Data Centers

Tech emissions are growing at an estimated 6% year on year, and globally, data centers now consume around 3% of the world’s electricity and produce about 2% of total GHG emissions.

In recent years, there have been substantial efficiency improvements in data centers, especially with regard to the building. Yet, at the same time, the industry faces ever-increasing demand for the computing power it provides – making the journey to net zero significantly challenging.

As data volumes continue to grow 30% per year, the Paris Agreement targets for the data centre industry still require significant emissions reductions of about 53% by 2030.
Despite substantial investments in renewable energy, the variability of wind and solar sources may not match a data centre’s demand profile, and renewable energy power purchase agreements might even be for a different grid or region.
The cost of carbon offsetting is rising, and whilst renewables have their place, consumption reduction and optimisation should be the priority.
PUE is not enough, and the new ISO30134 metrics are a welcome addition.
CSRD reporting will soon be required for companies of a certain profile.

The complexity of digital has presented challenges to carbon accounting, but the availability of data also offers opportunities that are not available to more traditional industries.

QiO’s Foresight Optima is uniquely placed to help data center operators on their sustainability journey with continuous efficiency improvements in energy and water consumption at multiple levels. Particular focus must be directed towards the IT stack over the coming years.

CSRD / EU Taxonomy reporting dashboard, tracking data continuously.

Monitor patterns in server power usage to automatically adjust power consumption to better match workloads while maintaining quality of service.

Extra insight into the IT estate enables reduction of the IT load energy consumption by up to 52%.

Features

Foresight Optima provides a suite of ready-to-deploy features with integrated algorithms that integrate with existing data centre infrastructure management (DCIM) systems to recommend and implement optimal control settings on HVAC and cooling systems as well as the CPU processors themselves, helping reduce energy and carbon emissions.

Autonomous Control

Automatically adjust performance and sleep states to match time-of-day workloads while maintaining Quality of Service. Run systems on open or closed-loop, either manually implementing suggested state changes or allowing automatic implementation.

Energy Mix Optimisation

Use location-specific weather forecast data to accurately predict energy generation from on-site solar or wind distributed energy sources, reducing fuel consumption and grid demand during peak tariff times, resulting in reduced emissions and lower running costs.

Energy Efficiency Index

Reduce energy and water consumption and costs in real-time without spending hours analysing time-series usage and workload data. The EEI algorithm recommends fan speed, temperature and even processor power state settings based on real-time server workload, resulting in quantified cost savings and reduced carbon emissions.

Case Study

A large data centre operator company used Foresight Optima DC+ to reduce server power consumption during idle time while maintaining service quality (QoS).

The Foresight Efficiency Index used granular telemetry data to identify opportunities for power savings and suggested actions to be taken on the platform to realise estimated power savings. The system ran in closed-loop, automatically implementing the AI-suggested actions and placing specific processors in the recommended power-saving state. This resulted in reduced CPU power consumption while maintaining application QoS results, leading to a reduced carbon footprint.

Large data centre operator

QiO is an Endorser of the EU Code of Conduct for Energy Efficient Data Centers.

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.