What were the main components of Tivoli Performance Modeler?

The main system components included data collectors, a modeling engine, and a reporting module. These components worked together to gather data, perform analysis, and generate reports.

How did the components of Tivoli Performance Modeler communicate with each other?

Communication between components was facilitated through internal interfaces. The data collectors would feed data to the modeling engine, which in turn would pass results to the reporting module.

What database or storage mechanisms were used by Tivoli Performance Modeler?

The tool used a proprietary database for storing model data and configuration information. The specific database technology was not publicly disclosed.

What administrative interfaces were available for Tivoli Performance Modeler?

Administrative interfaces were primarily GUI-based, providing users with a visual environment for configuration and monitoring. The specific CLI commands and network ports were not publicly documented.

What was the primary business value of using Tivoli Performance Modeler?

Tivoli Performance Modeler provided value by enabling users to model and predict the impact of system changes. This allowed for proactive capacity planning and optimization of resource allocation.

How did Tivoli Performance Modeler help in preventing performance issues?

By modeling different scenarios, users could identify potential performance bottlenecks before they impacted end-users. This proactive approach helped in maintaining system stability and performance.

How did Tivoli Performance Modeler support decision-making processes?

The tool facilitated informed decision-making regarding hardware upgrades and resource allocation. By simulating the effects of changes, users could make data-driven decisions.

What kind of insights did the reporting features provide?

The reporting capabilities provided insights into resource utilization and performance trends. This information was crucial for optimizing system performance and ensuring efficient resource usage.

What authentication methods were supported by Tivoli Performance Modeler?

The specific authentication methods supported by Tivoli Performance Modeler were not publicly documented. Security features were limited due to the tool's age and lack of ongoing support.

What access control model was used?

The access control model used by Tivoli Performance Modeler was not explicitly documented. It likely relied on basic user roles and permissions.

What encryption methods were used?

Details on encryption usage within Tivoli Performance Modeler were not publicly available. Given its age, it likely did not include modern encryption standards.

What audit and logging capabilities existed?

Audit and logging capabilities were present to track user activities and system events. The extent and detail of these logs were not widely publicized.

What administrative interfaces were available?

Administrative tasks were performed through the GUI. These tasks included configuring data sources, defining models, and generating reports. Specific CLI commands were not widely documented.

How was user management handled?

User management was handled through the GUI. This included creating, modifying, and deleting user accounts, as well as assigning roles and permissions.

What were the main configuration parameters?

The main configuration parameters were set through the GUI. These parameters included data source connections, modeling parameters, and report settings.

What monitoring and logging capabilities existed?

Monitoring and logging capabilities were available to track system performance and user activities. The specific details of these capabilities were not widely publicized.

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Tivoli Performance Modeler

Name: Tivoli Performance Modeler
Availability: InStock

IBM Not Supported z/OS

Data Discovery Mining and Processing Tools and Utilities Administration

Vendor

IBM

Product Overview

The architecture of Tivoli Performance Modeler comprised several key components. Data collectors gathered performance metrics from RMF and CMF data sources. The modeling engine processed this data, allowing users to simulate the impact of changes to system resources, including CPU, disk I/O, paging rates, and LPAR definitions.

The reporting module generated reports based on the modeled scenarios, providing insights into resource utilization and performance trends. The GUI was the primary interface for administration, configuration, and user management. The tool used a proprietary database for storing model data and configuration information.

Communication between components was facilitated through internal interfaces. The lack of current support and security updates makes this tool obsolete. Consider migrating to IBM Z Performance and Capacity Analytics or other modern solutions.

The tool's value was in enabling proactive capacity planning and optimizing resource allocation.

Frequently Asked Questions

What was the primary function of Tivoli Performance Modeler?

Tivoli Performance Modeler was a capacity planning and performance monitoring tool designed to model the impact of changes to system resources. It used RMF or CMF data to generate reports and provide insights into system performance. The tool is no longer supported by IBM.

What specific system resources could be modeled using Tivoli Performance Modeler?

The tool allowed users to model the effects of changes to CPUs, disk I/O response times, paging rates, and LPAR definitions. This modeling capability helped in predicting the impact of system changes before implementation.

What data sources did Tivoli Performance Modeler use for its analysis?

Tivoli Performance Modeler utilized RMF (Resource Measurement Facility) and CMF (Customer Management Facility) data. These data sources provided the necessary performance metrics for analysis and reporting.

What types of outputs did Tivoli Performance Modeler generate?

The tool generated reports based on the analyzed RMF and CMF data. These reports provided insights into system performance, helping users understand resource utilization and identify potential bottlenecks.

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