Telefonaktiebolaget Lm Ericsson (publ)

Magnus Templing

Patrik Åberg

Staffan Månsson

Victor Millnert

A software monitoring arrangement () comprising a controller () configured to: a) receive a software code module (); b) introduce a plurality of probes () into the software code module (), each probe () being arranged to provide output () when executed; c) execute the software code module () generating an image log file () based on the output () of the probes (); d) provide image analysis () of the image log file (); e) provide an indication () of an expected behavior based on the image analysis (); f) receive an indication () of an actual behavior; g) compare the expected behavior as indicated by its indicator () to the actual behavior as indicated by its indicator (); and provide an alert () based on the comparison.

Software execution monitoring using neural networks

A method of a priority trainer of a many core processing system comprising a plurality of cores is disclosed. The many core processing system is configured to execute one or more computer jobs, and wherein the priority trainer comprises a controller, the method comprising:

Priority trainer for many core processing system

Disclosed herein is a trainer of a many core system comprising a plurality of cores for controlling resource utilization within the many core system, wherein the trainer comprises a controller configured to cause a task scheduler to add a first synthetic variable load () to at least one task thread comprising at least one task and to schedule the at least one task thread; cause a generic probe element () to set a plurality of probes configured to measure a real time requirement at a respective plurality of points within an execution of the at least one task thread; cause a training element () to calculate a real time slack measurement constant (RS) value based on the worst case timing for each of the plurality of probes and to select at least one of the plurality of probes, wherein the selected at least one probe has a worst case RS value, wherein the training element is further configured to calculate a system load constant (SL) value based on the worst case RS value; and to cause a resource manager to adjust at least one system parameter based on the at least one probe having the worst case RS value. Also disclosed is a method of a trainer and a computer program product.

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Lund, Sweden

Magnus Templing