Adapting a pre-trained distributed resource predictive model to a target distributed computing environment
First Claim
1. A method, comprising:
- training a model into a trained model, wherein the model comprises a set of trained model parameters to capture resource consumption of computing and storage resources of a workload in a first computing environment, and the first computing environment is characterized by a first configuration;
detecting a difference between the first computing environment and a second computing environment characterized by a second configuration, whereinthe first configuration comprises at least a portion of the second configuration so that the first computing environment in which the model is trained comprises a computing node that is also in the second computing environment into which the trained model is to be deployed; and
deploying the trained model to the second computing environment at least by adapting the trained model to the second computing environment, wherein adapting the trained model comprises modifying the set of model parameters based at least in part on the difference prior to conclusion of adapting the trained model to the second computing environment.
1 Assignment
0 Petitions
Accused Products
Abstract
Systems for distributed resource system management. A first computing system operates in a first computing environment. A predictive model is trained in the first computing environment to form a trained resource performance predictive model that comprises a set of trained model parameters to capture at least computing and storage IO parameters that are responsive to execution of one or more workloads that consume computing and storage resources in the first computing environment. When the trained resource performance predictive model is deployed to a second computing environment, various computing system configuration differences, and/or workload differences and/or other differences between the first computing environment and the second computing environment are detected and measured. Responsive to the detected differences and/or measurements, some of the trained resource performance predictive model parameters are modified to adapt the trained resource performance predictive model to any of the detected and/or measured characteristics of the second computing environment.
-
Citations
20 Claims
-
1. A method, comprising:
-
training a model into a trained model, wherein the model comprises a set of trained model parameters to capture resource consumption of computing and storage resources of a workload in a first computing environment, and the first computing environment is characterized by a first configuration; detecting a difference between the first computing environment and a second computing environment characterized by a second configuration, wherein the first configuration comprises at least a portion of the second configuration so that the first computing environment in which the model is trained comprises a computing node that is also in the second computing environment into which the trained model is to be deployed; and deploying the trained model to the second computing environment at least by adapting the trained model to the second computing environment, wherein adapting the trained model comprises modifying the set of model parameters based at least in part on the difference prior to conclusion of adapting the trained model to the second computing environment. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
10. A non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by a processor, causes the processor to perform a set of acts, the set of acts comprising:
-
training a model into a trained model, wherein the model comprises a set of model parameters to capture resource consumption of computing and storage resources of a workload in a first computing environment, and the first computing environment is characterized by a first configuration; detecting a difference between the first computing environment and a second computing environment characterized by a second configuration, wherein the first configuration comprises at least a portion of the second configuration so that the first computing environment in which the model is trained comprises a computing node that is also in the second computing environment into which the trained model is to be deployed; and deploying the trained model to the second computing environment, at least by adapting the trained model to the second computing environment, wherein adapting the trained model comprises modifying the set of model parameters based at least in part on the difference prior to conclusion of adapting the trained model to the second computing environment. - View Dependent Claims (11, 12, 13, 14, 15, 16)
-
-
17. A system, comprising:
-
a storage medium having stored thereon a sequence of instructions; and a processor that executes the instructions to cause the processor to perform a set of acts, the set of acts comprising, training a model into a trained model, wherein the model comprises a set of model parameters to capture resource consumption of computing and storage resources of a workload in a first computing environment, and the first computing environment is characterized by a first configuration; detecting a difference between the first computing environment and a second computing environment characterized by a second configuration, wherein the first configuration comprises at least a portion of the second configuration so that the first computing environment in which the model is trained comprises a computing node that is also in the second computing environment into which the trained model is to be deployed; and deploying the trained model to the second computing environment, wherein at least by adapting the trained model to the second computing environment, wherein adapting the model comprises modifying the set of trained model parameters based at least in part on the difference prior to conclusion of adapting the trained model to the second computing environment. - View Dependent Claims (18, 19, 20)
-
Specification