‘Workload’ is a term that comes up often in computing. At its most basic, a workloads describes any job, task, program, or application you could run on a computer. A single workload might have a relatively low impact on system performance, but once many are running, enough resources must be dedicated to the right tasks. This is known as workload management.

You might hear the phrase workload management used in a few different contexts:

In data centers, workload management refers to the allocation of computing resources to maximize hardware, network and application performance and provide the best customer experience
In general computing, workload management refers to allocating computing resources to particular tasks.
In the workforce, workload management describes balancing jobs among employees to make sure everything gets done — and that no worker or department is overwhelmed

While all follow similar themes — using the available resources effectively and efficiently — this blog will specifically explore workload management in data centers. We’ll see how workload management is crucial for ensuring that data center IT infrastructure can handle the demands of modern applications, high-performance workloads, and big data analysis

What Does Workload Management Involve?

Many different tasks fall under the umbrella of data center workload management:

Upgrading hardware, software and operating systems: Ensuring the infrastructure can handle the growing demands of modern workloads.
Data distribution and storage management: Properly storing and distributing data to achieve maximum performance.
Backup processes: Making sure that plans are in place to recover data when needed.
Emergency planning: Understanding and — crucially — anticipating any emergencies that could occur. Then, making plans for business continuity should they happen.
Technical support: Resolving any issue as quickly as possible.

Workload alignment is another essential part of workload management. It involves analyzing the security, scale and platform needs of the applications and data, giving businesses the knowledge to optimize their resources. Done well, this can lead to cost savings, improved performance and easier future digital transformation.

Why is Workload Management Important?

Proper workload management in data centers helps businesses improve IT efficiency, reduce costs, and enhance system performance.

Improved Scalability
Balancing performance needs leads to more efficient use of computing resources. This can help businesses and data centers provision advanced technologies that rely on huge computing resources or power consumption, such as artificial intelligence (AI) and machine learning. It also allows the efficient scaling of resources when needed.

Effective Workload Placement
One key aspect of data center workload management is optimizing workload placement. It ensures deployments are in the most appropriate location, based on resource requirements. This strategy can improve key performance metrics, reduce latency, and avoid resource scarcity.

Efficient Resource Usage
Another critical aspect of workload management is choosing the appropriate resources for each workload. This deals with factors like CPU, memory and storage allocation, along with network bandwidth, to ensure that each application receives the power and connectivity it needs.

Reduced Costs and Waste
It’s estimated that one-third of cloud services spend is wasted. Whether on-premise, hybrid-cloud or multi-cloud, effective workload management can help reduce operating costs by minimizing idle resources and energy consumption — all while getting the most out of provisioned resources. Real-time monitoring of workload performance can also help streamline workload distribution in the future.

Effective workload management is essential for achieving the constant trade-off between performance, cost and availability. With the right workload management strategy, data centers can help you meet business needs, reduce downtime and maintain high performance.

Common Data Workload Issues

When optimizing IT infrastructure, it’s essential to understand some of the common issues many businesses face. Some typical hurdles can include the following:

Workload overload: When too many workloads run on a system, users see performance issues and slower processing times. It's vital to manage workloads properly, preventing resource overload and ensuring optimal performance
Lack of visibility: Workloads can be complex and difficult to manage. That’s especially true of today’s big data workloads. Without proper visibility, it can be tricky to optimize performance and troubleshoot problems when dealing with vast resource demands
A high volume of workloads: As businesses scale, they inevitably manage more workloads. This can create various challenges for IT teams — especially in making sure that all workloads are correctly managed, located and optimized
Dealing with changes: Business needs change rapidly. That means workloads can be dynamic, changing along with the company. To address this challenge, there must be systems in place to process changes and tweak performance when necessary

Static vs Dynamic Workload Management

As well as considering common workload issues, it's also important to look at your specific needs. Initially, look at the difference between your static and dynamic workload requirements.

Static workloads are generally more predictable. They’re less demanding and have relatively fast loading times — think software that is constantly running but doesn’t do much resource-intensive work. Static workload management is relatively simple thanks to its predictable demands, allowing IT teams to dedicate resources and not worry about regular maintenance.

Dynamic workloads, on the other hand, are more complex and need more resources. Examples include large, complex tasks that require a lot of processing power — but that might only run occasionally. Dynamic workload management requires systems and IT staff to be more versatile, allocating resources to handle unpredictable spikes in demands.

When planning your workload management, it helps to look at your common workloads. Are they mostly static and predictable? Or are there more dynamic needs? Understanding your everyday workload demands can help save costs, avoid over-provisioning and help systems run smoothly.

How To Reduce Your Data Center Workload?

Today’s public and private cloud workloads can combine into complex hybrid cloud environments. Without proper visibility, it’s easy to duplicate and over-provision IT infrastructure.
Thankfully, Digital Realty’s data center solutions offer several ways to reduce your data center workload. You can also adjust current IT methods to improve workload performance. Here are just some of the possible strategies:

Optimize existing IT infrastructure: Working with an experienced data center provider can help make sure your IT infrastructure is properly configured for your workloads. This can include upgrading hardware, optimizing network services and using the latest software
Use AI and monitoring software: Implementing AI in your IT infrastructure can help automate tasks, reduce manual work and improve performance. AI algorithms can identify and address potential issues before they become bigger problems — or even manage workload management for you. Monitoring software can also show you trends in workload performance over time
Restrict access to workloads: Another strategy is to limit workload access on a need-to-know basis. Review your current settings to make sure only those who require specific workloads have access — this can automatically reduce the strain on your system
Empower employees: Provide training on how to properly manage and maintain your data center workload. You might cover best practices, identifying common issues, or how to optimize performance

By implementing these plans, you can reduce and simplify your data center workload. In many cases, it helps to work with a trusted partner who can help you implement these strategies and maximize performance.

Data Workload Management Solutions & Tools

For effective data workload management, it’s important to use the right tools. Here are some examples:

Time management tools: These solutions help things run smoothly by scheduling common tasks. This ensures that efficient resource utilization and necessary jobs happen on time. They might include automation software, routine task schedulers and data processing, for example
Cloud platforms: Cloud computing brings scalable and flexible resources that can be used to manage your workloads. They provide on-demand resources that can be quickly provisioned for load-balancing spikes in traffic, allowing for automatic workload management
Performance monitoring: These tools help data centers monitor the performance of workloads — and identify potential issues. They can observe performance at the network, application and system levels, giving visibility into workload behaviors

Proper workload management is essential for reaching peak performance. It involves effectively allocating computing resources and performing various tasks such as upgrading hardware, managing data distribution, and preparing emergency plans.

But workload management can be a challenge. Common issues include workload overload and lack of visibility. Businesses can mitigate these problems by streamlining IT infrastructure, using performance monitoring software and working with experienced partners.

At Digital Realty, we help you implement effective workload management strategies that improve system performance. Our data centers offer the latest networking, monitoring and cloud computing technology — giving your workloads the resources they need and ensuring return on investment for your IT spend. Contact us today to learn more.

Frequently Asked Questions (FAQs)

Why is workload management important?

Workload management is important for businesses to optimize their IT infrastructure. Doing so can result in improved performance, reduced latency and less resource contention, for example. Workload management is especially useful in data centers, helping businesses achieve proper workload balancing, resource allocation and return on investment.

What is the workload of a data center?
Data center workloads describe the IT infrastructure behind the day-to-day running and services offered. The workload of a data center involves tasks like:

Data processing
Storage of data
Running servers, which can vary depending on the size, complexity and number of users

Proper workload management is important to ensure a high-quality service.

What is Data Center Management?
Data center management is the general overseeing of processes, services and applications related to data center service providers. It may involve tasks such as:

Managing and maintaining equipment
Updating software
Storing data
Ensuring proper maintenance, support and security for clients

The aim of data center management is to ensure the optimal functioning, security and reliability of its services and infrastructure.

How is AI helping to manage workloads in data center?
AI can now support data center providers and clients by automating workload management. It will monitor workloads and hardware performance, allocating tasks to the most efficient infrastructure. AI software can ensure optimal performance in near real-time by continuously tracking things like traffic, workloads, bandwidth and temperatures throughout the facility.

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