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dharmeshkakadia edited this page Oct 10, 2012
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We try to provide a solution of deploying Hadoop jobs across multiple cloud service providers.
- Auto Scaling - Based on the deadline, infrastructure and cost requirements, the scheduler will smartly scale the resources allocated to a job. The user is also provided an option to manually scale up/down.
- Ability to run across multiple cloud providers - If you have multiple jobs and access to multiple cloud providers, HadoopStack provides you the ability to run different jobs on different clouds.
- Job scheduling for minimizing cost and completion time - HadoopStack uses machine learning to smartly allocate jobs across multiple cloud providers aiming to reduce your cost and task-completion time.
- Performance optimization with storage integration - With storage administration privileges, HadoopStack auto-replicates the resources that being utilized heavily, thus improving performance.
- Client Tools - A web interface and a simple command line interface for interacting with HadoopStack.
- Running multiple jobs on a private cloud
- Running multiple jobs on a public cloud
- Running multiple jobs across private and public clouds
- Improving instance monitoring for better scheduling.
- Integration of other frameworks - R, mahout, hive/pig etc.