Understanding the avg_runtime Attribute in AutoSys

 Understanding the avg_runtime Attribute in AutoSys

AutoSys, a versatile job scheduling tool, provides several attributes to manage and optimize job execution. One such attribute is avg_runtime, which plays a crucial role in monitoring and managing job performance. Understanding the avg_runtime attribute helps in predicting job durations, optimizing schedules, and identifying potential issues in workflows. This article delves into what the avg_runtime attribute is, its significance, and how to effectively use it in AutoSys.

What is avg_runtime in AutoSys?

The avg_runtime attribute in AutoSys represents the average runtime of a job, calculated over a specific number of previous runs. This value provides insight into how long a job typically takes to complete, which can be crucial for scheduling, troubleshooting, and performance optimization.

How is avg_runtime Calculated?

AutoSys automatically calculates the avg_runtime by averaging the actual runtimes of a job over its last n runs. By default, AutoSys considers the last five successful runs to compute this average, but this number can vary based on your system's configuration.

The calculation process can be summarized as follows:

1. AutoSys tracks the execution time of each job from start to completion.
2. The runtimes of the last n successful runs are summed.
3. The sum is divided by n to obtain the avg_runtime.

For example, if the last five runs of a job took 10, 12, 11, 13, and 10 minutes respectively, the avg_runtime would be: $\text{avg\_runtime} = \frac{10 + 12 + 11 + 13 + 10}{5} = 11.2 \text{ minutes}$

Significance of avg_runtime in AutoSys

The avg_runtime attribute is significant for several reasons:

1. Job Scheduling: Knowing the average runtime of a job allows schedulers to optimize the job's start times, ensuring that dependent jobs are not delayed and system resources are used efficiently.

2. Performance Monitoring: By comparing the actual runtime of a job against its avg_runtime, administrators can quickly identify deviations that might indicate underlying issues, such as resource bottlenecks or changes in job behavior.

3. Alerting and Thresholds: Some organizations set thresholds based on the avg_runtime. If a job exceeds its expected runtime by a certain margin, it can trigger alerts for further investigation.

4. Capacity Planning: Understanding how long jobs typically take helps in capacity planning, ensuring that the infrastructure can handle peak loads without delays.

Using avg_runtime in AutoSys

AutoSys provides several ways to view and utilize the avg_runtime attribute. Here’s how you can make the most of it:

1. Viewing avg_runtime

To view the avg_runtime for a specific job, you can use the autorep command, which provides detailed information about the job, including its avg_runtime.

Example:

autorep -J job_name

The output will include the avg_runtime along with other job attributes and statuses.

2. Setting Alerts Based on avg_runtime

Administrators can configure AutoSys to send alerts if a job's runtime deviates significantly from its avg_runtime. This can be done using the max_run_alarm attribute, which specifies the maximum time a job can run before an alarm is triggered.

Example:

insert_job: my_job_name
job_type: c
command: /path/to/script.sh
machine: my_machine
max_run_alarm: 15

In this example, if the job exceeds its avg_runtime by a significant margin (say, 15 minutes), an alert will be generated.

3. Optimizing Schedules with avg_runtime

Schedulers can use the avg_runtime to fine-tune job start times, ensuring that jobs complete within their allocated time slots and that dependent jobs are not delayed.

For example, if a job has an avg_runtime of 10 minutes and must complete before a dependent job starts at 6:00 AM, the job should ideally be scheduled to start no later than 5:50 AM.

Best Practices for Using avg_runtime

• Regular Monitoring: Regularly monitor avg_runtime values for critical jobs to detect any significant changes that could indicate issues.

• Adjust Thresholds: Adjust max_run_alarm and other related attributes based on the avg_runtime to ensure that alerts are meaningful and not too sensitive or too lenient.

• Historical Analysis: Analyze the historical avg_runtime data to understand trends and make informed decisions about job scheduling and resource allocation.

• Resource Optimization: Use avg_runtime data to optimize resource allocation, ensuring that jobs have enough resources to complete within their expected timeframes.

Conclusion

The avg_runtime attribute in AutoSys is a valuable tool for job scheduling, performance monitoring, and optimization. By understanding and leveraging this attribute, administrators can ensure that jobs run efficiently, schedules are optimized, and potential issues are identified early. Regularly monitoring and analyzing avg_runtime can lead to more reliable and efficient job management in AutoSys, contributing to overall operational success.