PDL ABSTRACT

Stardust: Tracking Activity in a Distributed Storage System

Proceedings of the Joint International Conference on Measurement and Modeling of Computer Systems, (SIGMETRICS'06). June 26th-30th 2006, Saint-Malo, France.

Eno Thereska, Brandon Salmon, John Strunk, Matthew Wachs, Michael Abd-El-Malek, Julio Lopez, Gregory R. Ganger

Parallel Data Laboratory
Electrical and Computer Engineering
Carnegie Mellon University
Pittsburgh, PA 15213

http://www.pdl.cmu.edu/

Performance monitoring in most distributed systems provides minimal guidance for tuning, problem diagnosis, and decision making. Stardust is a monitoring infrastructure that replaces traditional performance counters with end-to-end traces of requests and allows for efficient querying of performance metrics. Such traces better inform key administrative performance challenges by enabling, for example, extraction of per-workload, per-resource demand information and per-workload latency graphs. This paper reports on our experience building and using end-to-end tracing as an on-line monitoring tool in a distributed storage system. Using diverse system workloads and scenarios, we show that such fine-grained tracing can be made efficient (less than 6% overhead) and is useful for on- and off-line analysis of system behavior. These experiences make a case for having other systems incorporate such an instrumentation framework.

FULL PAPER: pdf

 

 

 

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