Eno Thereska, Gregory R. Ganger
<span title="2008-06-12">2008</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="" style="color: black;">Performance Evaluation Review</a> </i> &nbsp;
Traditional performance models are too brittle to be relied on for continuous capacity planning and performance debugging in many computer systems. Simply put, a brittle model is often inaccurate and incorrect. We find two types of reasons why a model's prediction might diverge from the reality: (1) the underlying system might be misconfigured or buggy or (2) the model's assumptions might be incorrect. The extra effort of manually finding and fixing the source of these discrepancies,
more &raquo; ... y, in both the system and model, is one reason why many system designers and administrators avoid using mathematical models altogether. Instead, they opt for simple, but often inaccurate, "rules-of-thumb". This paper describes IRONModel, a robust performance modeling architecture. Through studying performance anomalies encountered in an experimental cluster-based storage system, we analyze why and how models and actual system implementations get out-of-sync. Lessons learned from that study are incorporated into IRONModel. IRONModel leverages the redundancy of high-level system specifications described through models and low-level system implementation to localize many types of system-model inconsistencies. IRONModel can guide designers to the potential source of the discrepancy, and, if appropriate, can semi-automatically evolve the models to handle unanticipated inputs.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1145/1384529.1375486</a> <a target="_blank" rel="external noopener" href="">fatcat:txfewnrx7ne7hied2xzvoxvllu</a> </span>
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