MapReduce Scheduling in Cloud Environments
Project Information
- Discipline
- Computer Science (401)
- Orientation
- Research
This project aims to develop modules for a MapReduce framework capable of efficiently utilizing the resources in a cloud environment. We will conduct research on the problems associated with existing MapReduce implementations and the components and trade-offs necessary for scheduling and migrating tasks dynamically.
Intellectual MeritThis project will advance the state of the art in scheduling for large-scale data intensive applications by taking into account the heterogeneity in the infrastructure of large grids and clouds.
Broader ImpactsThe software modules resulting from this project will be released as open-source to the HPC community.
Project Contact
- Project Lead
- Renan DelValle (rdelval1)
- Project Manager
- Renan DelValle (rdelval1)
- Project Members
- Renan DelValle, Jessica Hartog, John Weachock
Resource Requirements
- Hardware System
-
- Not sure
Plan to use the FutureGrid resources to increase the scale of our experiments from a few nodes to much larger sizes.
Scale of Use100-300 nodes, for experiments run a few times a week, for the next 6-12 months.
Project Timeline
- Submitted
- 06/25/2013 - 13:24