Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from
many thousands of different applications, across a number of clusters each with up to tens of …

User-facing, latency-sensitive services, such as websearch, underutilize their computing
resources during daily periods of low traffic. Reusing those resources for other tasks is rarely …

Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services
in both the research community and industry. When operating a datacenter, optimization of …

Modern resource management frameworks for largescale analytics leave unresolved the
problematic tension between high cluster utilization and job's performance predictability …

Enterprises are moving their business-critical workloads to public clouds at an accelerating
pace. Multi-tenancy is a crucial tenet for cloud data service providers allowing them to …

We present a new cluster scheduler, GRAPHENE, aimed at jobs that have a complex
dependency structure and heterogeneous resource demands. Relaxing either of these …

TetriSched is a scheduler that works in tandem with a calendaring reservation system to
continuously re-evaluate the immediate-term scheduling plan for all pending jobs (including …

Training machine learning (ML) models with large datasets can incur significant resource
contention on shared clusters. This training typically involves many iterations that continually …

Datacenter-scale computing for analytics workloads is increasingly common. High
operational costs force heterogeneous applications to share cluster resources for achieving …

Cloud computing with large-scale datacenters provides great convenience and cost-
efficiency for end users. However, the resource utilization of cloud datacenters is very low …