Scheduling the Spark Framework under the Mesos Resource Manager

Using clusters of servers and datacenters to process large numbers of data- and computation-intensive jobs is becoming mainstream. The need for large clusters is driven by the fact that many workloads are growing at a faster rate than the advances in single computer performance. To manage processing in a cluster of multiple computers, several...

Design and Evaluation of a Portfolio Scheduler for Business-Critical Workloads Hosted in Cloud Datacenters

Virtualized multi-cluster, multi-datacenter datacenters are central to the digital economy, but require new techniques to address increasing scales, changing architectures, and dynamic workloads. Especially the introduction of new Business-Critical workloads that we characterize, create new resource management challenges. We propose a resource...