Enhancing Power Management in Telecoms and Data Centers

David McGuirk

14 July 2023

Enhancing Power Management in Telecoms and Data Centers Enabling P and C States, Predictive Modelling, and Automated Closed-Loop Control

Telecom providers and data center operators strive to optimise power management for energy efficiency, thermal control, scalability, and performance. Enabling P and C states on CPU power management and utilising predictive modelling, along with automated closed-loop control, are effective strategies. This article explores the benefits, drawbacks, and impact of QiO’s Foresight Optima DC+™, a power management solution exclusively available on Intel chipsets. It highlights the integration of these approaches to enhance power management.

Pros and Cons of Enabling P and C States

Enabling P and C states offers significant advantages. It improves energy efficiency by allowing CPUs to dynamically adjust power consumption, reducing costs and environmental impact. Lower power consumption aids in thermal management and extends hardware lifespan. Effective power management optimises scalability and capacity planning, while modern CPUs can boost performance when needed. However, challenges such as response latency and workload optimisation need to be addressed.

Utilising Predictive Modelling and Automated Closed-Loop Control for Power Management:

Based on historical data and machine learning techniques, predictive modelling proactively adjusts power states to minimise response latency, optimise workload selection, and effectively handle power state transitions. Furthermore, automated closed-loop control continuously monitors and adjusts power management parameters based on real-time feedback, ensuring optimal performance and efficiency.

The Impact of QiO’s Foresight Optima DC+™ on Intel Chipsets

QiO’s Foresight Optima DC+™ is a power management solution developed in collaboration with Intel. Exclusively available on Intel chipsets, it aims to reduce carbon emissions and energy consumption in CPU fleets. Intel has observed energy savings of up to 52% (idle) and 24% (representative workload) using DC+™, thanks to its sophisticated ML and AI algorithms. Tests by WWT have shown up to 29% energy reduction in dynamic workloads, validating the solution’s potential for energy optimisation.

Synergy and Conclusion

By combining the benefits of enabling P and C states, utilising predictive modelling, and implementing automated closed-loop control with QiO’s Foresight Optima DC+™ exclusively on Intel chipsets, can significantly enhance power management strategies. This holistic approach improves energy efficiency, thermal control, scalability, and performance while mitigating potential drawbacks. The collaboration between QiO Technologies and Intel underscores their commitment to sustainability. The impressive results achieved with DC+™ highlight its effectiveness in real-world scenarios.

In conclusion, integrating P and C states, predictive modelling, and automated closed-loop control with QiO’s Foresight Optima DC+™ exclusively on Intel chipsets offers a comprehensive solution for sustainable and energy-efficient data centers. Operators should evaluate these approaches to optimise power utilisation and enhance operational efficiency. The combination of innovative technologies and strategies, supported by Intel’s chipsets, drives the transition towards greener data centers.

If you’d like to understand the energy reduction ROI potential within your IT infrastructure, email me directly at David.Mcguirk@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.