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Distributed Job Schedulers: An IT Overview

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What are Distributed Job Schedulers?

Distributed job schedulers are software solutions capable of launching unattended scheduled jobs or workloads across multiple servers. 

For example, a distributed job scheduler can be installed on one or more machines, through which a user can schedule tasks to run on servers A, B, C, and D. The user can chain these tasks together into a single job, so that a successful execution of server A tasks will trigger tasks to run on server B, and so on. This would be a distributed workflow.

Distributed tasks can be either periodic or ad hoc. For example, users can schedule jobs to execute periodically (every hour, every day, every second Tuesday, etc.) or as one-off executions (retrieving files for a custom report). Distributed scheduling systems also support parallel jobs.

Why are Distributed Job Schedulers Necessary?

Decades ago, it was sufficient to have a job scheduler on a machine –such as a mainframe– that could also execute scheduled workloads and batch jobs. As time wore on, however, and those IT environments grew, organizations, departments, and even teams brought in their own servers, databases, and operating systems built on a variety of scripting languages (Java, Python, UNIX, etc.). This resulted in a fragmented approach to job scheduling, with IT teams implementing schedulers and custom scripts for specific silos.

In order to reliably schedule and automate workloads across silos, IT teams can use distributed job schedulers. Distributed schedulers can also support multiple specialized servers.

Architecture of a Distributed System

Distributed environments are typically arranged in one of three ways:

  1. Centralized: A central node distributes jobs to worker or execution nodes, and orchestrates jobs between those execution nodes.
  2. Decentralized: Multiple central nodes, each with its own subset of the system.
  3. Tiered: A three-tier architecture, for example, includes a node for the scheduling software, plus a node for the workload to be executed on, and a node for database access.

Additionally, distributed systems can also include decentralized grid computing, where each node is its own subset (both the central node and execution node), and nodes are loosely connected over a network.

In many cases, distributed scheduling systems are decentralized and managed with an open-source project such as cron (Linux/UNIX) or Apache Mesos. Data centers frequently rely on distributed scheduling via tools such as Apache Kafka or MapReduce for managing distributed computing in big data environments.

Options for tiered systems usually include proprietary tools such as enterprise job schedulers which offer greater support and reduce the need for custom scripting.

Benefits of Distributed Scheduling

The primary benefit of a distributed scheduling system is that it more fault tolerant when compared to a traditional job scheduler. With a traditional scheduling system, the job scheduler is either installed on the execution machine or else communicates with only one execution machine. Either way, if one machine goes down, critical jobs stop running.

Alternatively, if an execution machine goes down in a distributed system, the scheduler(s) can route affected jobs to available machines.

Beyond fault tolerance, the benefits of distributed scheduling depends largely on the scheduling system being used. For instance, cron jobs can be used to establish a distributed scheduling system, but requires complex coding and offers little visibility (unless you want to write more code).

Then there are open-source scheduling systems such as Chronos or Luigi. Here’s Amazon AWS’s opinion on Chronos:

“Although Chronos is a significant step up over manual scripts or cron, it still requires some manual work to implement. Further, because Chronos requires Apache Mesos to manage communications and resource allocation, it requires the installation and configuration of Mesos throughout your network.” 

Amazon AWS offers JumpCloud as its own version of distributed scheduling, however, scripting is often necessary when integrating with other technologies.

Extensible, Distributed Scheduling for the Enterprise

Enterprise scheduling platforms are distributed systems with schedulers and execution machines that can be deployed on-premises or in the cloud. These tools often provide native integrations with major vendors (Microsoft, Oracle, IBM, VMware, Amazon) and in some cases provide REST API adapters that make it possible to integrate virtually any tool or technology.

Extensible, distributed systems enable IT to orchestrate jobs, workloads, and resources through end-to-end processes.

By leveraging an extensible platform, IT can realize the full benefits of distributed scheduling:

  • Processes, infrastructure, and systems can be monitored from a single pane of glass, with centralized repositories for logging
  • End-to-end processes can be developed and iterated without having to rely on custom scripts, accelerating roll-out and reducing human error
  • High availability with non-cluster failover to ensure jobs and workloads are completed on schedule even in the event of failure or outage
  • Simplified synchronization between processes and environments

Many modern scheduling systems are distributed. But only a few are truly extensible and can support the orchestration of end-to-end processes without the need for custom scripting. As IT environments become more complex and disparate, it will become increasingly critical for IT to have a unified, extensible, distributed scheduling system.


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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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