## 5 Experiments and Discussions
### Rule Validation with Dempster-Shafer Theory  
To generalize expert views or knowledge gained over larger contexts and measured data horizons related to various scales of product ecosystems and the workloads they manage, we alternatively study a special rule verification framework and the corresponding classifier proposed in [8]. This takes expert hypotheses as rules defined on features (and their combinations) to assess their quality, thus realizing an interpretable what-if analysis for further utilization in the knowledge store and real-time remedial executions. This framework utilizes DST of evidence or plausibility as additional assistance to help experts validate their observations about the environmental conditions in the classification setting. Thus, if their experience tells them that some patterns or behaviors of specific features within particular ranges might lead to the system misbehaving, they plug those hypotheses (in other words, rules) into the DS classifier and obtain validation of their quality, including uncertainty estimates to account for. Moreover, the classical rule induction algorithms and DS rule verification approach can be leveraged and combined, while feeding the automatically learned rules from the first strategy to be estimated with the second. In  
Figure 8: JRip rules for the first dataset (CaSA). Figure 9: JRip classifier performance for the first dataset (CaSA). particular, Rule 1, discovered by [PERSON], obtains a low uncertainty estimate through DS rule testing:  
(CaSA API Calls Total Requests \(\leq\) 0.1) and  
(CaSA Garbage Collector Aggregated Collection Time \(\geq\) 0.03)  
with a probability of positive class 0.943 and uncertainty 0.057. For Rules 2 and 3 learned by [PERSON], DST results in the following estimates:  
Collector To Controller Lookup Resource Elapsed Time Summary \(\geq\) 0.002  
with a probability of positive class 0.861 and uncertainty 0.139; and  
Net TCP Listen = 0  
with a probability of positive class 0.763 and uncertainty 0.237. In the above, we see a higher uncertainty in the last rule compared to the previous ones, which indicates that there is less evidence for such a condition leading to a KPI breach. The user of this framework can simply break value ranges (rule intervals) of all the data frame features to verify how they can be indicative, standalone, or in any combination. Here is a set of rules validated with splitting features into two ranges, which in several cases surprisingly results in high-quality rules:  
CaSA API Calls Total Requests < 0.424,  
with a probability of positive class 0.998 and uncertainty 0.002;  
CaSA Threads = 0.5,  
with a probability of positive class 0.993 and uncertainty 0.067;  
CaSA Free Physical Memory > 0.397,  
with a probability of positive class 0.859 and uncertainty 0.141;  
CaSA Garbage Collector PS Mark Sweep collection Time > 0.218,  
with a probability of positive class 0.857 and uncertainty 0.143. Another complex rule enforced by the user obtains the following estimate:  
(CaSA GarbageCollector PS MarkSwep Collection Count = 1) and  
(CaSA GarbageCollector PS Scavenge Collection Count = 0),  
with a probability of positive class 0.973 and uncertainty 0.027. The two-interval split is just motivated by the simplicity of rules that experts can expect from the DS rule verification. It also has cheap performance in terms of the algorithm execution. The probability of the positive class and the uncertainty level in the rule estimated by the DST are the main factors the expert user could take into account in approving it for the Knowledge Base and linking it to the troubleshooting procedures and action frameworks. As an expert tool, the DST rule verification allows users to discover patterns/conditions that affect the KPI state, thus guiding them to focus on the corresponding attributes of the environment in root cause identification or prevention of potential misbehaviors.