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Marco Varlese

mvarlese

Involved Projects and Packages

Derived from the base container, with bash and zypper preinstalled.

Environment for Java 15 development.

Image for R-Studio Server

Maintainer

Phoeβe (/ˈfiːbi/) wants to add basic artificial intelligence capabilities to the Linux OS.

System-level tuning is a very complex activity, requiring the knowledge and expertise of several (all?) layers which compose the system itself, how they interact with each other and (quite often) it is required to also have an intimate knowledge of
the implementation of the various layers.

Another big aspect of running systems is dealing with failure. Do not think of failure as a machine turning on fire rather as an overloaded system, caused by misconfiguration, which could lead to starvation of the available resources.

In many circumstances, operators are used to deal with telemetry, live charts, alerts, etc. which could help them identifying the offending machine(s) and (re)act to fix any potential issues.

However, one question comes to mind: wouldn't it be awesome if the machine could auto-tune itself and provide a self-healing capability to the user? Well, if that is enough to trigger your interest then this is what Phoeβe aims to provide.

Phoeβe uses system telemetry as the input to its brain and produces a big set of settings which get applied to the running system. The decision made by the brain is continuously reevaluated (considering the grace_period setting)
to offer eventually the best possible setup.

This project was created for package openSUSE-Tumbleweed-Container-kiwi via attribute OBS:Maintained

Maintainer

The project networking is intended for packages providing various networking services and related tools.

repository SLE_15 is for the latest service pack in SLE15.
repository SLE_12 is for the latest service pack in SLE12.

Phoeβe (/ˈfiːbi/) wants to add basic artificial intelligence capabilities to the Linux OS.

System-level tuning is a very complex activity, requiring the knowledge and expertise of several (all?) layers which compose the system itself, how they interact with each other and (quite often) it is required to also have an intimate knowledge of
the implementation of the various layers.

Another big aspect of running systems is dealing with failure. Do not think of failure as a machine turning on fire rather as an overloaded system, caused by misconfiguration, which could lead to starvation of the available resources.

In many circumstances, operators are used to deal with telemetry, live charts, alerts, etc. which could help them identifying the offending machine(s) and (re)act to fix any potential issues.

However, one question comes to mind: wouldn't it be awesome if the machine could auto-tune itself and provide a self-healing capability to the user? Well, if that is enough to trigger your interest then this is what Phoeβe aims to provide.

Phoeβe uses system telemetry as the input to its brain and produces a big set of settings which get applied to the running system. The decision made by the brain is continuously reevaluated (considering the grace_period setting)
to offer eventually the best possible setup.

VM images of openSUSE Leap 15.2 with Machine Learning frameworks, tools and libraries.

VM images of openSUSE Tumbleweed for various platforms.

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