What is Distributed Job Scheduling? An Overview
Distributed job scheduling refers to scheduling jobs across multiple nodes in a distributed computing system. It enables organizations to distribute workloads across multiple machines and optimize resource utilization, increasing efficiency and reducing costs.
In this article, we will take an in-depth look at what distributed job scheduling is, how it works, and why it is essential for modern distributed computing systems. We will also discuss some of the key benefits of using distributed job scheduling and the different types of job scheduling algorithms used in distributed systems.
What Are Distributed Job Schedulers?
Distributed job schedulers are software tools that can initiate scheduled jobs or workloads across multiple servers, without the need for manual intervention.
This is the workflow:
- Distributed job schedulers divide a large task or job into smaller, more manageable units called subtasks or tasks.
- The scheduler assigns these subtasks to different machines in the distributed system based on available resources, workload, and priority.
- The machines execute their assigned subtasks and communicate the results back to the scheduler.
Distributed job schedulers typically include:
- Load balancing: ensures that tasks are distributed evenly across available resources, avoiding overloading or underutilizing any machine.
- Fault tolerance: enables the scheduler to recover from failures in the system, such as a crashed machine, without losing any data or progress.
- Scalability: allows the system to handle increasing tasks or machines as needed.
Examples of distributed job schedulers include Apache Hadoop’s YARN, Apache Mesos, and Kubernetes, widely used in large-scale distributed computing environments such as data centers, cloud computing platforms, and scientific computing clusters.
What are the Benefits of Distributed Job Schedulers?
Having a mainframe job scheduler to execute scheduled workloads and batch jobs is no longer sufficient.
As the IT world became dominant, organizations, departments, and teams brought their own servers, databases, and operating systems built with different programming languages (like UNIX, Java, Python, SQL and more), which resulted in a fragmented approach, with each team implementing their own schedulers and custom scripts for specific silos.
IT teams now require distributed job schedulers to schedule and automate workloads across these silos reliably. The most effective job schedulers can support multiple specialized servers, enabling organizations to manage and optimize their computing resources more efficiently.
Other benefits include:
- Job execution in parallel across multiple machines reduces the time required to complete tasks.
- With the ability to manage and execute jobs across multiple machines, distributed job schedulers can handle more workloads.
- Fault tolerance and load balancing capabilities improve system reliability even in machine failures or other issues.
- Scaling to handle large workloads or increasing numbers of machines, allows the system to keep up with growing demands.
There are additional use cases. As an example, a distributed scheduling system can be set up using cron jobs, but it necessitates intricate coding and provides minimal visibility (unless more code is written).
Open-source scheduling systems like Chronos or Luigi are also available. While Amazon AWS offers JumpCloud as its own version of distributed scheduling, scripting is frequently required when integrating with other technologies.
What is the Architecture of a Distributed System?
A distributed system typically consists of multiple nodes or machines connected through a network.
Each node in the system performs a specific function and can communicate with other nodes to exchange information or perform tasks collaboratively. The nodes are:
- Centralized: The central node is responsible for distributing jobs to workers or execution nodes and execution orchestration of those jobs between those nodes.
- Decentralized: The system is divided into subsets, each managed by a separate central node.
- Tiered: A three-tier architecture with three nodes: one for scheduling software, another for executing the workload, and a third for accessing the database.
Distributed systems can incorporate decentralized grid computing, where each node functions as its own subset, and the nodes are connected over a network with loose connections. Decentralized scheduling systems are often managed using open-source projects like cron (Linux/UNIX) or Apache Mesos, while data centers may use tools like Apache Kafka or MapReduce for managing distributed computing in big data environments.
Tiered systems have various options, including proprietary tools like enterprise job schedulers that provide more support and reduce the need for custom scripting.
One of the available options for tiered systems is to use proprietary tools like enterprise job schedulers, which provide enhanced support and minimize the requirement for custom scripting.
The Types of Job Scheduling Algorithms
The distributed system algorithms are responsible for dividing tasks into smaller sub-tasks and assigning them to different nodes within the system.
There are several types of task scheduling algorithms used in distributed systems, including:
- Round Robin: Jobs are assigned to nodes in a cyclic order.
- Least Loaded: Jobs are assigned to nodes with the lowest workload.
- Priority: Jobs are assigned based on their priority level, with higher-priority jobs receiving preferential treatment.
- Fair Share: Nodes are assigned a fair share of jobs based on their processing power.
- Backfill: Jobs are filled in the gaps between higher-priority jobs, maximizing resource utilization.
- Deadline: Jobs are assigned deadlines, and the scheduler works to ensure they are completed before the deadline.
- Gang: Groups of related jobs are assigned to nodes simultaneously to reduce communication overhead.
Different algorithms may be more suitable for different workloads and system requirements.
Customized Enterprise Distributed Scheduling
Distributed enterprise scheduling platforms are becoming increasingly popular for managing jobs and workloads across on-premises and cloud environments.
They include integrations with companies like:
Some platforms offer REST API adapters that allow for seamless integration with virtually any tool or technology.
By utilizing an extensible platform, IT can achieve several benefits, including centralized monitoring and logging, faster roll-out, reduced human error, non-cluster failover to ensure workload completion in case of an outage, and more.
An Essential Part of Distributed Computing Systems
Organizations can manage and automate workloads across multiple machines by using distributed job schedulers, improving fault tolerance, load balancing, and scalability, among other benefits.
Distributed job scheduling also enables using different types of job scheduling algorithms, such as Round Robin, Least Loaded, and Fair Share, which can be more suitable for different workloads and system requirements.
As more organizations continue to bring their own servers, databases, and operating systems, distributed enterprise scheduling platforms are becoming increasingly popular for managing jobs and workloads across on-premises and cloud environments.