In one example, neuro-fuzzy expert system logic analyzes and assigns significance values to events occurring in a cloud infrastructure. The logic includes event significance logic and a plurality of neuro-fuzzy logic modules. Each of the logic modules includes fuzzy logic to determine a fuzzy value of an effect of a detected event on the cloud infrastructure. The event significance logic includes neuro-fuzzy logic to determine, based on the values of the effects of the detected event determined by the plurality of neuro-fuzzy logic modules, a value indicative of significance of the event to the cloud infrastructure.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: a processor; neuro-fuzzy expert system logic executable by the processor to analyze and assign significance values to events occurring in a cloud infrastructure, the logic comprising: a plurality of neuro-fuzzy logic modules, each of the logic modules comprising fuzzy logic to determine a fuzzy value of an effect of a detected event on the cloud infrastructure; and event significance logic comprising neuro-fuzzy logic to determine, based on the values of the effects of the detected event determined by the plurality of neuro-fuzzy network modules, a value indicative of significance of the event to the cloud infrastructure. 2. The system of claim 1, wherein the plurality of neuro-fuzzy logic modules comprise: event potential determination logic comprising fuzzy logic to determine a value of potential impact of the detected event on the cloud infrastructure; event seriousness determination logic comprising fuzzy logic to determine a value of seriousness of the effect of the detected event on the cloud infrastructure; event magnitude determination logic comprising fuzzy logic to determine a value of magnitude of effect of the event on the cloud infrastructure. 3. The system of claim 2, wherein the event potential determination logic is to determine the value of potential impact of the event on the cloud infrastructure based on a number of previously detected events that are similar to the detected event, a number of components previously affected by an event similar to the detected event, and a number of business services previously affected by an event similar to the detected event; wherein the event seriousness determination logic is to determine the value of seriousness of the event based on a number of users of the cloud infrastructure currently affected by the detected event, urgency of services affected by the detected event, and current utilization levels of components affected by the detected event; wherein the event magnitude determination logic is to determine the value of magnitude of effect of the event on the cloud infrastructure based on a number of components affected by the detected event, a number of business services affected by the detected event, and a number of events similar to the detected event detected concurrently with the detected event. 4. The system of claim 1, wherein the plurality of logic modules and the event significance logic each comprises a plurality of layers, the plurality of layers comprising: an input layer to map discrete values input to the logic module to input fuzzy term values; a rules layer to apply fuzzy logic rules to the input fuzzy term values; and an output layer to map output fuzzy term values generated by the rules layer to a fuzzy output value. 5. The system of claim 4, wherein each of the plurality of logic modules and the event significance logic further comprises an inference engine to specify a hierarchy for application of the fuzzy logic rules. 6. The system of claim 4, wherein the output layer is to generate a fuzzy output value and a crisp output value from the output fuzzy term values. 7. The system of claim 4, further comprising training logic to adjust the logic of the input layer, the rules layer, and the output layer of each of the plurality of logic modules and the event significance logic based on comparison of a training data set to outputs of the plurality of logic modules and the event significance logic. 8. A method, comprising: detecting an event occurring in a cloud infrastructure; applying neuro-fuzzy logic to determine effects of the event, the effects comprising: a value of potential impact of the detected event on the cloud infrastructure; a value of seriousness of the effect of the detected event on the cloud infrastructure; a value of magnitude of effect of the event on the cloud infrastructure; determining, via neuro-fuzzy logic, based on the value of potential impact, the value of seriousness, and the value of magnitude, a value indicative of significance of the event to the cloud infrastructure. 9. The method of claim 8, wherein the potential impact of the detected event is determined based on a number of previously detected events that are similar to the detected event, a number of components previously affected by an event similar to the detected event, and a number of business services previously affected by an event similar to the detected event. 10. The method of claim 8, wherein the value of seriousness of the event is based on a number of users of the cloud infrastructure currently affected by the detected event, urgency of services affected by the detected event, and current utilization levels of components affected by the detected event; and wherein the value of magnitude of effect of the event on the cloud infrastructure based on a number of components affected by the detected event, a number of business services affected by the detected event, and a number of events similar to the detected event detected concurrently with the detected event. 11. The method of claim 8, wherein the value of potential impact, the value of seriousness, the value of magnitude, and the value indicative of significance are each determined in a different neuro-fuzzy logic module, and the method further comprises: processing the detected event in a plurality of layers of each neuro-fuzzy logic module, the processing comprising: mapping, in an input layer, discrete input values o input fuzzy term values; applying, a rules layer, fuzzy logic rules to the input fuzzy term values; and mapping, in an output layer, fuzzy term values generated by the rules layer to a fuzzy output value. 12. The method of claim 11, further comprising specifying, in an inference engine, a hierarchy for application of the fuzzy logic rules. 13. The method of claim 11, further comprising to generating, in the output layer: a discrete output value of significance from the output fuzzy term values; and a fuzzy output value of significance from the output fuzzy term values. 14. The method of claim 11, further comprising adjusting logic of the input layer, the rules layer, and the output layer of each neuro-fuzzy logic module and the event significance logic based on comparison of a training data set to outputs of the neuro-fuzzy logic modules. 15. A non-transitory computer-readable medium encoded with instructions that when executed cause a processor to: provide a neuro-fuzzy expert system to analyze and assign significance values to events occurring in a cloud infrastructure; determine a fuzzy value of an effect of a detected event on the cloud infrastructure via a plurality of neuro-fuzzy logic modules, each of the logic modules comprising fuzzy logic; and determine, based on the determined values of the effects of the detected event, a value of significance of the event to the cloud infrastructure; and manage the event in the cloud infrastructure based on the determined value of significance; wherein the plurality of neuro-fuzzy logic modules comprise: event potential determination logic comprising fuzzy logic to determine a value of potential impact of the detected event on the cloud infrastructure; event seriousness determination logic comprising fuzzy logic to determine a value of seriousness of the effect of the detected event on the cloud infrastructure; and event magnitude determination logic comprising fuzzy logic to determine a value of magnitude of effect of the event on the cloud infrastructure.