Harvesting Resources

z/OS

Enhanced Definition

In the z/OS context, "harvesting resources" refers to the systematic collection and aggregation of operational data, performance metrics, log entries, or application-specific information from various z/OS components, subsystems, and applications. This process is crucial for monitoring, analysis, auditing, problem determination, capacity planning, and business intelligence.

Key Characteristics

- Automated Collection: Resource harvesting is typically automated through scheduled batch jobs, system utilities, or specialized monitoring agents to ensure continuous and consistent data capture.
- Diverse Data Sources: Data is collected from a wide array of z/OS sources, including System Management Facilities (SMF) records, Resource Measurement Facility (RMF) reports, system logs (SYSLOG), subsystem logs (CICS, DB2, IMS), security logs (RACF, ACF2, Top Secret), and application-specific datasets or databases.
- Purpose-Driven: The collection is always driven by a specific objective, such as performance analysis, security auditing, compliance reporting, capacity planning, or extracting data for business analytics.
- High Volume Data: Mainframe environments generate vast amounts of data, requiring robust and efficient methods for collection, storage, and initial processing to handle the scale.
- Data Transformation: Raw harvested data often requires transformation, filtering, and aggregation using tools like DFSORT, SAS, or specialized vendor utilities before it can be effectively analyzed or reported.

Use Cases

- Performance and Capacity Planning: Harvesting SMF and RMF data to analyze CPU utilization, I/O rates, memory consumption, and workload performance trends for future resource allocation and system upgrades.
- Security Auditing and Compliance: Collecting RACF/ACF2/Top Secret audit trails, system logs, and application access logs to detect unauthorized activities, track user actions, and demonstrate compliance with regulatory requirements.
- Problem Determination and Diagnostics: Gathering diagnostic data such as SYSLOG entries, CICS transaction dumps, DB2 DSNMs, or application error logs to identify, diagnose, and resolve system or application issues.
- Business Intelligence and Reporting: Extracting specific application data from DB2, IMS, or VSAM files using JCL and utilities (e.g., DSNUTILB, IDCAMS, DFSORT) for external reporting, data warehousing, or analytics platforms.
- Software Asset Management: Collecting configuration data and software inventory details from various LPARs to manage licenses, track software usage, and ensure adherence to licensing agreements.

Related Concepts

Harvesting resources is foundational to System Management Facilities (SMF) and Resource Measurement Facility (RMF), as these are primary sources of system-level performance and accounting data. It heavily relies on Job Control Language (JCL) for scheduling and executing batch jobs that perform data extraction and initial processing. The collected data feeds into processes like Performance Monitoring, Capacity Planning, Security Auditing, and Problem Determination, often involving specialized Data Transformation and Reporting Tools to make the raw data actionable.

Best Practices:

Define Clear Objectives: Before harvesting, clearly define what data is needed, why it's being collected, and how it will be used to avoid collecting unnecessary data and optimize the process.
Automate and Schedule: Implement robust JCL procedures and scheduling tools (e.g., IBM Z Workload Scheduler) to automate data collection at regular intervals, ensuring consistency and minimizing manual effort.
Optimize Extraction Efficiency: Use efficient z/OS utilities (e.g., DFSORT, IDCAMS, DSNUTILB, DFSMSdss) and techniques to minimize the impact of data extraction on production system performance and resource consumption.
Implement Data Security: Ensure that harvested data, especially sensitive information (PII, financial data), is protected throughout its lifecycle—during collection, transmission, storage, and analysis—using encryption and access controls.
Establish Data Retention Policies: Define and enforce clear data retention and archival policies for harvested data, balancing the need for historical analysis with storage costs and compliance requirements.
Validate Data Integrity: Regularly verify the completeness and accuracy of harvested data to ensure that analyses and reports derived from it are reliable and trustworthy.