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Streaming IMS Connect Trace Data for Machine Learning

  • 1.  Streaming IMS Connect Trace Data for Machine Learning

    Posted 2 hours ago

    Introduction

    Mainframes remain essential for processing high-volume transactions in industries like finance, insurance, and public services. However, they often lack real-time observability and integration with modern analytics platforms. This article explores how integrating IMS Connect with Apache Kafka and diagnostic tools enables real-time trace streaming and analytics.

    IMS Connect serves as the communication bridge between TCP/IP-enabled clients and the IBM IMS system, which encompasses both transactional and database functions. The integration of a trace and diagnostic tool with IMS Connect extends its capabilities by capturing granular operational data through event and transaction trace records. Historically, such trace information has been analyzed only after incidents occur, slowing down issue resolution. By incorporating Apache Kafka-a resilient and distributed event streaming platform-this approach enables immediate ingestion and analysis of IMS Connect traces.

    A central component of the integrated monitoring system-which combines IMS Connect, a trace and diagnostic tool, and Kafka-is the early detection of workload spikes. Unexpected increases in transaction volume can strain IMS subsystems, leading to latency or potential failure. By utilizing Kafka for trace ingestion and applying machine learning and statistical techniques, the system flags anomalies as they emerge. This empowers operations teams to act swiftly-whether by redistributing workloads, scaling infrastructure, or activating rate limits. Additionally, the analysis of long-term transaction trends supports proactive planning to mitigate future spikes.

    Another critical focus of the monitoring system is diagnosing failed messages, particularly those using the Open Transaction Manager Access (OTMA) protocol. Failures may result from timeouts, malformed requests, or backend inconsistencies. Real-time monitoring of failure patterns-enriched with contextual data via Kafka Streams or Apache Flink-enables classification of these events and supports rapid remediation. This approach significantly reduces mean time to resolution (MTTR) and strengthens overall system resilience.

    Analyzing message throughput is equally essential for evaluating system performance. Tracking the frequency of OTMA messages helps identify potential underutilization or overload scenarios. For instance, a drop in throughput may indicate system degradation, while a spike could signal a transaction surge. When combined with anomaly detection techniques, these metrics enable intelligent scaling decisions and more efficient resource management.

    Understanding client-specific performance is also a key priority. Metrics such as request volume, response latency, and failure ratios per client offer valuable visibility into usage patterns and service quality. These insights not only help identify high-traffic or error-prone clients but also form the foundation for behavior modeling using machine learning techniques. Such modeling supports the forecasting of future trends and enables targeted performance optimizations.

    At the heart of the solution lies a robust real-time pipeline. Trace records are extracted from a trace and diagnostic tool with IMS Connect journal, parsed, and sent to Kafka topics using producer applications. Streaming frameworks like Kafka Streams process this data for enrichment, filtering, and categorization. The output is then visualized using platforms like Tableau, offering dynamic dashboards for monitoring system behavior, transaction flow, and issue hotspots.

    The monitoring system's intelligence is further strengthened through predictive modeling. Deterministic algorithms detect predefined conditions, while ML models identify emerging or previously unseen issues. Techniques like time-series forecasting, clustering, and supervised classification are explored to uncover insights and automate anomaly prediction.

    This article demonstrates how mainframe environments can be augmented with real-time, cloud-native analytics technologies. By combining the resilience of z/OS with the agility of Kafka-based streaming, organizations can unlock deep operational insights, minimize downtime, and elevate system responsiveness. This integration lays the groundwork for a new generation of intelligent mainframe monitoring solutions.

    Streaming Pipeline

    Figure : Trace and diagnostic tool with IMS Connect  - records for streaming


    1.        Data Ingestion: Trace records are collected in real time from a diagnostic tool integrated with IMS Connect, then normalized and prepared for Kafka ingestion.

    2.        Kafka Cluster: The central streaming backbone buffers, partitions, and persists the incoming records, allowing for high-throughput data processing with fault tolerance.

    3.        Stream Processing: Kafka Streams consumers process the data. Tasks include enrichment with metadata, filtering, failure classification, and performance metric computation.

    4.        Visualization & Dashboards: The processed output is consumed by visualization layers that support live monitoring dashboards. These platforms offer dynamic rendering of spikes, failure patterns, and client metrics.

    5.        Machine Learning Integration: The streamed data is also available to data science pipelines that apply anomaly detection and predictive analytics models.

    Analytics and machine Learning insights

    Integrating trace diagnostics from IMS Connect with real-time streaming platforms enables advanced analytics, alerting, and predictive modeling.  

    Detecting Workload Spikes

    Figure : Detecting workload spikes – message rate/minute vs time


    Transaction spikes can strain IMS, causing delays or failures. Real-time trace analysis enables immediate detection of such anomalies using statistical thresholds and ML models like isolation forests. Dashboards visualize these events, triggering alerts for quick response. Historical trends also support forecasting future spikes for better resource planning.

    Analysis of Failed OTMA Messages

    Figure : Failed OTMA message analysis


    OTMA failures often signal systemic or client-specific issues like timeouts or malformed requests. Streaming and classifying these traces in real time using Kafka Streams or Flink enables error categorization and root-cause analysis. Enriched data supports failure dashboards, reducing MTTR and improving fault visibility.

    Monitoring IMS Connect Message Throughput

    Figure : Real-time monitoring – IMS Connect message throughput 


    Analyzing OTMA message rates helps detect underutilization, overload, or bottlenecks. Time-series trends reveal drops indicating lag or spikes suggesting transaction floods. Combined with anomaly detection, this enables early capacity risk alerts and performance tuning.

     

    Real-Time Client Behavior Analytics

    Monitoring client interactions with IMS Connect is key to SLA compliance and performance tuning. Metrics like message rate and latency feed ARIMA and LSTM models to forecast trends-growth or stabilization. These forecasts, combined with clustering and anomaly detection, highlight abnormal usage, enabling proactive tuning and early issue detection. 

    Conclusion
    This work illustrates how integrating IMS Connect with real-time streaming and analytics frameworks enables modern observability, predictive modeling, and operational intelligence in legacy environments. By unlocking trace data for active use, organizations can shift from reactive problem-solving to proactive, data-driven decision-making.



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    Santosh Dorge
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