Providing timely results in the face of rapid growth in data volumes has become important for
analytical frameworks. For this reason, frameworks increasingly operate on only a subset of …

Monotask is a unit of work that uses only a single type of resource (eg, CPU, network, disk
I/O). While monotask was primarily introduced as a means to reason about job performance …

Job scheduling in Big Data clusters is crucial both for cluster operators' return on investment
and for overall user experience. In this context, we observe several anomalies in how …

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

We argue for breaking data-parallel jobs in compute clusters into tiny tasks that each
complete in hundreds of milliseconds. Tiny tasks avoid the need for complex skew mitigation …

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

To reduce the impact of network congestion on big data jobs, cluster management
frameworks use various heuristics to schedule compute tasks and/or network flows. Most of …

Scheduling diverse applications in large, shared clusters is particularly challenging. Recent
research on cluster scheduling focuses either on scheduling speed, using sampling to …

The rise in popularity of machine learning, streaming, and latency-sensitive online
applications in shared production clusters has raised new challenges for cluster schedulers …

Large-scale data analytics frameworks are shifting towards shorter task durations and larger
degrees of parallelism to provide low latency. Scheduling highly parallel jobs that complete …