Modeling cloud inefficiencies using domain-specific templates

A server, comprising:

• one or processors;
• memory;
• wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs comprising instructions for:
• obtaining telemetric log data for one or more services, distinct from the server, executing on one or more cloud computing systems;
• determining one or more disaggregation data for the one or more services based on the telemetric log data by applying one or more disaggregation algorithms;
• forming feature vectors based on the telemetric log data;
• identifying software of service types and one or more cloud wastage templates by inputting the feature vectors to trained one or more classifiers, wherein the cloud wastage templates follow conventions of a domain specific language (DSL) that describe the one or more cloud computing systems, wherein each classifier is a machine-learning model trained to identify cloud wastages for predetermined states of the one or more cloud computing systems;
• determining one or more cloud states of one or more computing resources used by the one or more services based on the one or more disaggregation data;
• and
• cataloging cloud inefficiencies using the one or more cloud wastage templates based on the one or more cloud states.

A non-transitory computer readable storage medium storing one or more programs configured for execution by a computer system having one or more processors, the one or more programs comprising instructions for:

• obtaining telemetric log data for one or more services, distinct from the server, executing on one or more cloud computing systems;
• determining one or more disaggregation data for the one or more services based on the telemetric log data by applying one or more disaggregation algorithms;
• forming feature vectors based on the telemetric log data;
• identifying software of service types and one or more cloud wastage templates by inputting the feature vectors to trained one or more classifiers, wherein the cloud wastage templates follow conventions of a domain specific language (DSL) that describe the one or more cloud computing systems, wherein each classifier is a machine-learning model trained to identify cloud wastages for predetermined states of the one or more cloud computing systems;
• determining one or more cloud states of one or more computing resources used by the one or more services based on the one or more disaggregation data;
• and
• cataloging cloud inefficiencies using the one or more cloud wastage templates based on the one or more cloud states.