Pamela Isabel Delgado Borda

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Publications associées (5)

Towards reproducible software studies with MAO and Renku

Pamela Isabel Delgado Borda, Christine Choirat

In software engineering, the developers' joy of decomposing and recomposing microservice-based applications has led to an enormous wave of microservice artefact technologies. To understand them better, researchers perform hundreds of experiments and empiri ...

ELSEVIER2022

Hybrid, Job-Aware, and Preemptive Datacenter Scheduling

Pamela Isabel Delgado Borda

Scheduling in datacenters is an important, yet challenging problem. Datacenters are composed of a large number, typically tens of thousands, of commodity computers running a variety of data-parallel jobs. The role of the scheduler is to assign cluster reso ...

EPFL2018

Kairos: Preemptive Data Center Scheduling Without Runtime Estimates

Willy Zwaenepoel, Florin Dinu, Pamela Isabel Delgado Borda, Diego Didona

The vast majority of data center schedulers use task runtime estimates to improve the quality of their scheduling decisions. Knowledge about runtimes allows the schedulers, among other things, to achieve better load balance and to avoid head-of-line blocki ...

2018

Job-aware Scheduling in Eagle: Divide and Stick to Your Probes

Willy Zwaenepoel, Florin Dinu, Pamela Isabel Delgado Borda, Diego Didona

We present Eagle, a new hybrid data center scheduler for data-parallel programs. Eagle dynamically divides the nodes of the data center in partitions for the execution of long and short jobs, thereby avoiding head-of-line blocking. Furthermore, it provides ...

ACM2016

Hawk: Hybrid Datacenter Scheduling

Willy Zwaenepoel, Florin Dinu, Anne-Marie Kermarrec, Pamela Isabel Delgado Borda

This paper addresses the problem of efficient scheduling of large clusters under high load and heterogeneous workloads. A heterogeneous workload typically consists of many short jobs and a small number of large jobs that consume the bulk of the cluster's r ...

USENIX Association2015

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