This content is part of the Essential Guide: Guide to Google Cloud Platform services in the enterprise

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Google Cloud Scheduler brings job automation to GCP

Google's Cloud Scheduler managed service assists with job execution and management for cloud workloads, and it evens another score with AWS and Azure.

As an old-time TV pitchman who sold a popular rotisserie oven once said, sometimes, you just like to set it and forget it.

In a sense, that's what Google Cloud Platform wants customers to do with the workloads they run on GCP through Cloud Scheduler, a managed service built on the widely used open source job scheduling tool, cron.

Many GCP customers already use a simple version of cron, but that has some logistical overhead. For one, developers must manage the underlying infrastructure connected with cron and restart jobs manually if one doesn't complete properly. And checking to see if a cron job has run successfully involves some manual labor.

Google Cloud Scheduler, now in beta, masks that complexity, according to the vendor. It resends jobs until they execute successfully and supports fault tolerance for the Cloud Scheduler instance itself, with the option to deploy it in multiple GCP regions. Customers can invoke schedules through a UI, command-line interface or API, and they can monitor jobs' status through the UI. Cloud Scheduler uses a serverless architecture, so customers only pay for job invocations, as needed.

Google Cloud Scheduler checks another box for enterprise appeal

Compared to its competitors, Google is late to the game with Cloud Scheduler, although it has had a manual cron service for App Engine.

Microsoft's Azure Scheduler service became generally available in late 2015, but it will be replaced by Azure Logic Apps. The latter has a broader functional intent and scope than Cloud Scheduler, with additional capabilities for application and process integration, data integration and B2B communication, among others.

AWS rolled out similar capabilities to Scheduler with its Batch service in late 2016, and users can also schedule functions with cron in AWS Lambda.

[Google Cloud Scheduler] helps DevOps [teams] focus on higher-level problems, rather than basic plumbing.
Holger Muellervice president and principal analyst, Constellation Research

Still, Cloud Scheduler is another indication of GCP's ambitions to attract more business from enterprises, which run large quantities of regular jobs, such as database updates and reports.

While Google encourages customers to use Cloud Scheduler for App Engine workloads on GCP, the service also works with any HTTP/S endpoint or Publish/Subscribe messaging topic. One example of the former is an on-premises enterprise application that exposes back-end data to a cloud service via HTTP/S.

Publishers take many forms, such as a sensor installed at a remote oil rig. As the sensor generates various types of messages, the publish/subscribe approach sends them to a broker system, which then forwards them on to subscribers in real time. This approach can save time and effort by eliminating the maintenance of a slew of point-to-point integrations, and it makes sense for use cases such as IoT. Google offers a publish/subscribe service for GCP.

Google Cloud Scheduler uses a serverless architecture, so customers only pay for job invocations as needed; pricing starts at $0.10 per job, per month, with three free jobs per month. It's difficult to compare Cloud Scheduler's cost to, for example, Azure Scheduler, which has a much more granular pricing model.

Tools such as Google Cloud Scheduler, AWS Batch and Azure Scheduler can reduce IT tasks, but as with all investments into infrastructure, enterprises must weigh the efficiencies and ease of use of automation versus vendor and tool lock-in concerns.

"If you're running a lot of Google services, in general, or are building next-generation applications, this can provide significant operational time savings," said Holger Mueller, vice president and principal analyst with Constellation Research in Cupertino, Calif. "It helps DevOps [teams] focus on higher-level problems, rather than basic plumbing."

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