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Computers are deterministic dynamical systems (CHAOS 19:033124, 2009). Amongother things, that implies that one should be able to use deterministicforecast rules to predict their behavior. That statement is sometimes-but notalways-true. The memory and processor loads of some simple programs are easy topredict, for example, but those of more-complex programs like compilers arenot. The goal of this paper is to determine why that is the case. We conjecturethat, in practice, complexity can effectively overwhelm the predictive power ofdeterministic forecast models. To explore that, we build models of a number ofperformance traces from different programs running on different Intel-basedcomputers. We then calculate the permutation entropy-a temporal entropy metricthat uses ordinal analysis-of those traces and correlate those values againstthe prediction success

Determinism, Complexity, and Predictability in Computer Performance