What is Traditional Job Scheduling? And How did Enterprise Job Scheduling Evolve?

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Job Scheduling has been around for decades. It can be defined most simply as the orderly, reliable sequencing of batch program execution. Put simply, a job scheduler is a tool for automating IT processes. Historically, a scheduler has been an IT Ops data center tool, rather than something that is application-centric. It was established based on the need at the operating system level to run jobs. Job Scheduling uses clocks and calendars to determine the timing of various processes critical to the business. For example, the Finance department establishes a regular time when the books will close, which in turn determines when IT Ops will need to run month-end financial reports.

Job scheduling packages are used for controlling the unattended processing of batch jobs on a single system. Job schedulers are generally platform-specific. They are additionally configured to submit batch jobs for execution according to a pre-defined schedule or after a dependent event occurs. In most cases, this is done on a platform-by-platform basis. In fact, there are many native job schedulers built into operating systems. Microsoft Windows features Windows Task Scheduler, while Linux and UNIX platforms have CRON as their native job scheduler. These job schedulers emerged as tools for executing tasks during the "batch window,”— that is, running batch jobs after-hours when business processes are finished. Job schedulers take care of activities like database maintenance and prevent the need for operator intervention during the job schedule.

Job Scheduling takes one small evolutionary step beyond the fully manual execution of jobs and tasks known as batch processingRealizing the limitations of batch processing, IT operators employing batch processing eventually began looking to operating system tools to help automate labor-intensive manual jobs management. In the UNIX world, for example, that tool was CRONtab. Because CRONtab fulfilled the urgent need for a job scheduling tool, it was widely used,  despite significant drawbacks such as:

  • No business calendars
  • No dependency checking between tasks
  • No centralized management functions to control or monitor the overall workload
  • No audit trail to verify jobs had run
  • No automated restart/recovery of scheduled tasks
  • No recovery from machine failure
  • No flexibility in scheduling rules
  • No ability to allow cross-platform dependencies

Despite these limitations, job scheduling increased in both use and popularity. On the mainframe, a combination of internal operating system functionality with third-party scheduling tools were a bit more sophisticated in what they provided, but were still limited in addressing the more advanced emerging automation challenges. Over time, data centers began to shift from a mainframe-centric world to a distributed systems environment using multiple servers. Likewise, businesses were increasingly moving off of singularly powerful legacy systems and onto distributed servers built on Unix, Linux, or even Windows. This required the purchase of more servers, which didn’t have the same unit of power as a mainframe as standalone units, but working in combination could still offer equivalent power at a fraction of the cost. 

Evolution of Job Scheduling

Exactly how did manual batch processing evolve into its more autonomous ancestor, job scheduling? The short answer is: enterprise growth and demand. Batch processing, the procedure by which computers automatically complete pre-determined batches of jobs in sequential order with minimal human interaction, was in the end still a highly manual process, requiring data entry clerks for both set up and execution. Batch processing therefore relied heavily on IT operations, limiting their capacity to perform other work as more processes were added based on various business demands.

Job scheduling, as a result, introduced a new axis of automation - time and dependence. Using this new function, IT operations could schedule processes to run on certain dates and times, rather than having to trigger them manually. Some examples of dependent events that can submit jobs in a job scheduler are specific days and times of the week (i.e. Monday at 10:00 AM or Tuesday at 3:00 AM), specific days of the month (i.e. last date of month; first Monday; the 15th), or the completion of another batch job.

Evolving from batch processing to job scheduling meant that IT operations were one step closer to dynamically aligning with company-wide business processes. Rather than supporting, but being separated from weekly and monthly business activities, jobs and business processes now had the ability to be more synchronized and simultaneously lower maintenance. But despite this leap forward, traditional job scheduling still has its limitations. 

Challenges of Job Scheduling

In concept, the demands for distributed job scheduling were similar to those of mainframe driven, time-based sequencing and dependencies models. However, the move to distributed computing created new difficulties. For example, the lack of centralized management control became an issue when jobs were running on many different servers, rather than on a single central computer. As mentioned above, in contrast to this distributed environment, traditional job scheduling software generally runs jobs only on one machine. This introduces a number of problems for organizations, including:

  • Silos: job schedulers generally don’t talk or work with each other across platforms. This led to a lack of synchronization between job schedulers running on different operating systems, such that critically related jobs on different operating systems had the possibility of not running, or running out of sequence. 
  • Complexity: more time is needed for IT personnel to perform their duties. Scheduling jobs and maintenance becomes more complicated as several schedulers on different operating systems must now be manually configured and maintained, increasing complexity and the potential for errors. 
  • Manual intervention: job schedulers frequently require manual intervention to correct problems between related scheduled processes on different machines, such as when a file is created on one machine and FTP’d to a second machine to be processed.
  • Extra Programming: job scheduling frequently requires additional scripting or programming to fill in gaps that occur when coordinating processes between machines and operating systems. 
  • Missing Links: traditional job schedulers do not come with integrated or managed file transfer capabilities, limiting or altogether halting the data supply chain until manual workarounds are configured. 
  • Rigidity: job scheduling functions best when each sequence of jobs begins and ends in a single platform. Moving jobs across different platforms, therefore, is quite difficult, causing new challenges when business processes inevitably change or evolve.
  • System Incompatibility: job schedulers often lack compatibility with systems management solutions, leading to frequent critical update issues, errors, and additional network maintenance. 
  • Visibility: although job schedulers are always accompanied by service level management agreements, running multiple schedulers on multiple machines or platforms greatly limits the ability to accurately evaluate run times via service level management
  • Time-Based: Job schedulers typically run batch processing at a certain time, which is referred to as time-based scheduling. The upcoming automation use cases however require a dynamic automation approach that is based on real-time events. 


Job scheduling was a necessary and revolutionary step forward in IT automation, allowing IT operations to eliminate much of the manual processes filling their schedules and opening a world of possibilities for digital enterprises. Given the complexity of modern business environments, however, traditional job scheduling alone is not a set-and-forget automation solution. In order to achieve truly dynamic, real-time automation, it is necessary to invest in an enterprise job scheduling software that can coordinate IT workflows and IT jobs across platforms and environments, orchestrating previously siloed systems into one harmonious force. If your organization is interested in taking your traditional job scheduling to the next level, contact us today to learn how.  

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Leon Stamm

Written by Leon Stamm

Leon Stamm is the Director of Marketing for Stonebranch Worldwide. He has been with Stonebranch for 4 years.