Relevance of Using Interpretability Indexes for the Design of Schedulers in Cloud Computing Systems
Sebastián García Galán, Mouad Seddiki, Rocío Pérez de Prado, Enrique Munoz Expósito, Adam Marchewka, N. Ruiz-Reyes · 2020
At the dawn of the fourth industrial revolution, artificial intelligence is impregnating our society by an overwhelming number of applications with rising complexity. This circumstance has triggered a new debate on explainable artificial intelligence in terms of transparency and confidence. Therefore, the relevance of interpretability is remarkable since this concept provides us with transparence and understandability in avoidance of black-box systems. On the other hand, cloud computing is a new paradigm of distributed computation based on the externalization of computing needs offered as services, whose performance has a significant economic and environmental impact and is strongly influenced by the scheduler, which is, likely, the most critical part in charge of allocation computational resources. In this regard, fuzzy rule-based systems are knowledge-based systems that are increasingly arising as an alternative for the development of cloud scheduling systems, mainly due to their intrinsic features such as adaptability to environments, dynamism and capability to cope with uncertainty. Bearing in mind the above-mentioned ideas, this paper presents a study-case in which the interpretability in fuzzy rule-based schedulers for cloud computing, obtained through automatic learning processed, has been analyzed. Results show how an inherent feature, like interpretability, vanishes due to the use of learning processes for both total execution time and power consumption optimization, which put the focus on the relevance of facing interpretability in this kind of systems.