Efficient Far Memory-Aware Scheduling With FaMAS

Chiheng Lou, Xin Jin

Abstract: Benefiting from the low-latency RDMA network, far memory techniques that enable applications to leverage memory on remote machines have been proposed to improve job performance and resource utilization in datacenters. In a far memory-equipped datacenter, applications exhibit diverse characteristics when using far memory. However, existing datacenter schedulers are oblivious to the presence of far memory, resulting in suboptimal scheduling decisions that can compromise the performance guarantee of tasks. To this end, we present FaMAS, a far memory-aware datacenter scheduler that exploits the benefits of far memory to improve datacenter efficiency and avoid severe performance degradation of tasks. FaMAS tackles the far-memory scheduling problem for two objectives: reducing average job completion time and meeting deadlines. FaMAS introduces two novel far memory-aware algorithms: Job Completion Time First (JCTF) and Service Level Objective First (SLOF), tailored to the above two objectives respectively. JCTF formalizes the scheduling target with insights from the trade-off between the performance and the resource usage of individual tasks, while SLOF incorporates the growth rate of resource usage to represent tasks. The experimental results show that JCTF improves the average job completion time by up to $1.81\times $ and SLOF achieves up to $3.95\times $ lower deadline violations over the best traditional algorithm.

External IDs:dblp:journals/ton/LouJ25