Machine learning algorithms for quality of service assurance in network traffic

A non-transitory computer-readable medium having a plurality of non-transitory instructions executable with a processor to predict future idle capacity of a network, the plurality of non-transitory instructions being executable for:

• measuring traffic volumes on a network during multiple different time windows to obtain a plurality of first data sets, each of the first data sets having associated with it a future traffic rate;
• mathematically generating a plurality of vectors, each of the vectors corresponding to and having a higher dimensionality than one of the first data sets;
• encoding each of the vectors, thereby generating a plurality of encodings corresponding to the plurality of first data sets;
• determining a future traffic value based on the future traffic rate of each of the first data sets that generated a specific encoding selected from the plurality of encodings;
• associating the future traffic value with the specific encoding and storing in memory;
• repeating the steps of determining and associating for each encoding of the plurality of encodings, thereby generating a plurality of encoding-to-future traffic value associations;
• measuring current traffic volumes on the network to produce a current traffic volume data set;
• creating a current traffic encoding representing the current traffic volumes within the current traffic volume data set;
• matching the current traffic encoding to one or more of the encoding-to-future value associations to obtain the future traffic value, which is a predicted future traffic rate for the current traffic volume data set;
• subtracting the predicted future traffic rate from a maximum network capacity to determine a future idle capacity;
• and
• reallocating at least a portion of the future idle capacity of the network.

A system for predicting future idle capacity of a network comprising:

• a processor configured to:
• measure traffic volumes on a network during multiple different time windows to obtain a plurality of first data sets, each of the first data sets having associated with it a future traffic rate;
• mathematically generate a plurality of vectors, each of the vectors corresponding to and having a higher dimensionality than one of the first data sets;
• encode each of the vectors, thereby generating a plurality of encodings corresponding to the plurality of first data sets;
• determine a future traffic value based on the future traffic rate of each of the first data sets that generated a specific encoding selected from the plurality of encodings;
• associate the future traffic value with the specific encoding and store in memory;
• repeat the steps of determining and associating for each encoding of the plurality of encodings, thereby generating a plurality of encoding-to-future traffic value associations;
• measure current traffic volumes on the network to produce a current traffic volume data set;
• create a current traffic encoding representing the current traffic volumes within the current traffic volume data set;
• match the current traffic encoding to one or more of the encoding-to-future traffic value associations to obtain the future traffic value, which is a predicted future traffic rate for the current traffic volume data set;
• subtract the predicted future traffic rate from a maximum network capacity to determine a future idle capacity;
• and
• reallocate at least a portion of the future idle capacity of the network.