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.
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.
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
07 Mar 2023
Dynamic placement optimization in edge computing scenarios can enable significant capex and opex savings, new research indicate
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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Some of the things we can help you with
Optimization technology for distributed computing and its networking
Consuming distributed compute quickly grows complex. Analyzing all possible workload placement options require computer based optimization algorithms. Discover our placement optimization technology by which automatic placement can be determined across a network of compute to acheive lowest possble cost while meeting requried performance constraints.
Operational framework architectures for automation
Defining a suitable operational framework is complex. Making use of open source components, enabling the needed API exposure and model driven concepts is key to avoid unwanted lock-ins. We help Clients to enable the needed Service agility.
Technology strategy consultation
Navigating the width of acronyms and concepts popping up is really time consuming, and evaluating their maturity and usefulness is even more difficult. Arctos Labs is well suited to deliver such investigations that helps Clients in speeding up their cloud transformation.
Model development to enable DevOps & automation
In order to harvest the benefits, there needs to be real model development done, whilst at the same time evolve a proper DevOps environment and build the skills needed for the future. This step is what separates the successful companies from the rest. We have the needed competence and experience to be a key facilitator of such evolution.