Automatically distribute your workloads across the edge-to-cloud continuum

Model-driven optimization for edge to cloud computing and its networking

Acheive required latency and performance at lowest possible cost

Imagine a future where computing is a commodity just like electricity or water, where the resources for computing you need, are just around the corner but also optimally linked to the cloud. Imagine a world where data is generated at an exponential pace, and we move the compute closer to where the data is created instead of moving the data to where the compute resides. To start our digital future journey, we need to master how software components can be optimally distributed across a computing grid.

These are some of the reasons why we at Arctos Labs started to develop our ground-breaking edge cloud optimization technology. We wanted to enable a holistic optimization that combines data, data transportation, and computation into a holistic view.

Through this initiative, we think we contribute to a better world by lowering the energy footprint imposed by our computation, as well as providing an optimized compute grid that is more fit for purpose.

What is ECO?

ECO is a model-driven optimization engine powered by AI. It is intended to add a critical feature to any IT automation system as a plug-in. ECO can understand and make use of the underlying compute grid network metrics as well as application constraints such as latency to match for possible locations, but also impose optimization criteria such as cost for computing and data transport to the final selection.

Read more on Arctos Labs ECO solution

When do You need ECO?

You need ECO when your application is divided into multiple components that are potentially distributed across multiple locations and you want to have a precise way of controlling the distribution in an automated manner depending on how users are moving, the load is fluctuating, or cost vs benefit trade-offs are important.

Read more on what makes ECO different

Discover our edge cloud placement optimization technology

16 Feb 2023
Arctos Labs is excited to be announced as one of @STL Partners 100 edge companies to watch
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07 Mar 2023
Dynamic placement optimization in edge computing scenarios can enable significant capex and opex savings, new research indicate
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03 Apr 2023
Our white paper outlines the need for orchestration and optimization in IoT systems to improve availability and resilience
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