Ironmodel

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="https://fatcat.wiki/container/pkal2zplnrarzkkzugv4u5ithy" 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.
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