What Is Data Center Energy Consumption? 

Data center energy consumption refers to the total amount of electrical energy used by data centers. These facilities house servers, networking devices, and storage systems, which are crucial for the operation of online services and applications. The energy consumed is significant, as data centers must operate continuously to ensure service availability and reliability. Maintaining these operations involves not only powering the equipment but also managing the cooling systems required to dissipate the heat generated by running hardware.

Continuous monitoring and management of energy consumption in data centers are vital for reducing operational costs and minimizing environmental impact. Excessive energy use in data centers can lead to higher greenhouse gas emissions, which contribute to global climate change. Companies are increasingly focusing on improving the energy efficiency of their data centers to mitigate these issues.

This is part of a series of articles about data center costs.

In this article:

• Statistics of Data Center Energy Consumption
• Drivers of Data Center Energy Consumption
• How to Measure Data Center Energy Consumption
• How to Improve Data Center Energy Consumption Efficiency
• Optimizing Data Center Energy Usage with Intel® Tiber™ App-Level Optimization

Statistics of Data Center Energy Consumption

According to the International Energy Agency, data centers consumed 460 terawatt-hours (TWh) of electricity in 2022, representing 2% of global electricity usage. This consumption is projected to increase significantly due to the rising demands of power-intensive workloads such as artificial intelligence (AI) and cryptocurrency mining. By 2026, data center energy consumption could rise to between 650TWh and 1,050TWh. This potential increase is equivalent to adding the entire power consumption of a country like Sweden at the lower end, or Germany at the higher end of the scale.

In the United States, data centers accounted for 200TWh of electricity use in 2022, about 6% of the country’s total power consumption. This figure is expected to grow to 260TWh by 2026. The situation is more pronounced in Ireland, where data centers could account for 32% of the nation’s power consumption by 2026, up from 17% in 2022.

Drivers of Data Center Energy Consumption

Servers

Servers are the primary consumers of energy within data centers. These machines process and store vast amounts of data, requiring substantial electrical power to operate. The energy consumed by servers is directly proportional to their workload; high-performance computing tasks significantly increase power usage. As such, managing server efficiency involves optimizing workloads and ensuring task distribution to avoid overloading individual units.

Energy-efficient server design focuses on using low-power components and power management techniques. Modern servers are equipped with dynamic voltage and frequency scaling (DVFS) and power capping features to reduce energy consumption during low-usage periods. By implementing server virtualization, organizations can further decrease the number of physical servers required, thereby reducing overall energy usage and operational costs.

Cooling Systems

Cooling systems are essential for maintaining the optimal operating temperature of data center equipment. These systems consume a significant portion of the total energy used by data centers. Traditional cooling methods, such as air conditioning units, are often energy-intensive. Inefficient cooling systems can lead to high operational costs and increased energy consumption.

Modern data centers employ cooling solutions like liquid cooling, which uses chilled liquid to transfer heat away from equipment more efficiently than air cooling. Innovations like aisle containment and free cooling also contribute to reducing energy consumption. By isolating hot and cold air streams within the data center, these methods prevent the mixing of conditioned and heated air, improving cooling efficiency and reducing the energy required for temperature control.

Network Devices

Network devices, including routers and switches, are critical for data center operations. These devices facilitate data communication between servers, storage systems, and external networks. Although individually network devices may consume less power than servers, their cumulative energy usage is significant due to their continuous operation.

Energy-efficient network device design focuses on minimizing power usage without compromising performance. Power over Ethernet (PoE) technology and sleep mode features help reduce the energy consumption of network devices during idle periods. Additionally, optimizing network configurations and consolidating network paths can lower the overall power demand, contributing to more efficient energy usage.

Storage Drives

Storage drives, both traditional hard disk drives (HDDs) and solid-state drives (SSDs), play a crucial role in data centers. These devices store large amounts of data and are continuously accessed to retrieve and write information. Storage drives’ energy consumption varies depending on their type and access patterns; SSDs, on average, consume less power than HDDs while providing faster data access.

Reducing the energy consumption of storage drives involves optimizing data management strategies. Techniques like data deduplication and compression reduce the amount of storage space required, which can minimize the number of active drives. Implementing tiered storage, where frequently accessed data is stored on energy-efficient SSDs and less frequently accessed data on HDDs, can optimize both performance and energy usage.

Tips from the expert: In my experience, here are tips that can help you better manage and optimize data center energy consumption: 1. Leverage renewable energy sources: Integrating renewable energy sources, like solar or wind power, into your data center’s energy supply can significantly reduce reliance on fossil fuels, lower electricity costs, and enhance sustainability. 2. Implement AI-driven energy management: Utilize AI and machine learning algorithms to predict workloads and dynamically adjust power and cooling resources. AI can optimize energy usage by learning usage patterns and preemptively scaling power and cooling to match demand, further enhancing efficiency. 3. Adopt liquid immersion cooling: Beyond liquid cooling, liquid immersion cooling involves submerging servers in non-conductive cooling fluids. This method can drastically reduce energy needed for cooling and allows for higher-density configurations while maintaining optimal temperature levels. 4. Optimize power distribution units (PDUs): Upgrade to intelligent PDUs that provide real-time power monitoring, outlet control, and environmental sensors. These PDUs can help optimize power usage at the rack level and prevent energy waste by shutting off unused outlets and balancing loads. 5. Encourage energy-aware software development Foster a culture of energy-aware programming within your development teams. This involves creating software that minimizes CPU cycles, reduces memory usage, and efficiently manages I/O operations. Optimized code can lower server load, reducing energy consumption.

How to Measure Data Center Energy Consumption

Power Usage Effectiveness (PUE)

Power usage effectiveness (PUE) is a widely used metric for measuring data center energy efficiency. PUE is defined as the ratio of total facility energy consumed to the energy consumed by IT equipment alone. A lower PUE value indicates higher efficiency, with the ideal PUE being 1.0, where all consumed energy is used solely by the IT equipment.

Calculating PUE involves monitoring the energy usage of the entire data center facility, including cooling systems, power delivery infrastructure, and IT equipment. Regularly tracking PUE helps identify inefficiencies and areas for improvement. Many data centers aim for a PUE of below 1.5, reflecting optimized energy use for both IT operations and supporting systems.

Data Center Infrastructure Efficiency (DCIE)

Data center infrastructure efficiency (DCIE) is the inverse of PUE and is another metric used to gauge data center energy performance. DCIE is calculated as the percentage of energy used by IT equipment relative to the total energy consumed by the facility. A higher DCIE value indicates better energy efficiency.

DCIE offers a different perspective on data center energy consumption, highlighting the proportion of energy that directly supports IT operations. Improving DCIE involves implementing energy-efficient technologies and optimizing infrastructure management. Regular assessment of DCIE can guide strategies for reducing overall energy consumption and improving the sustainability of data center operations.

Corporate Average Data Center Efficiency (CADE)

Corporate average data center efficiency (CADE) is a metric for organizations that operate multiple data centers. It measures the average energy efficiency across all data center facilities, providing a broader perspective on corporate energy performance. CADE is calculated by averaging the DCIE or PUE values of all data centers within the organization.

Tracking CADE helps organizations benchmark energy efficiency across different facilities and identify best practices that can be implemented company-wide. By focusing on improving CADE, organizations can achieve significant energy savings and enhance their overall sustainability efforts. This metric encourages a holistic approach to energy management, considering the collective performance of all data centers.

How to Improve Data Center Energy Consumption Efficiency

Optimize Your Cooling System

Optimizing the cooling system is one of the most effective ways to improve data center energy efficiency. Techniques such as airflow management, which includes hot and cold aisle containment, can significantly enhance cooling effectiveness. By preventing the mixing of hot and cool air, these strategies reduce the workload on cooling systems, thereby cutting energy consumption.

Implementing advanced cooling technologies, such as liquid cooling or free cooling, can also contribute to energy savings. Liquid cooling systems provide efficient heat dissipation and are more effective than traditional air cooling methods. Free cooling takes advantage of natural cool air from the environment, reducing the dependence on artificial cooling systems. Adopting these technologies can result in substantial energy savings and lower operational costs.

Use Server Virtualization

Server virtualization involves running multiple virtual servers on a single physical server, improving hardware utilization and reducing energy consumption. By consolidating workloads onto fewer physical servers, organizations can significantly decrease the number of servers needed, leading to lower energy usage.

Virtualization technology also enhances flexibility and scalability, allowing for dynamic resource allocation based on demand. This adaptability ensures that servers operate efficiently, minimizing idle periods and optimizing energy use. With fewer physical servers, data centers can also reduce the cooling and space requirements, further enhancing energy efficiency.

Increase Temperature Set Points

Raising the temperature set points in data centers can lead to significant energy savings. Modern IT equipment is designed to operate efficiently at higher temperatures than traditional settings. Increasing the set points by a few degrees can reduce the workload on cooling systems, thereby decreasing energy consumption without compromising equipment performance.

It’s essential to monitor the data center environment closely when adjusting temperature set points to ensure that equipment operates within safe parameters. Implementing advanced environmental monitoring systems can provide real-time data on temperature and humidity levels, enabling precise control and optimization of cooling strategies.

Turn Off Idle IT Equipment

Turning off idle IT equipment is a straightforward yet effective strategy for reducing data center energy consumption. Many servers and storage devices remain powered on even when not in active use, leading to unnecessary energy waste. Implementing policies and technologies that automatically shut down or put idle equipment into low-power states can yield significant energy savings.

Automated power management tools can help identify and manage idle equipment, ensuring that power is used efficiently. Regular audits and monitoring of equipment usage patterns can also assist in optimizing power management strategies, reducing energy waste, and lowering operational costs.

Use Data Center Infrastructure Management (DCIM) Software

Data center infrastructure management (DCIM) software provides tools for monitoring, managing, and optimizing data center resources. DCIM solutions offer real-time insights into energy usage, environmental conditions, and equipment performance, enabling better decision-making and efficient resource utilization.

DCIM software can help identify inefficiencies, track energy consumption trends, and implement corrective actions to improve overall energy efficiency. By integrating DCIM into data center operations, organizations can achieve better visibility, control, and optimization of their infrastructure, leading to sustainable energy consumption and reduced operational costs.

Optimize at the Application Level

Optimizing energy consumption at the application level involves tailoring software to use hardware resources more efficiently. One effective method is to optimize code to reduce computational requirements, which can lead to less energy usage. Efficient coding practices, such as algorithm optimization, parallel processing, and minimizing redundant processes, can significantly cut down on the workload and power consumption of servers.

Load balancing and dynamic resource allocation also play crucial roles. By distributing workloads evenly across servers and reallocating resources based on real-time demand, data centers can avoid overloading specific servers and underutilizing others. This ensures that hardware operates at optimal efficiency, reducing energy waste.

Application-level optimization can also include the use of containerization and microservices. These technologies allow for more granular control over resource usage, enabling applications to scale efficiently and only use necessary resources. Containers, in particular, offer lightweight alternatives to virtual machines, reducing the overhead and energy consumption associated with running multiple operating systems.

Related content: Read our guide to data center optimization

Optimizing Data Center Energy Usage with Intel® Tiber™ App-Level Optimization

Intel® Tiber™ App-Level Optimization offers a powerful solution for reducing the carbon footprint of cloud operations. By optimizing cloud workloads to enhance performance, businesses can significantly reduce resource waste and lower their environmental impact. This innovative approach not only helps companies contribute to a sustainable future but also aligns their operations with global environmental goals.

The App-Level Optimization technology continuously adapts to application workload patterns, ensuring optimal performance while minimizing energy consumption. With the integration of the CO2 Saving Meter into App-Level Optimization’s dashboard, companies can monitor their carbon emission reductions in real-time, gaining insights into the positive environmental impact of their optimization efforts. Supported by climatiq’s carbon footprint measurement calculations, this tool empowers businesses to track and reduce their carbon footprint alongside cost and resource reductions, making a tangible difference in their sustainability journey.