Treat Data Centres like Elite Athletes – for the planet

Venessa Moffat

23 May 2023

Data centers are becoming increasingly pervasive in today’s digital age. As businesses and organisations rely more heavily on digital technologies, the demand for data center services is growing rapidly. Unfortunately, with this growth comes a collection of challenges, and there are opportunities for us as a sector to address these challenges better. We still do not look at the data centre as a whole, and there are teams within organisations that still don’t share information or even interact much with one another. This is holding us back from meeting sustainability goals and addressing the increasing challenges effectively. It’s not even a new problem – Ken Brill identified the issue in 2006 – nearly 20 years ago.

“When IT and Facility personnel work closely together to develop realistic strategies to accommodate increasing demands for power and cooling, information availability and uptime can improve significantly.” Ken Brill, Founder, The Uptime Institute, 2006

What if a data centre were an elite athlete?

Athletes often have a team of people looking after them, including coaches, trainers, sports psychologists, sports nutritionists, and medical professionals such as sports doctors and physical therapists. This team works together to support the athlete’s physical and mental well-being, optimise their performance, and help them achieve their goals. A wonderful example of systems thinking.

However, even with a team of experts looking after them, silo thinking can be a challenge. Each member of the team may have their own specialised area of expertise and focus on their own goals, which can lead to a lack of communication and collaboration between different departments. For example, the sports psychologist may be focused on improving the athlete’s mental resilience, while the nutritionist may be focused on optimising their diet for performance. But, without coordination between these two departments, the athlete may not receive the most effective care.

Similarly, with data centres, without the team working collaboratively, there may be ways to increase performance potential. With the increasing capacity being brought online around the world every quarter, we need collaboration in order to be able to address the challenges we face.

The challenges facing data centres today:

Energy consumption: Data centres consume significant energy to power and cool their equipment. The Paris Climate Agreement demands that we reduce net greenhouse gases by 55% by 2030. We have optimised the buildings to within an inch of their lives. It’s time to look at the IT stack, which is the only reason we have the building!
Cyber security: With the increasing amount of data being stored and processed in data centers, cybersecurity is a major concern. Data breaches and cyber attacks can result in significant financial, operational and reputational damage. We need a more holistic approach to security in our sector.
Regulatory compliance: Data centres must comply with a variety of regulations, such as data privacy laws and sustainability regulations. Compliance can be complex and costly, and failure to comply can result in legal and financial penalties or lack of sustainable investment. New IT metrics are part of CSRD reporting, and well as some other building metrics.
Immersion Cooling: the increasing numbers of HPC, Blockchain, and AI data centers bring the need for liquid cooling. This also allows the waste heat to be more usable. However, implementing this technology can be tricky in a traditionally air-cooled building.
Alternative Fuel Sources: with increased requirements from grid supplies, data centres are looking to alternative and reliable fuel sources. Battery technology is becoming more affordable, and hydrogen fuel cells are being investigated. Various designs are being explored to make the sector more sustainable against continued significant growth. Software defined power is being spoken of more too.

With all of the above challenges, there is an increased need to innovate and respond in a joined-up manner. As Ken Brill, the founder of the Uptime Institute, stated nearly 20 years ago that IT and Facilities need to be aligned, and it has never been more true than it is today.

Effective collaboration between IT and facilities teams is critical to tackling the sector’s challenges today. By working together, IT and facilities teams can improve uptime, reduce costs, and enhance the overall performance of the data center. This, in turn, can facilitate reduced energy consumption, increased cyber security, reduced costs at scale, and ease compliance with increasing reporting requirements. All this, as well as being better for the planet.

Treat data centres like elite athletes for the benefit of all of us.

If we treat our data centres like elite athletes and pool our expert resources, the planet and everything on it will be better off. Systems thinking is better than silos for all of us.

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I’d love to hear from companies where the silo problem doesn’t exist and how you are addressing the challenges of the sector, you can reach me at venessa.moffat@qio.io

DEUTSCHE

Die vierte industrielle Revolution, oder Industrie 4.0, ist gekennzeichnet durch das intelligente Vernetzen von Maschinen, Produkten und Mitarbeitern mit cyber-physikalischen Systemen in intelligenten Fabriken. Die Produktion in intelligenten Fabriken wird hochflexibel. Die Optimierung der Wertschöpfungskette erfolgt darin basierend auf Echtzeit-Anforderungen, Mitarbeiter werden von intelligenten Kontrollsystemen profitieren. In der intelligenten Fabrik wird die Kombination aus Material, Lieferanten, Ausrüstung und qualifizierten Mitarbeitern so organisiert, dass sie Einheiten produzieren, die den Kundenanforderungen in Echtzeit genügen. Die Personalisierung auf Kunden- und Marktsegmente wird durch integrierten Bedarfssignale gewährleistet, um skalierbare Geschäfte zu erzielen. Die intelligente Fabrik erfordert eine flexible und agile vernetzte Produktion.

Flexibilität bedeutet, dass die Betriebsmittel in der Lage sein müssen, eine Vielzahl unterschiedlicher Produktionsprozesse durchzuführen. Die Mitarbeiter müssen über ein sehr breites Spektrum an Qualifikationen und Erfahrungen im Umgang mit verschiedenen Systemen und Methoden verfügen.

Darüber hinaus erfordert die Flexibilität eine sehr hohe Verfügbarkeit der Einsatzmittel. Vernetzt bedeutet, dass alle relevanten Informationen für die Herstellung eines Produkts in Echtzeit zwischen allen Systemen und den am Prozess beteiligten Personen ausgetauscht werden. Tritt beispielsweise bei kritischen Anlagen ein Fehler auf, müssen neue Wege durch die Produktion gefunden werden, um Aufträge termingerecht zu erfüllen. Um das zu erzielen müssen alternative Arbeitspläne und die daraus resultierenden Produktionskosten und -zeiten analysiert werden. Neue Produktionseinheiten entstehen durch die neue Kombination von Ressourcen, Ausrüstung und Personal pro Auftrag. Dies erfordert eine enge Interaktion zwischen Ausrüstung, Ressourcen und Mitarbeitern mit einem intelligenten Produktionssystem, das sowohl die alternativen Routen als auch die Produktionskapazitäten verwaltet. Daher ist die Integration von Daten aus verschiedenen Systemen intern und extern unerlässlich.

Ziel dieses Forschungsprojekts ist es, Techniken zur Realisierung schlanker intelligenter Fabriken zu entwickeln, umzusetzen und zu validieren:

Das erste Projektziel ist die Entwicklung von neuen Algorithmen die den aktuellen Zustand der Fabrik bei der Erstellung des Produktionsplans berücksichtigen.

Das zweite Projektziel ist die Entwicklung einer Methodik für das Lösen von Systemen mit multiplen und teilweise probabilistischen Randbedingungen basierend auf reinforcement learning techniques.

Das dritte Projektziel ist es, eine mathematische Darstellung des genauen Zustands und Verhaltens der Produktionsanlagen zu generieren, die QiO den PARCS score nennt.

In diesem Arbeitspaket werden hochdimensionale teilweise diskrete Optimierungsprobleme mit einer Vielzahl von Nebenbedingungen mit modernen mathematischen Verfahren bearbeitet.

ENGLISH

The fourth industrial revolution, or Industry 4.0, is marked by the intelligent networking of machines, products and employees with cyber-physical systems in Smart Factories. Production in Smart Factories will be highly flexible, highly productive and resource efficient. The optimization of the value chain is based on real-time requirements. At the Smart Factory, the combination of materials, suppliers, equipment and skilled employees is organized to produce units that meet customer demands in real time. Personalization to customer and market segments is ensured by integrated demand signals to achieve scalable business.

The Smart Factory requires flexible and agile networked production. Flexibility means that the operating resources must be able to carry out a variety of different production processes. Employees are required to have a very broad range of qualifications and experience in using different systems and methods. Furthermore, flexibility requires a very high availability of operating resources.

Networked means that all the relevant information for the manufacture of a product is shared in real-time between all of the systems and people involved in the process. This requires information about potential machine breakdown, delays to a supplier order, missing parts, personnel availability or available skills.

If a fault occurs to critical piece of equipment, for instance, new routes through production must be found in order to fulfil orders on time. In order to do this, alternative work schedules and the resulting production costs and times must be analysed. New production units arise through the new combination of resources, equipment and personnel per order. This requires close interaction between equipment, resources and the workforce with a master intelligent production system, which manages the alternative routes as well as production capacity.

As such integration of data from diverse systems internal and external is therefore essential. The aim of this research project is to develop, implement and validate techniques for the realization of Smart Factories:

The first project objective is the development of new algorithms that take into account the current state of the factory when creating the production plan.

The second project objective is the development of a methodology for solving systems with multiple and partially probabilistic constraints based on reinforcement learning techniques.

The third goal of the project is to generate a mathematical representation of the exact condition and behaviour of the production facilities, which QiO calls the PARCS™ score. It is designed to enable engineers within software tools such as the QiO Foresight Engine™.

This work package deals with high-dimensional, partially discrete optimization problems with a variety of constraints using modern mathematical methods. The results of the project proposed here go well beyond the general state of the art, especially in the fields:

Artificial intelligence for the dynamic learning and modeling of production processes

Artificial intelligence for production planning and supply chain management

Traditional approaches to modeling and describing production processes assume a well-known problem and seek a solution to a specific problem. This approach is not suitable for solving such problems in the context of an intelligent factory. The boundary conditions that describe the processes change regularly here and they are mostly of a probabilistic nature. We plan to learn the processes dynamically based on the data produced by a production hall. We call such dat, the voice of the production hall.

Such automated learning is important because constructing a graphical model by a human expert can be cumbersome and time-consuming. If, as is often the case, there is a database of examples, learning techniques can do at least part of the work. This approach should lead to an always current and dynamic view on the production processes. It should also form a mathematical basis for dynamic production planning as it will be described in the next.