Dynamic topology, on the other hand, refers to a dynamic map that captures the resources and their relationships as the environment changes at run-time and provides a near-real-time visibility of the same.

Static topology refers to a map that is constructed based on the build and deploys information on applications and infrastructure components. Static and dynamic topology managementĪpplication and network topology refer to a map or diagram that lays out the connections between different mission-critical applications in an enterprise. Anomalies detected from metrics, logs, and tickets are grouped using multiple algorithms (e.g., Temporal, Spatial, and Association Rule mining) in Watson AIOps for event grouping. The goal of event grouping and classification is to reduce the noise for IT operations management personnel and help them focus on a few important events that need their immediate attention. For example, an application has become unavailable or a disk is full/reaching capacity, etc. Event groupingĪn event indicates that something that is noteworthy has happened in an IT operations environment. It employs a set of time-tested time-series algorithms (e.g., Granger Causality, Robust Bounds, Variant/Invariant, Finite Domain, and Predominant Range) to capture seasonality and significant trends and to perform forecasting. Watson AIOps’ metric-based anomaly detection analyzes metrics data from various systems (e.g., New Relic, AppDynamics, and SolarWinds) to automatically learn the normal behavior of metrics in your company and detect anomalies from those metrics. Users don’t have to set static thresholds or manual rules to detect anomalies. We use deep learning algorithms to both prepare features from logs during log parsing and to make anomaly predictions.

This is much sooner than traditional thresholding-based or error-string-matching types of alerting techniques can, thereby significantly reducing the meant time to diagnose an incident. IBM Watson AIOps’ state-of-the-art and multi patent-pending log anomaly detection technology is capable of automatically parsing IT application and infrastructure logs from log aggregation tools (e.g., Splunk, Humio, LogDNA, and Logstash) to automatically detect anomalies in real-time. In this article, we describe the AI in our Watson AIOps solution: This, in turn, saves millions of dollars by preventing direct costs (lost revenue, penalties, opportunity costs, etc.) and indirect costs (customer dissatisfaction, lost customers, lost references, etc.).įortunately, Watson AIOps can do all of this for you! Figure 1 shows the various types of data that Watson AIOps can process. AI can help IT operations managers, also known as Site Reliability Engineers (SREs) in detecting issues early, predicting them before they occur, reducing event and alert noise, locating the specific application or infrastructure component that is the source of the issue, determining the scope of incident impact, and recommending relevant and timely actions.Īll these analytics help reduce the mean times to detect (MTTD), identify/isolate (MTTI) and resolve (MTTR) an incident. Information Technology (IT) Operations management is a vexing problem for most companies that rely on IT systems for mission-critical business applications.ĭespite the best intentions of engineers, good designs, and solid development practices, software and hardware systems deployed in companies in service of critical business applications are susceptible to outages, resulting in millions of dollars in labor, revenue loss, and customer satisfaction issues each year.Īrtificial intelligence (AI) can help solve these problems.

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