What Is a Workload?

What is a workload?

In computing, a workload refers to the tasks, processes, or jobs a system, service, or application executes. Workloads are not just about applications; they represent the activity or demand on the underlying infrastructure.

In the rest of this resource, we’ll explore the difference between workloads and applications, common types of workloads, and how to manage, automate, and protect workloads in modern IT environments. We’ll also highlight real-world examples to make these concepts concrete. Continue reading to learn more.
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• Workloads vs. applications
  
• Types of workloads
  
• Examples of workloads
  
• Workload management
• Workload protection
• Key takeaways and additional resources
  

Workloads vs. applications

At first glance, workloads and applications may seem interchangeable, but they serve distinct purposes in system design. An application refers to the software itself, whether it’s a web app, mobile app, or desktop software. Workload, however, is what the application demands from the underlying system, such as central processing units (CPUs), memory, or disk space.

Think of an e-commerce application. The app’s workload is determined by the number of transactions (or orders) processed per minute, concurrent users, and the backend processes handling inventory updates, user sessions, or recommendation engines. The app is static in concept, but its workload fluctuates based on demand.

Understanding this difference is critical when designing systems, especially for scalability and performance. While the application may be built with a certain number of features, its workload will change as user activity increases, new features roll out, and infrastructure evolves.

Types of workloads

Here are some common types:

1. Transactional workloads: Refers to systems that process high volumes of transactions, such as databases or online payment platforms. These workloads are characterized by their low latency requirements and high reliability.

2. Batch workloads: These workloads execute a series of jobs that can be processed in batches. Data analytics pipelines, nightly reporting, and extract, transform, load (ETL) jobs are good examples of batch workloads.

3. Interactive workloads: These workloads involve real-time activity, like interaction with end users, in both web and mobile apps. They demand quick response times to user inputs.

4. Compute-intensive workloads: Refers to applications that require significant processing power, such as machine learning model training, video rendering, or simulations.

5. Data-intensive workloads: Systems that must process, store, and retrieve massive amounts of data, such as large-scale databases or big data platforms.

Examples of workloads

• E-commerce websites: This workload involves user requests for products, database queries to fetch inventory details, processing of payment transactions, and shipping updates.
• Machine learning model training: Requires intensive CPU or graphics processing unit (GPU) resources to train models on large datasets. This is often categorized as a compute-intensive workload.
• Streaming services: Platforms like Netflix handle interactive workloads where video is streamed on demand to users. This involves a data-intensive backend storing large multimedia files.
• Real-time fraud detection: A common use case for a financial application involves analyzing real-time transactions to detect potential fraud – this streaming workload processes thousands of transactions per second during peak times.

Workload management

Workload management involves distributing tasks within an available resource system, such as a CPU, memory, and storage, to ensure efficient performance. Proper workload management is crucial to applications running efficiently and effectively.

Tools like OpenPBS and Slurm allow you to manage the workload in a cluster environment because they schedule tasks and assign resources dynamically within different nodes. These tools optimize hardware resources by scheduling workloads so that high-priority jobs receive resources and less important tasks can be delayed or queued until resources become available.

Workload automation

Scalable workload management requires automation. Workload automation refers to the process of automatically scheduling, running, and managing tasks inside an application or system. Due to its scalability, it supports resource optimization, minimizes hands-on effort, and delivers consistent performance, particularly in dynamic environments.

Key concepts in workload automation

• Task scheduling: OS-level automation often involves scheduling tasks. Once configured with rules, such as specific times, events, or system thresholds, it can invoke tasks automatically. Examples of simple OS-level automation include cron jobs in Linux or a task scheduler on Windows.
• Resource scaling: Automation platforms like Kubernetes scale resources dynamically because of real-time demand. For instance, if there’s a spike in users on a web application, Kubernetes will automatically roll out new instances (containers) to balance the load and distribute it evenly.
• Error handling: Automated systems can handle task failures. Once a task fails, automation tools can retry, log an error, or cause an alert for manual input. That way, downtime is minimized and continues uninterrupted while the system does what it should.
• Dependency management: Because workload automation allows you to specify which tasks depend on what, it means you can ensure processes are executed in the proper sequence. Consider a data processing pipeline where ETL jobs have to run in order – extract must precede transformation and loading.

Tools for workload automation

1. Kubernetes: The leading platform for managing containerized workloads, Kubernetes automates the deployment, scaling, and management of containerized applications. It uses controllers to monitor the system’s state and adjusts resources to match demand.

2. AWS (Amazon Web Services) Lambda: Serverless platforms like AWS Lambda allow you to run code in response to events without provisioning or managing servers. This automation model enables workloads to scale automatically and efficiently in response to user interactions, scheduled tasks, or other triggers.

3. Apache Airflow: Airflow is a popular open source platform for programmatic authoring, scheduling, and monitoring workflows. It’s especially useful for batch workloads where tasks must run in sequence or at specific times (e.g., data pipelines and ETL jobs).

4. Terraform: Terraform can automate workloads by provisioning and managing infrastructure as code (IaC). It can integrate with platforms like AWS and Google Cloud to automate scaling policies, provision servers, and manage complex infrastructure workflows.

Workload protection

With growing complexity, workloads are increasingly vulnerable to security threats, which makes workload protection vital. Workload protection involves securing applications, their data, and the infrastructure that supports them.

A common strategy involves using zero trust security models, where every entity interacting with a workload (whether user or application) is authenticated and authorized before accessing resources. Tools like AWS Shield can help protect workloads from distributed denial of service (DDoS) attacks, while container security solutions like Falcon monitor Kubernetes workloads for anomalies.

Cloud workload protection (CWP) involves continuously monitoring and removing threats from cloud workloads and containers. A CWP platform (CWPP) is a security solution that protects workloads of all types in any location, offering unified cloud workload protection across multiple providers. Cloud providers usually offer native workload protection services, such as data encryption at rest and in transit, identity and access management (IAM), and network isolation through virtual private clouds (VPCs).

Key takeaways and additional resources

• Workload: Refers to the demand placed on a system, whereas an application is software.
• Types of workloads: Range from transactional and interactive to batch, compute-intensive, and data-intensive. Each workload should be handled according to the demands placed on it.
• Workload management: The efficient management of workloads is often automated. The aim is to utilize resources based on the system’s demands.
• Protecting the workload: Protection from threats to the workload is important, and cloud-native tools and security models help cater to this.

We also leave you with some adjacent resources to help your business build and manage applications at scale.

• High Availability Architecture: Requirements & Best Practices
• What Is a Distributed Application? Definition and Examples

You can visit our blog and concepts hub to learn more about workload-related concepts from Couchbase.