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Fast In-Memory Computing

In-Memory for Speed and Scale

The ScaleOut Digital Twins service uses patented in-memory computing technology that provides the speed and scalable memory capacity needed to host thousands of digital twins for real-time analytics and simulations. It stores digital twins as memory-based objects and automatically distributes them across a cluster of either cloud-based instances or on-premises servers.  As the workload grows, the cluster adds servers to scale its capacity on demand. Users just need to focus on developing digital twin models, and the service takes care of the rest.

To minimize latency, the service delivers each message to the server which hosts the target digital twin. This avoids both the delay and overhead of retrieving digital twin state information and enables message processing within a few milliseconds. At the same time, in-memory computing technology harnesses all servers to generate aggregate statistics every few seconds.

A programmer on a computer in a large server room

Patented Technology

ScaleOut’s in-memory computing technology has been in continuous development for more than a decade and incorporates patented scalability and high availability.

Ideal for Digital Twins

In-memory computing’s object-oriented data storage and computing offer an ideal match for the digital twin model and enable hosting for thousands of digital twins.

Built-In Aggregate Analytics

ScaleOut’s in-memory computing technology incorporates fast, data-parallel computing that combines and visualizes digital twin state information in seconds.

A diagram showing scaling an airline simulation using digital twins

Real-Time Predictive Modeling

The ScaleOut Digital Twins service enables digital twins to simulate many thousands of entities and their interactions. This provides a powerful tool for extracting insights and making real-time predictions about complex systems that must operate at peak efficiency. The service can harness as many servers as it needs to run a large simulation with maximum throughput, and it orchestrates the simulation’s progress at a rate selected by the user. Aggregate analytics and queries let users monitor the simulation’s behavior and results.

For example, an airline can use a digital twin simulation to model the myriad entities that it must manage on a daily basis, including passengers, pilots, aircraft, airport gates, and bags. The simulation can inject weather events and system outages to measure the impact on flight delays, gate congestion, and passenger re-bookings. Simulations can run faster than real time to evaluate alternative scheduling decisions and assist in decision making.

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