US 2019/0104028 A1 · PERFORMANCE MONITORING AT EDGE OF COMMUNICATION NETWORKS USING HYBRID MULTI-GRANULAR COMPUTATION WITH LEARNING FEEDBACK

Abstract

Example implementations disclosed herein are directed to monitoring communications networks in real time to ensure that they are performing in a desired fashion. Metrics such as utilization and latency are monitored to ensure that customer SLAs (Service Level Agreements) are met in a timely fashion. To detect problems in advance, example implementations predict the future values of metrics and detect events if the future value violates certain conditions. Example implementations build models that can predict the future values of metrics and analyze historical, near real time and real time streaming data so as to build predictive models.

Claims15

Dependent claims

A management server comprising:

a memory configured to manage a first prediction model of traffic to a first type of storage configured to store data for a first time period in a storage system, a second prediction model of traffic to a second type of storage configured to store data for a second time period in the storage system, and a third prediction model of traffic to a third type of storage configured to store data for a third time period in the storage system, wherein the second time period is longer than the first time period, and wherein the third time period is longer than the second time period;
a processor, configured to:
compute a prediction model of traffic to the storage system based on the application of weights to the first prediction model, the second prediction model, and the third prediction model;
and
update the application of weights based on traffic results.

2 more elements

A method, comprising:

managing a first prediction model of traffic to a first type of storage configured to store data for a first time period in a storage system, a second prediction model of traffic to a second type of storage configured to store data for a second time period in the storage system, and a third prediction model of traffic to a third type of storage configured to store data for a third time period in the storage system, wherein the second time period is longer than the first time period, and wherein the third time period is longer than the second time period;
computing a prediction model of traffic to the storage system based on the application of weights to the first prediction model, the second prediction model, and the third prediction model;
and
updating the application of weights based on traffic results.

1 more element

Dependencies are inferred from the claim text.

Performance monitoring at edge of communication networks using hybrid multi-granular computation with learning feedback

Life of this patent

Performance monitoring at edge of communication networks using hybrid multi-granular computation with learning feedback

Life of this patent

Abstract

Claims15