Sriram Rao

Network-Aware Scheduling for Data-Parallel Jobs: Plan When You Can

Virajith Jalaparti, Peter Bodik, Ishai Menache, Sriram Rao, Konstantin Makarychev, Matthew Caesar
Appears in: 
CCR August 2015

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 these schedulers consider the job input data fixed and greedily schedule the tasks and flows that are ready to run. However, a large fraction of production jobs are recurring with predictable characteristics, which allows us to plan ahead for them.

Multi-resource packing for cluster schedulers

Robert Grandl, Ganesh Ananthanarayanan, Srikanth Kandula, Sriram Rao, Aditya Akella
Appears in: 
CCR August 2014

Tasks in modern data-parallel clusters have highly diverse resource requirements along CPU, memory, disk and network. We present Tetris, a multi-resource cluster scheduler that packs tasks to machines based on their requirements of all resource types. Doing so avoids resource fragmentation as well as over-allocation of the resources that are not explicitly allocated, both of which are drawbacks of current schedulers.

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