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The Uptime Institute’s Global Data Center Survey 2022 suggests that over the next five years, data centers should play a role in energy conservation and carbon reduction, rather than being a hindrance to it.

With this in mind, Huawei plans to change the way people used to plan, build and operate data centers during the construction. Huawei’s Dongguan Cloud Data Center T1 project makes noticeable progress in energy conservation and carbon reduction. Put into operation in 2019, the project’s PUE reached 1.28 in 2021.

In Part 1 of this article, we have seen how digital technology enablement contributed to the success of the T1 project. Today, we look at other advanced energy-savings technologies which were employed.

AI-based PUE optimization
The T1 project employs an AI-based iCooling solution to decrease the energy consumption of the cooling system, which typically uses over 30% of a data center’s energy. This solution constantly monitors the energy consumption of all cooling subsystems and delivers the most efficient cooling strategy based on factors like current IT load and outdoor temperature.

Green construction and material recycling
The construction of traditional data centers often results in a high amount of waste and excessive water and power consumption. To address these issues, the T1 project employs a prefabricated modular solution where core components and systems are manufactured and pre-tested in a factory before being delivered and assembled on-site like LEGOs. This approach significantly reduces water usage and construction waste by 80%, reduces carbon emissions by 90%, and recycles up to 85% of materials, promoting eco-friendly construction and maximizing resource recovery.

Efficient and energy-saving devices
As data centers consume a large amount of energy, the use of energy-efficient and energy-saving devices is crucial to reducing energy consumption. The Huawei FusionPower6000 is utilized for power supply and distribution, providing a single source of power for each row, which boosts end-to-end efficiency to 95.5%. The cooling system is equipped with high-performance fans and wet film humidifiers to enhance energy efficiency. Additionally, smart modules are arranged in the equipment room to divide cold and hot aisles, optimizing heat exchange efficiency.

By using energy-saving technology, data centers can reduce their impact on the environment, contribute to a more sustainable future, and demonstrate their commitment to environmental responsibility.

Source: Huawei Digital Power

Huawei’s Dongguan Cloud Data Center T1 project (T1 project for short) is among the two green data centers in China to have been selected as a typical application case of key energy-saving technologies 2021 by the National Energy Conservation Center for the large amounts of energy it has saved during operation. Put into operation in 2019, the project’s PUE reached 1.28 in 2021.

Today, we look at how digital technology enablement contributed to the success of the T1 project.

Due to insufficient simulation of air ducts and airflows at the design phase and backward onsite assembly and construction, traditional data centers have a higher PUE than the designed one. By contrast, the T1 project uses digital design, simulation, and manufacturing, reaching a designed PUE in actual operation.

Next week, we will look at other advanced energy-savings technology which was employed in the T1 project.

Source: Huawei Digital Power

Data centers consume a great deal of energy and resources, and they are one place that many organizations seek to cut back – with the goal of eventually reaching net-zero carbon emissions. Globally, leading data center operators have committed to carbon neutrality and science-based targets for emissions reduction by 2030. The challenge is how to achieve this. Huawei has outlined four core trends for the future direction of data centers.

Solving the challenge of growing demand for data with low-carbon solutions, Huawei’s next-gen simplified, green, intelligent, and secure data center infrastructure solution is only one of the solutions. Reducing carbon emissions is essential for the industry and an area in which innovation focuses on converging software and hardware as well as cross-product-domain collaboration. With so many technical innovations, expertise and best practice at our disposal, the journey to zero-carbon data center is now achievable.

After-sales maintenance helps ensure that a customer gets as much use and value as possible out of their purchase. A great product should be backed up with excellent maintenance support as this provides a positive experience to customers.

The business reasoning behind after-sales support is that it can contribute to brand loyalty and positive word-of-mouth. An example of a brand which understands this reasoning is Huawei.

Huawei Hi-Care services provide a variety of maintenance services for customers. Huawei Hi-Care provides industry-leading Technical Support Centers (TAC), global service organizations, hourly spare parts supply capabilities, standard Issue to Resolution (ITR) processes, and advanced IT tools to respond to customer service requests in a timely and efficient manner.

Huawei has always been committed to providing customers with more professional and intelligent maintenance services. Huawei Hi-Care services go beyond traditional phone or email support as customers can seek help from multiple channels such as the intelligent Q&A chatbot and the technical support community. In addition, customers can use the intelligent maintenance center ServiceCare to manage inventory and service requests, and quickly obtain product technical information and service reports.

At GreenBay, we believe that an effective maintenance strategy is concerned with maximising equipment uptime and facility performance while balancing the associated resources expended and ultimately the cost. When a brand offers maintenance services (such as Huawei Hi-Care) which can assist in getting sufficient return on your asset investment, it is an indication that your maintenance strategy is on the correct trajectory to be successful and fuss-free.

A data center’s power usage effectiveness or PUE, is seen as a key indicator for evaluating its energy efficiency. The PUE value rises as the data center becomes less efficient. Cooling, a key component of a data center, is one of the main factors which can address the issue of energy efficiency.

Cooling is closely related to equipment heat dissipation, equipment configuration, facility environment, and external climate conditions. After Operations and Management (O&M) reaches a certain maturity level, hardware energy saving or human experience-based simple optimization cannot further reduce power consumption.

Therefore, Huawei has leveraged on machine learning to develop its iCooling intelligent thermal management solution for data centers. The iCooling solution incorporates deep learning to analyse historical data and identify key factors which affect energy consumption and create a PUE prediction. An optimisation algorithm then establishes the ideal parameters which are transmitted to various control systems.

The key technologies used in the iCooling solution include:

• Big data collection
  Given the complexity of data center cooling systems, information about the power supply system, cooling system, and environment parameters must be collected.
• Data governance and feature engineering
  First, a mathematical tool is used to perform data governance on the raw data collected, providing high-quality data for subsequent model training. Second, feature engineering is performed on large amounts of raw data to identify the key parameters that affect PUE.

Creating a PUE model using a neural network:
Neural networks are a set of machine learning algorithms that can simulate the cognitive behavior of interactions between neurons. Deep neural networks can play a role in increasing the cooling efficiency of data centers. The machine learning algorithms of these networks can find the relationships between parameters of different pieces of equipment and systems. A mathematical model or the PUE model of the data center is created based on large amounts of data from sensors.

Inference and decision-making using genetic algorithms:
Based on the input PUE model and the operating data collected in real time, the algorithms find the best policy in four steps: parameter traversal and combination, service rule assurance, calculating the energy consumption of the cooling system, and selecting the optimal policy.

At Huawei’s cloud data center Langfang in China, the deployment of iCooling has resulted in a PUE that is eight percent lower than it previously was, which translates to major savings in annual power costs. At a China Mobile data center in Ningxia, the introduction of iCooling technology has reduced the data center’s total energy consumption by 3.2%, saving more than 400,000 kWh of electricity a year. According to Huawei, as data centre loads increase and AI learning capability improves, six million kWh of electricity will be saved in the data center every year, the equivalent to a reduction of about three million kilogrammes of carbon dioxide emissions.

With extensive experience and technical expertise in global data center construction, Huawei has proven that the power of AI and big data is the key to create greener data centers.

Source: Huawei