Creating a workload placement strategy for data centre performance

Modern hybrid cloud environments empower companies with almost endless computing options. But while they bring many advantages and possibilities, it’s also vital to optimise their performance. In 2023, ensuring optimal performance and ROI on cloud investment remains the top priority for 82 per cent of cloud-based businesses.

A key step in the optimisation process is a workload placement strategy. The term ‘workload’ refers to using computing resources to run a specific task or application. In modern cloud-based IT infrastructure, these workloads are often placed in colocation data centres or across a combination of private and multi-cloud services.

A workload placement strategy defines how, where and even when workloads are executed – ensuring minimal wasted resources and optimal performance levels. In this blog, we will explore what a well-devised workload placement strategy looks like. We’ll also see its effects on performance and look into key considerations when creating a data centre workload strategy.

Understanding Workload Placement Strategy

A workload placement strategy means allocating different workloads to the right resources within a data centre network. A strategy like this plays a major role in workload management, impacting operational efficiency and maximising quality of service.

Within a placement strategy, there are various possible objectives:

• Ensuring tasks are executed swiftly and effectively to optimise their performance for the end-user

• Matching workloads to the right resources, making maximal use of the available infrastructure

• Minimising wasted effort, needless provisioning and idle resources

• Distributing workloads in a way that achieves a balance of application performance versus total cost.

Creating an effective strategy can be challenging. There are many different workload characteristics, hardware capabilities and network service considerations, after all. For example, high-intensity workloads require suitable servers to run properly. Network factors — like latency and bandwidth needs — also impact workload placement decisions.

That’s why it's essential to take a holistic approach to designing a workload placement strategy. You’ll need to account for all relevant factors to achieve the best possible performance. The reward is a well-balanced strategy that improves overall workload performance.

Analysing Workload Characteristics

Before devising a workload placement strategy, it's crucial to analyse workload characteristics. These will help dictate decisions and, in turn, hugely affect performance.

Workloads have different needs, dependencies and make different demands of their cloud services and IT environment. For that reason, there are many factors that contribute to an overall strategy. For example:

• Compute-intensive workloads need significant CPU processing power

• Memory-intensive workloads require enough RAM or scalability options to run effectively

• Storage-intensive workloads must have access to considerable on-premises or public cloud disk space

• Latency-sensitive workloads require fast networking infrastructure or edge computing capabilities

• Mission-critical workloads may require significant disaster recovery and backup plans.

While these examples may be obvious, understanding them is an essential step for optimal workload placement. Data analysis can help you identify each workload’s needs. You might monitor key performance metrics and evaluate historical trends — both of which provide insight into workload behaviour.

Workload profiling is another key step. It involves studying workload attributes and patterns in cloud computing, including things like traffic analytics and peak usage periods. By developing a detailed profile of each workload, you can better anticipate needs. Then, you can optimise resources, achieve cost savings and prevent system overloads.

Assessing Data Centre Resources

Evaluating the available resources within a data centre is another essential step towards creating a workload placement strategy. Each of these factors influences how and where workloads can be placed:

• The available server capacity

• Network bandwidth and speeds

• The type and size of storage capacity

• Resource power consumption.

Understanding resource limitations — and any potential bottlenecks — is crucial. Identifying limitations means you can manage workloads to prevent system overloads or performance degradation.

Data Centre Infrastructure Management (DCIM) tools also support assessing and monitoring these resources. On-demand insights into the data centre's infrastructure can help track resource usage, predict potential issues and make data-driven workload placement decisions.

Defining Placement Policies and Criteria

It’s also vital to define workload placement policies and criteria. What are non-negotiables to your business? Such measures can include performance requirements and security demands. It’s also important to consider private cloud locality and data compliance requirements.

Aligning your digital transformation initiatives and computing needs with the cloud provider Service Level Agreements (SLAs) will ensure that the strategy supports your goals.

Remember that there might need to compromise. A strategy focused on high performance might bring higher costs, for example. Prioritising low latency might limit workload distribution options. Overall, balancing business needs against performance levels and cloud computing costs is the key to a successful strategy.

Implementing Placement Strategies and Automation

Implementing workload placement strategies involves a sequence of steps:

1. Initial assessment, investigating the needs of your workloads and business

2. Policy creation, defining how your strategy will work

3. Deployment, putting the plan into action

4. Continuous adjustment, refining the strategy to achieve the best performance and ROI.

Automation, AI algorithms and orchestration tools play a vital role in this process. They streamline tasks, give real-time insights and ensure efficient workload distribution across your ecosystem. By analysing your data, these tools can suggest ongoing optimal workload placement — keeping the strategy agile and effective.

Real-time monitoring and adjusting are key benefits of this dynamic approach. They let the system react to changing workload patterns and data centre conditions — maintaining high-quality service delivery at all times.

Summary

An effective workload placement strategy is essential to delivering key business metrics — from optimisation to cost reduction and efficient resource utilisation. By understanding your workloads, assessing resources, defining placement policies, and leveraging automation and AI, you can place your workloads in the most effective environment possible.

Embracing these strategies can transform your workload placement and achieve the highest ROI for your hybrid IT environment. Contact us or visit the Digital Realty resources library to learn more.

Frequently Asked Questions (FAQs)

What is a workload in a data centre?

In a data centre, a workload is the processing power a task needs to execute effectively. It can include everything from database reads and interconnection to running apps and services. Efficient workload management is crucial for optimal data centre performance and resource usage.

What is workload placement?

Workload placement means the strategic allocation of workloads within a data centre network. This strategy plays a critical role in achieving performance and cost-effectiveness by ensuring workloads are optimally placed within their IT environment. This helps increase operational efficiency, minimise costs and use resources effectively.

What is workload shifting?

Workload shifting refers to relocating workloads within a data centre or across multiple service providers. Typically, shifting will be done to optimise performance, manage resources properly and address changing workload demands. Reasons for shifting workload placement include energy costs or needs, consolidation, data storage or network bandwidth limitations.