者权重更高,降低准确率分布方差,使模型性能更均匀分布,实现联邦学习公平性。 联邦学习的两种公平性:一种是均衡公平性,强调“人人平等有机会“,关心“表现差“的客户;另一种是贡献公平性,强调“按劳分配,多劳多得,优胜劣汰“。
Fair and efficient resource allocation optimization for internet of vehicles (IoV) in edge computing environments S. W. AlMhameedL. KarimiS. C. Choudhury
Fair and efficient resource allocation optimization for internet of vehicles (IoV) in edge computing environments S. W. AlMhameedL. KarimiS. C. Choudhury