Dynamic Heterogeneous CMPs (DHCMP), which provide the capability to configure different number and types of processing cores at system runtime, dramatically improve energy- and power-efficiency by scheduling workloads on the most appropriate core type. A significant body of recent work has focused on improving system throughput through scheduling on asymmetric CMPs (ACMP). However, none of the prior work has looked into fairness. In this work, centralized run queue is introduced and a heterogeneity-aware fair scheduler (HFS) is proposed to address the fair scheduling problem on DHCMP. HFS algorithm can not only gain the capability of DHCMP to configure the types of processing cores to match the granularities of parallelism in the tasks, but also keep the fairness when tasks running simultaneously. Experimental results demonstrate that HFS on DHCMP outperforms the best performing fair scheduler on SCMP and ACMP by 10.55% in user-oriented performance (ANTT), and 14.24% in system throughput (WSU).