Modification of cluster configuration settings using machine learning

A method comprising:

• determining a first numerical value representative of an amount of central processing unit (CPU) resources allocated to a microservice of an active computing cluster and a second numerical value indicative of an amount of memory allocated to the microservice of the active computing cluster;
• computing a first set of similarity scores using the first numerical value, each similarity score in the first set of similarity scores being indicative of a level of similarity of the active computing cluster to one of a plurality of computing clusters with respect to the amount of CPU resources allocated to the microservice;
• computing a second set of similarity scores using the second numerical value, each similarity score in the second set of similarity scores being indicative of a level of similarity of the active computing cluster to one of the plurality of computing clusters with respect to the amount of memory allocated to the microservice;
• generating a set of overall similarity scores based on the first set of similarity scores and the second set of similarity scores;
• selecting, based on the set of overall similarity scores and using a machine learning model, a subset of computing clusters from the plurality of computing clusters;
• generating a recommended modification to the amount of CPU resources or the amount of memory allocated to the microservice based on the subset of computing clusters;
• and
• adjusting the amount of CPU resources or the amount of memory allocated to the microservice according to the recommended modification.

A non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to perform operations comprising:

• determining a first numerical value representative of an amount of central processing unit (CPU) resources allocated to a microservice of an active computing cluster and a second numerical value indicative of an amount of memory allocated to the microservice of the active computing cluster;
• computing a first set of similarity scores using the first numerical value, each similarity score in the first set of similarity scores being indicative of a level of similarity of the active computing cluster to one of a plurality of computing clusters with respect to the amount of CPU resources allocated to the microservice;
• computing a second set of similarity scores using the second numerical value, each similarity score in the second set of similarity scores being indicative of a level of similarity of the active computing cluster to one of the plurality of computing clusters with respect to the amount of memory allocated to the microservice;
• generating a set of overall similarity scores based on the first set of similarity scores and the second set of similarity scores;
• selecting, based at least in part on the set of overall similarity scores and using a machine learning model, a subset of computing clusters from the plurality of computing clusters;
• generating a recommended modification to the amount of CPU resources or the amount of memory allocated to the microservice based on the subset of computing clusters;
• and
• adjusting the amount of CPU resources or the amount of memory allocated to the microservice according to the recommended modification.