Mixture model approach for network forecasting

A system, comprising:

• at least one computing device;
• and
• a mixture model forecasting engine executable in the at least one computing device, wherein when executed the mixture model forecasting engine causes the at least one computing device to at least:
• generate respective network traffic models for individual ones of a plurality of host types, the plurality of host types including at least one of:
• a host type optimized for data storage, a host type optimized for computation, or a host type optimized for system memory, wherein the respective network traffic models are generated using a first training data set;
• determine respective weights for individual ones of the respective network traffic models based at least in part on at least one of:
• respective error variance values, respective Akaike information criterion (AIC) values, respective Bayesian information criterion (BIC) values, or respective overall measures of discrepancy for the individual ones of the respective network traffic models, wherein the respective overall measures of discrepancy assess a respective accuracy of the individual ones of the respective network traffic models using a second training data set;
• and
• generate a network forecast based at least in part on a hardware footprint forecast defining a respective predicted number of racks at a data center and on the respective network traffic models weighted by the respective weights.

A method, comprising:

• generating, by a computing device, respective network traffic models for individual ones of a plurality of host types, the plurality of host types including at least one of:
• a host type optimized for data storage, a host type optimized for computation, or a host type optimized for system memory, wherein the respective network traffic models are generated using a first training data set;
• determining, by the computing device, respective weights for individual ones of the respective network traffic models based at least in part on at least one of:
• respective error variance values, respective Akaike information criterion (AIC) values, respective Bayesian information criterion (BIC) values, or respective overall measures of discrepancy for the individual ones of the respective network traffic models, wherein the respective overall measures of discrepancy assess a respective accuracy of the individual ones of the respective network traffic models using a second training data set;
• and
• generating, by the computing device, a network forecast based at least in part on a hardware footprint forecast defining a respective predicted number of racks at a data center and on the respective network traffic models weighted by the respective weights.