Event correlation provides a powerful lever to analyze the events data to better manage the enterprise IT systems. While many event correlation algorithms are present in literature, applying them in practice presents many real-world challenges. In this paper, we present our experience with the challenges, the workarounds, and the opportunities in using event correlations in enterprise IT systems. We propose a domain-aware way to select the right scope of events for correlation, with self-tuning parameters and recommending right correlation signatures based on the use-case. We present the effectiveness of our ideas with a real-world case-study.

Organizations across the globe require a reliable anomaly detection solution that allows for continuous quality control. Considering the scale and complexity of infrastructure, the most common methods include setting a blanket threshold by using knowledge of the experts or by applying simple statistical measures, which results in an alarm deluge. In this paper, we propose an approach to derive optimal thresholds by analyzing both the temporal and spatial properties of metrics related to entities. Additionally, our solution also self-tunes and self-learns to accommodate the tacit knowledge of experts and domains constraints. We demonstrate the effectiveness of our solution through a series of experiments and a real-world case study.

Today's enterprises heavily rely on their batch systems to ensure smooth business operation. Any delay in these processes has direct impact on sales, revenue, customer experience, and brand image. In this paper, we present an approach to predict these batch processes and generate ahead-of-time notifications of potential batch-induced outages. We present several case-studies to demonstrate the effectiveness of the proposed solution in various real-world scenarios.