Optimizing JCL (Job Control Language) jobs is crucial for improving the efficiency and performance of batch processing in mainframe environments. This section will cover various techniques and best practices to optimize JCL jobs, ensuring they run faster and use system resources more effectively.

1. Efficient Resource Allocation: Properly allocate CPU, memory, and I/O resources to avoid bottlenecks.
2. Minimizing Job Execution Time: Techniques to reduce the overall execution time of JCL jobs.
3. Reducing I/O Operations: Strategies to minimize the number of input/output operations.
4. Parallel Processing: Leveraging parallelism to execute multiple tasks simultaneously.
5. Best Practices: General guidelines to follow for writing optimized JCL jobs.

• Specify Appropriate REGION Parameter: The REGION parameter controls the amount of memory allocated to a job or step. Allocating too much or too little memory can impact performance.

  //STEP1 EXEC PGM=MYPROG,REGION=4M
  

  • Explanation: This example allocates 4 megabytes of memory to the step.

• Use the TIME Parameter Wisely: The TIME parameter limits the CPU time a job or step can use. Setting appropriate limits can prevent runaway jobs.

  //STEP1 EXEC PGM=MYPROG,TIME=1440
  

  • Explanation: This example sets the CPU time limit to 24 hours (1440 minutes).

• Efficient Data Set Allocation: Use the appropriate space allocation parameters (e.g., SPACE, UNIT) to avoid excessive I/O operations.

  //MYDATA  DD  DSN=MY.DATA.SET,DISP=(NEW,CATLG,DELETE),
  //            SPACE=(CYL,(10,5)),UNIT=SYSDA
  

  • Explanation: This example allocates a new data set with an initial space of 10 cylinders and an additional 5 cylinders if needed.

• Sort Utility: Use the SORT utility for sorting and merging data sets efficiently.

  //SORTSTEP EXEC PGM=SORT
  //SYSIN    DD  *
  SORT FIELDS=(1,10,CH,A)
  /*
  

  • Explanation: This example sorts a data set based on the first 10 characters in ascending order.

• Combine Steps: Where possible, combine multiple steps into a single step to reduce overhead.

  //STEP1 EXEC PGM=MYPROG
  //STEP2 EXEC PGM=MYPROG2
  

  • Optimization: If MYPROG and MYPROG2 can be combined into a single program, it reduces the job's overall execution time.

• Buffering: Increase the buffer size for data sets to reduce the number of I/O operations.

  //MYDATA  DD  DSN=MY.DATA.SET,DISP=SHR,BUFNO=20
  

  • Explanation: This example sets the buffer number to 20, which can improve I/O performance.

• Avoid Unnecessary Data Set Access: Only access data sets when necessary and close them as soon as they are no longer needed.

  //MYDATA  DD  DSN=MY.DATA.SET,DISP=SHR
  //        DD  DSN=MY.OTHER.DATA,DISP=SHR
  

  • Optimization: Ensure that each data set is accessed only when required.

• Parallel Job Execution: Submit multiple jobs to run in parallel using different initiators.

  //JOB1  JOB  (ACCT),'PARALLEL JOB 1',CLASS=A
  //JOB2  JOB  (ACCT),'PARALLEL JOB 2',CLASS=B
  

  • Explanation: This example submits two jobs to run in parallel, each using a different class.

• Concurrent Steps: Design jobs with multiple steps that can run concurrently.

  //STEP1 EXEC PGM=MYPROG1
  //STEP2 EXEC PGM=MYPROG2,COND=(0,LT)
  

  • Optimization: If MYPROG1 and MYPROG2 can run concurrently, design the job to allow parallel execution.

1. Regularly Review and Update JCL: Periodically review JCL jobs to ensure they are optimized for current system configurations and workloads.
2. Use Symbolic Parameters: Use symbolic parameters to make JCL jobs more flexible and easier to maintain.
3. Monitor Job Performance: Continuously monitor job performance and make adjustments as needed.
4. Document Changes: Keep detailed documentation of any changes made to JCL jobs for future reference.

Given the following JCL job, identify and implement optimizations:

1. Combine Steps if Possible: If MYPROG1 and MYPROG2 can be combined, do so.
2. Optimize Space Allocation: Use cylinders instead of tracks for better performance.
3. Increase Buffer Size: Set an appropriate buffer size for the data set.

Optimized JCL:

Optimizing JCL jobs involves efficient resource allocation, minimizing job execution time, reducing I/O operations, leveraging parallel processing, and following best practices. By implementing these techniques, you can significantly improve the performance and efficiency of your JCL jobs. In the next section, we will explore efficient data set usage in more detail.