Updated index.rst

Updated index.rst

index.rst

@@ -51,6 +51,13 @@ Because, most commercial cloud operating systems are deployment oriented and clo
 
 A lot. NeuronRain analytics can learn key-value pairs which can be read by kernel_analytics kernel module dynamically. Kernel thus is receptive to application layer a feature hitherto unavailable. Earlier OS drove applications - this is reversed by making applications drive kernel behaviour. 
 
+(*) Are there existing examples of machine learning being used in Linux kernel?
+
+Yes. There have been some academic research efforts, though not commercial, to write a machine learning scheduler for linux kernel.Linux kernel presently has Completely Fair Scheduler (CFS) which is based on Red-Black Tree insertion and deletion indexed by execution time. It is "fair" in the sense it treats running and sleeping
+processes equally. If incoming processes are treated as a streaming dataset, a hypothetical machine learning enabled scheduler could ideally be a "Multilabel Streaming Dataset Classifier" partitioning
+the incoming processes in the scheduler queue into "Highest,Higher,High,Normal,Low,Lower,Lowest" priority labels
+assigning time slices dynamically according to priority classifier. It is unknown if there is a classifier algorithm for streaming datasets (though there are streaming majority, frequency estimator, distinct elements streaming algorithms). In supervised classification, such algorithm might require some information in the headers of the executables and past history as training data, neural nets for example. Unsupervised classifier for scheduling (i.e scheduler has zero knowledge about the process) requires definition of a distance function between processes - similar processes are clustered around a centroid in Voronoi cells.
+
 (*) Who can deploy NeuronRain?
 
 Anyone interested in dynamic analytics driven kernel. For example, realtime IoT kernels operating on smart devices, driverless vehicles, robots, drones, embedded systems etc.,.