PDL PROJECTS

// TRACE

Contact: Greg Ganger

I/O traces play a critical role in storage systems evaluation. They are captured through a variety of mechanisms, analyzed to understand the characteristics and demands of different applications, and replayed against real and simulated storage systems to recreate representative workloads. Often, traces are much easier to work with than actual applications, particularly when the applications are complex to configure and run, or involve confidential data or algorithms.

//TRACE is a new approach for extracting and replaying traces of parallel applications. Its tracing engine (the causality engine) automatically discovers inter-node data dependencies and inter-request compute times for each node (process) in an application. It does so by selectively delaying I/O in order to expose data dependencies among the compute nodes. The learned dependency information is saved in per-node annotated I/O traces. Such annotation allows a parallel replayer to closely mimic the behavior of a traced application.


People

FACULTY

Greg Ganger
David O'Hallaron

STAFF

Gregg Economou
Michael Stroucken

STUDENTS

James Hendricks
Julio López
Mike Mesnier
Raja Sambasivan
Matthew Wachs

Publications

Acknowledgements

We thank the members and companies of the PDL Consortium: Actifio, American Power Conversion, EMC Corporation, Facebook, Google, Hewlett-Packard Labs, Hitachi, Huawei Technologies Co., Intel Corporation, Microsoft Research, NEC Laboratories, NetApp, Inc., Oracle Corporation, Panasas, Samsung Information Systems America, Seagate Technology, Symantec Corporation, VMware, Inc., and Western Digital for their interest, insights, feedback, and support.

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© 2014. Last updated 15 March, 2012