Increasing workloads and complex interdependencies between jobs make monitoring schedules and predicting batch performance difficult. Complex environments require proven solutions to forecast workloads, verify performance, and understand scheduling and Job Control Language (JCL) environments. ASG Workload Optimization Suite is comprised of ASG-Workload Planner, ASG-Workload Analyzer, and ASG-Workload Mapper™, and addresses all three of these important needs, allowing Production Control, Operations Management, and other data center staff to better manage critical batch processing environments. ASG-Workload Planner reduces data center complexity by providing an easy-to-use, interactive graphical presentation of the information contained in your job scheduling program’s database. Information is presented in flowchart format so that activities and their interdependencies are clearly and concisely illustrated. ASG-Workload Analyzer offers data centers an innovative, graphical tool to track processing performance. Using ASG-Workload Analyzer, the data center staff can efficiently analyze the effectiveness of its processing, and analyze problem areas. Long hours analyzing data with homegrown analysis programs are virtually eliminated. With ASG-Workload Analyzer, workload analysis and tracking information is available in easy-to-interpret graphs. ASG-Workload Mapper is a knowledge-gathering tool that provides a comprehensive view of IT software assets and interrelationships across the enterprise. It focuses on your scheduling and JCL environments and provides a detailed interaction analysis between data and application flow. Interfaces are available to automate the capture of details from scheduler(s), JCL, and applications into integrated views of their relationships. These views are also interactive and allow the information to be seen in a variety of ways (e.g., list, graphic, cross-reference, etc.). Because users are able to quickly generate queries, the needed information is just a click away. ASG-Workload Mapper’s cross-referencing functions are extensive. Users can follow the flow of data between different applications or quickly identify the impact of a change.