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Stork for the Cloud soon.. (April 21, 2012)

Cloud hosted Stork data scheduling and optimization services will be available soon. Please keep tuned. For previous releases of Stork, please click here.


April 29, 2011 - Stork 2.0.1 released

Stork 2.0.1 fixes a build issue which would cause the build to fail if the SRM external was not present. As this was just a build issue, those with working Stork installations need not bother updating. However, those who were experiencing build issues previously related to the SRM transfer module may find their issue resolved in this release. Source code and binaries are avaialable for download from the Downloads page.

November 12, 2010 - Stork 2.0 released

The Stork Project team is pleased to announce a new release 2.0 of the Stork Data Placement Scheduler. New features includes Estimation services , Optimization services, Support for SRM transfer module, Support for higher versions of GCC and many more.. Click here for the Release Note for a complete list of changes over version Stork 1.2.1. Stork source code and Binaries are avaialable for download from our Downloads page.


Stork is a batch scheduler specialized in data placement and data movement, which is based on the concept and ideal of making data placement a first class entity in a distributed computing environment. Stork understands the semantics and characteristics of data placement tasks and implements techniques specific to queuing, scheduling, and optimization of these type of tasks.

Stork acts like an I/O control system (IOCS) between the user applications and the underlying protocols and data storage servers. It provides complete modularity and extendibility. The users can add support for their favorite storage system, data transport protocol, or middleware very easily. If the transfer protocol specified in the job description file fails for some reason, Stork can automatically switch to any alternative protocols available between the same source and destination hosts and complete the transfer.

Stork can interact with higher level planners and workflow managers. This allows the users to schedule both CPU resources and storage resources together. Currently, some implementations of Condor DAGMan and Pegasus come with Stork support.

Development of the Stork Data Scheduler is funded by the National Science Foundation (NSF) through its Strategic Technologies for Cyberinfraastructure (STCI) Program.