Disks are among the most frequently failing components in today's IT environments. Despite a set of defense mechanisms such as RAID, the availability and reliability of the system are still often impacted severely. In this paper, we present a highly accurate SMART-based analysis pipeline that can correctly predict the necessity of a disk replacement even 10-15 days in advance. Our method has been built and evaluated on more than 30000 disks from two major manufacturers, monitored over 17 months. Our approach employs statistical techniques to automatically detect which SMART parameters correlate with disk replacement and uses them to predict the replacement of a disk with even 98% accuracy.

Configuring enterprise database management systems is a notoriously hard problem. The combinatorial parameter space makes it intractable to run and observe the DBMS behavior in all scenarios. Thus, the database administrator has the difficult task of choosing DBMS configurations that potentially lead to critical incidents, thus hindering its availability or performance. We propose using machine learning to understand how configuring a DBMS can lead to such high risk incidents. We collect historical data from three IT environments that run both IBM DB2 and Oracle DBMS. Then, we implement several linear and non-linear multivariate models to identify and learn from high risk configurations. We analyze their performance, in terms of accuracy, cost, generalization and interpretability. Results show that high risk configurations can be identified with extremely high accuracy and that the database administrator can potentially benefit from the rules extracted to reconfigure in order to prevent incidents.

Workflow graphs (WFGs) are control-flow graphs extended by parallel fork and join. They are used to represent the main control-flow of e.g. business process models modeled in languages such as BPMN or UML activity diagrams. A WFG is said to be sound if it is free of deadlocks and exhibits no lack of synchronization. We study the question whether the executions of a time-annotated sound WFG meet a given deadline. We present polynomial-time algorithms and NP-hardness results for different cases. In particular, we show that it can be decided in polynomial time whether some executions of a sound WFG meet the deadline. Furthermore we show that for general probabilistic WFGs, it is NP-hard to determine whether the probability of an execution meeting the deadline is higher than a given threshold, whereas the expected duration of an execution can be computed in polynomial time.

We present a novel technique that optimizes the dispatching of incident tickets to the agents in an IT Service Support Environment. Unlike the common skill-based dispatching, our approach also takes empirical evidence on the agent's speed from historical data into account. Our solution consists of two parts. First, a novel technique clusters historic tickets into incident categories that are discriminative in terms of agent's performance. Second, a dispatching policy selects, for an incoming ticket, the fastest available agent according to the target cluster. We show that, for ticket data collected from several Service Delivery Units, our new dispatching technique can reduce service time between $35\%$ and $44\%$.