nvme monitor: continuous discovery and connect to discovered subsystemsa project by ematsumiya Project Description |
GCC aggregate trackinga project by jamborm Currently GCC relies only on the most generic alias analysis when attempting to track data in aggregates in interprocedural (IPA) optimizations. In the course of this project I plan to revive patches for using simple escape analysis to track all data which do not have their address escaped and use that information to track constants within them, analyze the impact on a number of benchmarks and submit them to GCC trunk. |
JUnit SLEnkins Test for Firefoxa project by cgrobertson Create a JUnit test suite for Firefox browser and integrate the tests into SLEnkins. |
User assisted udev rulesan idea by sbrabec udev is a perfect tool for applying hardware based rules. But there are some devices that are indistinguishable by its identification and even by probe. |
add features to libstorage-ngan invention by aschnell Add some features to libstorage-ng. |
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Stratos Analysis Toolsan invention by nwmac Extend Stratos (https://github.com/SUSE/stratos) by adding the ability to integrate open-source Analysis tools such as Popeye, Kube Score, Anchore, Clair etc, so that users can run these tools on their clusters from Stratos and view the results from Stratos. |
HAKube UI plugin for Ranchera project by epenchev |
PowerPC appliances deploy toolan idea by k0da We need a tool similar to suseviclient for deploying powerpc appliances. |
Ceph crushmap visualizationa project by qakapil The CRUSH algorithm determines how to store and retrieve data by computing data storage locations. CRUSH empowers Ceph clients to communicate with OSDs directly rather than through a centralized server or broker. With an algorithmically determined method of storing and retrieving data, Ceph avoids a single point of failure, a performance bottleneck, and a physical limit to its scalability. CRUSH requires a map of your cluster, and uses the CRUSH map to pseudo-randomly store and retrieve data in OSDs with a uniform distribution of data across the cluster. |