A workload, in the most general sense, is the amount of time and computing resources a system or network takes to complete a task or generate a particular output. It refers to the total system demand of all users and processes at a given moment.
Within IT, the term has continually evolved and become loaded with meaning, particularly with the rise of cloud computing. Broadly speaking, workload is used to refer to a computational task or process and the computing, storage, memory and network resources the task requires.
In a cloud computing context, workload refers to any service, application or capability that consumes cloud-based resources. In this cloud context, virtual machines, databases, applications, microservices, nodes and more are all considered workloads.
Workloads can range from simple tasks, like running a single app or computation, to complex operations, like processing large-scale data analytics or running a suite of interconnected apps. Managing workloads is a critical aspect of IT resource optimization, directly impacting system performance, cost, stability and ultimately, the success of business operations.
With the proliferation of cloud computing and virtualization, workload management has become increasingly complex1. The use of hybrid cloud, multicloud and public cloud resources means that workloads can span platforms and locations, each with unique characteristics and management requirements.
To navigate the complexities of managing workloads across computing environments and workflows, organizations are turning to advanced tools. They use tools like backend APIs, workload automation software, AI-based predictive analytics and cloud management platforms (for example, Amazon Web Services (AWS), Google Cloud Platform, IBM Cloud® and Microsoft Azure).
Companies are also adopting strategies like workload placement, where they determine the best location for each workload based on factors like cost, performance, lifecycle, compliance and business requirements. This approach ensures that each workload is running in an environment that is optimally suited to its specific needs.
Learn how both APM and ARM can enable faster decisions and resource application.
Register for the ebook on observability myths
The term “workload” is often used interchangeably with “application.” While both workloads and applications are interconnected and integral components of any IT infrastructure (and applications can be considered workloads), they serve rather different purposes.
Applications are programs, or groups of programs, designed to help end users perform specific tasks and meet certain business needs. Workloads refer to the processing demands of those tasks. In other words, workloads power applications (or parts thereof). However, it’s important to note that a workload isn’t necessarily exclusive to a single app. Many workloads perform tasks across applications.
Furthermore, application lifecycles tend to change when needs change or when more advanced technologies emerge. However, workloads change based on infrastructural factors, like system performance, user traffic, resource allocation and processing needs.
As stated, using workloads can be as simple as running a single app or as sophisticated as running an ecosystem of connected apps, with lots of variations in between. Therefore, successful workload deployment may require use of multiple types of workloads.
Some key types of workloads include:
Transactional workloads involve real-time user interaction, typically in the form of numerous short online transactions. Deploying transactional workloads requires systems that can handle multiple concurrent users and provide fast, consistent responses, so they’re commonly used for e-commerce sites to manage purchases, payments, product searches and more.
Batch workloads are non-interactive jobs that are processed in bulk, often sequentially. Because they require substantial processing power, batch workloads are common in environments that process high volumes of data, for example, payroll, billing and weather modeling. These jobs are often run during off-peak hours to prevent interference with interactive or transactional workloads. These workloads also tend to require parallel processing, wherein tasks are divided into smaller subtasks and executed simultaneously across multiple servers and processors.
Analytical workloads are characterized by complex queries running on large data sets. Unlike transactional workloads, which involve small, simple transactions, these workloads conduct in-depth data analyses—often using artificial intelligence and machine learning—to identify trends, relationships and insights. Because of their high data throughput, analytical workloads are commonly used for data warehousing and big data analytics.
Most enterprise applications rely on foundational databases to function. If a database is performing poorly, it’ll create bottlenecks for the apps that use it. Database workloads help address these issues. Database workloads are fine-tuned to accelerate and optimize search functionality for the other apps that depend on a database. They also allow teams to analyze metrics like memory and CPU usage, input/output (I/O) throughput and query execution rates.
HPC workloads run complex simulations and mathematical computations that require significant computing power. For example, a meteorological research team might run a simulation of El Niño-related climate patterns. Like batch workloads, HPC workloads tend to have high levels of parallelism.
When teams are in the software development and testing process, they will often rely on test and dev workloads, which handle tasks like compiling code, running unit tests and performing load tests. Much like the development process itself, test and dev workloads can be unpredictable and may require developers to quickly provision and de-provision resources as needs change.
These workloads are often critical in IT environments that require real-time, lightning-fast data processing to deliver immediate results, like stock trading apps, video streaming services and sports betting platforms.
IT environments have become increasingly complex, requiring tools and resources that can handle a diverse range of tasks, often simultaneously. This challenge is where hybrid workloads—which combine elements of different workload types—become a real asset.
An example of a hybrid workload is a real-time analytics application. The application processes incoming data with transactional workloads, runs complex queries on the data with analytical workloads, and generates reports using batch workloads.
The emergence of cloud computing over the last decade has driven the development of new workload types, including software-as-a-service (SaaS), containerized and microservices-based applications, virtual machines (VMs) and serverless computing. Enterprises are even exploring use cases for generative AI (gen AI) workloads.2 Regardless of type, workloads can also be categorized by their state (that is, stateful or stateless), and in the case of cloud workloads, by their usage patterns (that is, static or dynamic).
Stateful workloads need to retain information and status from one session to another, so they “remember” data from previous interactions. With a stateful application, if a user logs out and then logs back in, the app remembers their information and activity. Database systems, where data remains even after the session ends, are great examples of stateful workloads.
These workloads don’t save user data for the user’s next session. Each session is executed as a new interaction and responses are independent from previous user data. Stateless workloads can simplify app design, since developers don’t need to manage state information, but they can also make user experience personalization more complex.
Static workloads use a relatively constant amount of computing resources over long periods of time and on a consistent schedule.
Dynamic workloads also called temporary workloads, adjust and configure computing resources based on computing demand.
As the adoption of cloud computing grows, workload management practices shift in kind. Modern businesses typically use a combination of traditional on-premises data centers and cloud infrastructures to manage their workloads efficiently.
On-premises workloads run on an organization’s own hardware infrastructure, hosted locally in the organization's facilities. Public cloud-based workloads run on servers managed by a third-party cloud service provider (CSP) and are located off-site, often in multiple locations around the world. Both infrastructures offer advantages for managing enterprise workloads.
On-premises workloads provide:
With on-premises solutions, organizations have complete control over their workloads. This flexibility includes the ability to choose and customize all hardware and software, which is particularly beneficial to organizations with IT needs that aren’t easily met by standard cloud offerings.
On-premises solutions can offer greater security and compliance control, especially for organizations in industries or regions with stringent data sovereignty requirements and auditing processes. Because data is stored locally, rather than in a shared cloud environment, it's easier to enforce strict security protocols and control each team member’s data access.
Furthermore, some regulations require data to be stored within certain geographical boundaries, and companies can more easily guarantee storage compliance with on-premises workloads.
While the upfront costs of on-premises infrastructure can be quite steep, the ongoing costs of maintaining onsite workloads are relatively stable and easier to plan for. If an organization has the capital to invest and expects its needs to remain consistent over the long term, on-premises workloads can be a financially sound choice.
Sometimes, on-premises workloads perform better than cloud-based workloads. With on-premises infrastructure, data doesn't have to leave the local network, resulting in faster processing times and minimizing latency issues that can cause performance bottlenecks.
Onsite workloads are accessible even when internet connectivity is unstable or temporarily absent. Offline app availability can be a significant advantage for companies in regions with poor internet infrastructure, or for environments that require 24x7 app access.
Public cloud-based workloads, on the other hand, provide:
Cloud workloads typically follow an operational expenditure model, where users pay only for the resources they use, as they use them. This model can make cloud computing a more cost-effective entry point to workload management, especially for smaller businesses and startups.
Cloud providers have vast resources that can be allocated and de-allocated on demand, allowing organizations to easily scale workloads in response to shifting resource demand.
Though the organization is still responsible for managing and securing its own applications and data, cloud-based workloads put many maintenance tasks (for example, hardware repairs, software upgrades, security patching, etc.) in the provider’s hands.
Cloud services often include disaster recovery capabilities, as well as infrastructure redundancies, to maintain workload availability even when servers or data centers fail.
Cloud workloads can be executed and adjusted quickly, allowing for faster innovation and giving cloud-based businesses a competitive advantage. With cloud platforms, organizations can deploy new apps and services within minutes, while doing so on-premises might take weeks or months.
Many companies opt to use private clouds (also known as corporate clouds), which provide a combination of certain benefits offered by both on-premises and public cloud architectures.
A company can choose on-premises or public cloud-based workloads or a combination of the two. Using and managing these workloads effectively can improve organizational decision-making, as well as the overall efficiency, performance and cost-effectiveness of enterprise IT infrastructures.
The IBM Turbonomic® hybrid cloud cost optimization platform allows you to continuously automate critical actions in real-time that proactively deliver the most efficient use of compute, storage and network resources to your apps at every layer of the stack.
IBM Instana democratizes observability by providing a solution that anyone across DevOps, SRE, platform, ITOps and development can use to get the data they want with the context they need. Purpose-built for cloud-native yet technology-agnostic, the platform automatically and continuously provides high fidelity data—1 second granularity and end-to-end traces—with the context of logical and physical dependencies across mobile, web, applications and infrastructure.
IBM® Workload Automation is an automation platform integrating IT, operational and business workflows to optimize orchestration for the execution of unattended and event-driven processes built to operate in hybrid environments.
Leverage enterprise-wide workload automation for schedule-driven and event-driven workloads.
Understand the five principles that help the operations team think about the service management and operational facets of their application.
Learn about IT infrastructure which refers to the combined components of enterprise IT environments and services.
Review a sample architecture for IBM Cloud® Security and Compliance Center Workload Protection.
Optimize and automate complex workloads for greater IT efficiency.
Learn how teams can monitor and adjust workloads, and manage unresponsive flows and threads, with IBM Integration Bus for z/OS®.
Footnotes
1 Enterprises expect continued shift from legacy on-premises to mix of modern venues (link resides outside ibm.com), S&P Global Market Intelligence, 27 March 2023.
2 Market Analysis Perspective: Worldwide Enterprise Infrastructure Workloads 2023 (link resides outside ibm.com), IDC, September 2023.