Ziel dieses Projektes ist die Entwicklung von Methoden zur Algorithmusauswahl und -konfiguration unter Ressourcenbeschränkungen. Von besonderem Interesse sind Szenarien, in denen eine einzelne Auswertung eines Algorithmus teuer ist. Wir gehen auf den Fall ein, dass viele konkurrierende Kandidatenalgorithmen verfügbar sind und jeder Algorithmus spezifische Hyperparameter hat, die angepasst werden müssen, um das bestmögliche Ergebnis zu erzielen. Eine vollständige Suche in diesem Bereich ist nicht möglich, da die Anzahl der Konfigurationen, die während der Optimierung ausgewertet werden können, aufgrund ihrer langen Laufzeiten stark begrenzt ist. In einer typischen Situation ist der Algorithmus ein maschinelles Lernverfahren, das auf einen Regressions- oder Klassifizierungsdatensatz angewendet wird. Das Optimierungsziel ist es, aus einer Reihe von Kandidaten eine maschinelle Lernmethode zu finden und deren Hyperparameter so zu konfigurieren, dass die bestmögliche Vorhersagequalität erreicht wird.
Die modellbasierte Optimierung (MBO) stellt sich dieser Herausforderung. Sie verwendet ein Regressionsmodell als Hilfsmittel, um die Zielfunktion zu approximieren. So wird beispielsweise die Vorhersagequalität eines maschinellen Lernalgorithmus für eine bestimmte Aufgabe durch eine Gaußsche Prozessregression vorhergesagt. Die aus dem Hilfsmodell gewonnenen Vorhersagen helfen, schnell in Regionen im Suchraum mit vielversprechender Vorhersagequalität zu gelangen. Dies reduziert die Anzahl der teuren Auswertungen während der Optimierung. Aufgrund der enormen Anzahl möglicher Konfigurationen kann die gesamte MBO wall-clock runtime immer noch unangemessen lang sein. Der Einsatz paralleler Computersysteme und eine effiziente Ressourcennutzung werden unerlässlich.
Um diesen Herausforderungen zu begegnen, haben wir das Framework Resource Aware Model Based Optimization (RAMBO) mit Scheduling für heterogene Laufzeiten entwickelt. Es erweitert MBO auf die Arbeit an parallelen Systemen und maximiert gleichzeitig die Ressourcenauslastung. So können Methoden zur Optimierung der Effizienz von eingebetteten Systemen angepasst werden, um die parallele Ausführung von maschinellen Lernaufgaben in modernen Computersystemen zu verbessern. In der dritten Phase wollen wir Szenarien mit zusätzlichen Herausforderungen, wie zeitvariable Zielfunktionen, unzureichende Vorhersagequalität aufgrund kleiner Stichprobengrößen und Datenströme untersuchen. Effiziente Lösungen für diese Herausforderungen lassen sich durch die Erweiterung des RAMBO-Frameworks und die weitere Verbesserung der Planungsstrategien erzielen. Zeitvariable Zielfunktionen sind in der realen Welt üblich, d.h. die Konfiguration der am besten funktionierenden Algorithmen ändert sich im Laufe der Zeit, was allgemein als Konzeptdrift bezeichnet wird. Für kleine Stichprobengrößen werden wir die Kombination von realen Daten und zusätzlichen simulierten Daten untersuchen. Für Datenströme wird sich die maschinelle Lernmethode an Veränderungen anpassen, um die Vorhersagequalität zu optimieren. Diese Erweiterungen von RAMBO machen es für eine Vielzahl von Anwendungen nutzbar, einschließlich der Datenratenvorhersage für Mobiltelefone sowie der Verkehrsanalyse und -vorhersage.
Zur Bewertung der neuen Methoden verwenden wir etablierte Benchmarks, die ein kontrolliertes Umfeld bieten, um objektive Schlussfolgerungen zu ziehen. In einem zweiten Schritt werden wir auch unseren vorgeschlagenen Ansatz mit realen Daten überprüfen. Neben den entwickelten Methoden selbst sind ein wesentliches Ergebnis dieses Projekts eigenständige und gut dokumentierte Open-Source-Softwarepakete, die die Reproduzierbarkeit der Experimente und die zukünftige Nutzbarkeit für andere Forscher auf der ganzen Welt sicherstellen.
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