Data Center Digital Twin for Sustainable Exascale Computing

Graph-Massivizer targets “sustainable science throughput” through scalable energy-aware,  exascale  operation  and  traceable  TCO 107  understanding,  including  sustainability  indicators  and  their environmental effects (e.g., GHG emissions). The Graph-Massivizer tools will enable the creation of a novel, graph-based digital twin of a data centre; this digital twin will further support the construction of sustainable exascale computing operational models to support scientific discovery in the next decade.

The Data Center Digital Twin for Sustainable Exascale Computing Use Case in detail

High Performance Computing (HPC) plays a crucial role in scientific progress, but as systems approach exascale, maintenance becomes increasingly difficult. This Use Case introduces a graph-based digital twin of HPC centers that addresses anomaly handling, energy efficiency enhancement, and carbon emissions reduction.

The main objective of this Use Case is to develop and implement a sustainable framework to tackle various challenges encountered in HPC systems, including handling anomalies, enhancing energy efficiency, reducing carbon emissions, and ultimately optimizing system performance.

The logic of the Use Case involves the integration of various components, such as Graph Inceptor to output the graph representation of the telemetry data of the HPC for anomaly prediction models, as well as the Graph Scrutinizer to execute the BGOs, as well as the Optimizer and Choreographer to provide the computational resources for performing inference/graph queries.

Expected Outcomes of the Use Case

The targeted outcomes are the implementation of a sustainable model for data centers based on graphs, the development of an ontology for HPC systems, and the creation of a data center data model. The outcomes will be achieved by creating a graph-based digital twin of HPC centers to optimize system performance and make data centers more scalable and sustainable.

This solution will enable the implementation of complex queries that make the work easier for facility managers and engineers that are not directly possible for current monitoring systems.

The solution plans to export open-source ontologies of the Marconi100 public dataset, as well as to provide technologies to be tested in data centers in production, and it will target data centers and HPC systems for exploitation. The stakeholders involved in this use case are data center owners, operational data analytics framework developers, and data center operators.

Global Foresight for Environment Protection

Global foresight for environment protection focuses on geopolitical and business aspects of ESG, including climate  action, responsible production and consumption patterns, clean water and sanitation, and clean and affordable energy. The foresight comprehends insights on future trends and scenarios to guide decision-making in developing better  policies. A contextual graph built through data from the Common Crawl, Linked Open Data Cloud, and global media news provides unique insights into mass media’s convergence of the three societal systems (economy, politics, science).

The Global Foresight for Environment Protection Use Case in detail

Global foresight for environmental protection focuses on the geopolitical and business aspects of ESG, which includes climate action, responsible production and consumption patterns, clean water, and affordable energy.

This approach involves analyzing news articles and extracting information about events happening in the world, particularly those concerning companies mentioned in the articles. With the help of Graph-Massivizer, past events will be used to compute potential future events to detect trends and anomalies.

Use case overview

The main objective of this use case is to identify individual relations related to global companies in online media mentions. These relations are related to various topics concerning environmental protection and business operations. Based on these extracted relations, predictions about potential future events can be made. These predictions will be based on identified generalized sequences of events.

Using Graph Massivizer, we can identify shared sequences of events across multiple companies and use those patterns to predict new events based on a sequence of recent events about a company. Besides predicting future events, the aim is also to identify specific trends and anomalies in the data by observing data in individual industries, sectors, and geographies.

The outcome of this project is the ability to identify common sub-graphs related to individual companies in large graphs of data. These patterns can be used to predict, as accurately as possible, likely future events.

This Use Case relies on cutting-edge technology to help organizations predict future events based on past data patterns. Our workflow is based on the initial provision of data as JSON objects, which are then converted to a graph structure using an established ontology. The Graph Scrutinizer is our main component for computing patterns and trends, and it operates on data imported into Graph Massivizer. By leveraging its powerful capabilities, the Graph Scrutinizer can predict potential future events for a new subgraph based on common patterns.  The Use Case relies primarily on Graph-Inceptor for ingesting new data and Graph-Scrutinizer for extracting common patterns and trends from the graph.

Expected Outcomes of the Use Case

The results of our use case are reported in the form of a list of potential events predicted for a given input sequence of past events. Each predicted event has an associated probability, giving a clear sense of the likelihood of it occurring based on the frequency of the pattern in past data.

Our prediction component is the perfect addition to your existing product, seamlessly integrating with it to offer you an additional service for an extra charge.

We are committed to offering this service to organizations in the geopolitical segment, hedge funds and investment companies, and general companies interested in monitoring their supply chain. As the primary stakeholders in this use case, analysts will benefit from our technology as it enables them to understand what is happening globally and the potential consequences of these events. By leveraging our cutting-edge tools and technology, you can be confident in your ability to predict future events and make informed decisions that will keep you ahead of the curve.

Detail of the User Interface