Batch Processing and Workload Orchestration: An Overview

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Batch processing refers to the scheduled execution of jobs.

What is Batch Workload Processing?

Batch workload processing refers to groups of jobs (batches) that are scheduled to be processed at the same time. Traditionally, batch workloads are processed during batch windows, periods of time when overall CPU usage is low (typically overnight). The reason for this is two-fold:

  1. Batch workloads can require high CPUs, occupying resources that are needed for other operational processes during the business day
  2. Batch workloads are typically used to process transactions and to produce reports, for example, gathering all sales records that were created over the course of the business day

Today, batch processing is done through job schedulers, batch processing systems, workload automation solutions, and applications native to operating systems. The batch processing tool receives the input data, accounts for system requirements, and coordinates scheduling for high-volume processing. Batch processing differs from stream processing in that batch processing requires non-continuous information.

A History of Batch Processing

Batch processing is rooted in the pre-history of computers. As far back as 1890, the United States Census Bureau used an electromechanical tabulator to record information from the US census. Herman Hollerith, who invented the Tabulator, went on to found the company that in turn became IBM.

CDC 6600 was an early supercomputer that ran batch processes.
The CDC 6600 supercomputer, circa 1964 / Photo by Arnold Reinhold

By the middle of the 20th century, batch jobs were being run using data punched on cards. In the 1960s, with the development of multiprogramming, computer systems began to run multiple batch jobs at the same time to process data from magnetic tape instead of punch cards.

As mainframes evolved and became more powerful, more batch jobs were being run and so applications were developed to make sure that batch jobs only ran when there were sufficient resources, in order to prevent delays. This helped give rise to modern batch processing systems.

Examples of Batch Processing

Batch processing use cases can be found in banks, hospitals, accounting, and any other environment where a large set of data needs to be processed. For example, report generations run after the close of business, when all credit card transactions have been finalized. Utility companies collect data on customer usage and run batch processes to determine billing.

In another use case, a financial data management company runs overnight batch processes that provide financial reports directly to the banks and financial institutions they serve.

Advantages and Disadvantages of Batch Processing

Batch processing is useful because it provides a method of processing large amounts of data without occupying key computing resources. If a healthcare provider needs to update billing records, it might be best to run an overnight batch, when demands on resources will be low.

Similarly, batch processing helps reduce downtime by executing jobs when computing resources are available.

Batch processing tools, however, are often limited in scope and capability. Custom scripts are often required to integrate the batch system with new sources of data, which can pose cybersecurity concerns where sensitive data is included. Traditional batch systems can also be ill-equipped to handle processes that require real-time data, for example stream processing or transaction processing.

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Modern Batch Processing Systems

Modern batch processing systems provide a range of capabilities that make it easier for teams to manage high-volume workloads. This can include event-based automation, constraints, and real-time monitoring. These modern capabilities help ensure that batches only execute when all necessary data is available, reducing delays and errors.

In order to further reduce delays, modern batch processing systems include load balancing algorithms to make sure batch jobs are not sent to servers with low memory or insufficient CPUs available. 

Meanwhile, advanced date/time scheduling capabilities make it possible to schedule batches while accounting for custom holidays, fiscal calendars, multiple time zones, and much more.

However, because of the growing need for real-time data and the increasing complexity of modern data processing, many IT organizations are opting for workload automation and orchestration platforms that provide advanced tools for data management and integration.

Batch Processing Takes to the Cloud

The modern IT department is diverse, distributed, and dynamic. Instead of relying on homogeneous mainframes and on-premises data centers, batch processes are being run across hybrid environments. There’s a good reason for this.

As mentioned earlier, batch processes are frequently resource-intensive. Today, with the growth of big data and online transactions, batch workloads can require quite a lot. Leveraging cloud infrastructure gives IT the ability to provision compute resources based on demand, instead of having to install physical servers that would, for a good chunk of the day, likely be idle.

The amount of data IT has to manage to meet business needs continues to grow, and batch workload tools are evolving to meet these needs. For example, IT doesn’t have the resources needed to manually execute each ETL process, or to manually configure, provision, and deprovision VMs. Instead, batch workload tools are being used to automate and orchestrate these tasks into end-to-end processes.

For example, an automation and orchestration tool can be used to move data in and out of various components of a Hadoop cluster as part of an end-to-end process that includes provisioning VMs, running ETL jobs into a BI platform, and then delivering those reports via email.

As organizations become more dependent on cloud-based resources and applications, the ability to orchestrate job scheduling and batch workloads across disparate platforms will become critical.

Batch Workload Orchestration

Automation and orchestration tools are increasingly extensible, with several workload automation solutions already providing universal connectors and low-code REST API adapters that make it possible to integrate virtually any tool or technology without scripting.

This is important, because instead of having job schedulers, automation tools, and batch processes running in silos, IT can use a workload orchestration tool to centrally manage, monitor, and troubleshoot all batch jobs.

IT orchestration tools can, for example, automatically generate and store log files for each batch instance, enabling IT to quickly identify root causes when issues arise. Real-time monitoring and alerting make it possible for IT to respond to or prevent delays, failures, and incomplete runs, accelerating response times when issues do occur.

Automatic restarts and auto-remediation workflows are also increasingly common, while batch jobs can be prioritized to ensure that resources are available at runtime.

Additionally, extensible batch workload tools make it possible to consolidate legacy scripts and batch applications, enabling IT to simplify and reduce operational costs.

Future of Batch Processing

Traditional batch scheduling tools have given way to high-performance automation and orchestration platforms that provide the extensibility needed to manage change. They enable IT to operate across hybrid and multi-cloud environments and can drastically reduce the need for human intervention.

Machine-learning algorithms are being used to intelligently allocate VMs to batch workloads to reduce slack time and idle resources. This is critical for teams managing high-volume workload runs or with large numbers of virtual or cloud-based servers.

With machine learning running in real-time, additional resources can be reserved if an SLA-critical workload is at risk of an overrun. This includes provisioning additional virtual or cloud-based machines based on dynamic demand. Coupled with auto-remediation, this provides a powerful tool to make sure that service delivery isn’t delayed to the end-user or external customer.

In the long-run, IT is becoming more diverse and distributed, and the types of workloads IT is responsible for will continue to expand. The maturation of new technologies -artificial intelligence, IoT, edge computing- will place new pressures on IT teams to quickly integrate new applications and technologies.

IT is rapidly changing, but some things, such as batch processing, stay the same. 

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Brian is a staff writer for the IT Automation Without Boundaries blog, where he covers IT news, events, and thought leadership. He has written for several publications around the New York City-metro area, both in print and online, and received his B.A. in journalism from Rowan University. When he’s not writing about IT orchestration and modernization, he’s nose-deep in a good book or building Lego spaceships with his kids.

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