WORKLOAD MIGRATION
Embodiments of the present invention provide concepts for identifying an edge computing environment location as a target for workload migration. For example, embodiments may provide for a machine-learning algorithm to be trained to predict or suggest the most appropriate edge location for migrating a workload. Using a description of a workload, the machine-learning algorithm may predict/suggest one or more edge locations.
1 . A method of training a machine learning algorithm for identifying an edge computing environment location as a target for workload migration, the method comprising:
receiving: i) a description of a training workload; and ii) an identifier of an edge location for migrating the training workload; and
training the machine learning algorithm based on the description of a training workload as a training input for the machine learning algorithm and the identifier of the edge location for migrating the training workload as a training output for the machine learning algorithm.
2 . The method of claim 1 , wherein the training comprises adjusting a configuration of the machine learning algorithm so as to decrease an outage time objective.
3 . The method of claim 1 , wherein the training comprises:
adjusting a configuration of the machine learning algorithm so as to decrease a difference between the identifier of the edge location for migrating the training workload and a second identifier that is output by the machine learning component.
4 . The method of claim 1 , wherein the machine learning algorithm comprises a neural network and wherein the training is based on a loss function for a gradient of the neural network.
5 . The method of claim 1 , wherein the description of the training workload comprises a time series of a plurality of variables.
6 . A method for workload migration at an edge computing environment, the method comprising:
providing a description of a workload to a machine learning algorithm for identifying an edge computing environment location as a target for workload migration; and
obtaining a prediction result from the machine learning algorithm based on the description of the workload, the prediction result including an identifier of an edge location.
7 . The method of claim 6 , wherein the machine learning algorithm is trained using a training algorithm configured to receive an array of training inputs and known outputs, wherein the training inputs comprise descriptions of training workloads and the known outputs comprise identifiers of edge locations for migrating the training workloads.
8 . The method of claim 6 , further comprising communicating the workload to an edge computing environment associated with the identifier of an edge location included in the prediction result.
9 . The method of claim 6 , further comprising:
prior to providing the description of the workload to the machine learning algorithm, processing the description of the workload so as to meet a predetermined formatting requirement.
10 . The method of claim 6 , further comprising:
receiving feedback on the prediction result; and
modifying the configuration of the machine learning algorithm based on the received feedback.
11 . A computer program product for training a machine learning algorithm for identifying an edge computing environment location as a target for workload migration, the computer program product comprising one or more computer readable storage media having program instructions embodied therewith, the program instructions executable by a processing unit to cause the processing unit to perform a method comprising:
receiving: i) a description of a training workload; and ii) an identifier of an edge location for migrating the training workload; and
training the machine learning algorithm based on the description of a training workload as a training input for the machine learning algorithm and the identifier of the edge location for migrating the training workload as a training output for the machine learning algorithm.
12 . The computer program product of claim 11 , wherein the training comprises adjusting a configuration of the machine learning algorithm so as to decrease an outage time objective.
13 . The computer program product of claim 11 , wherein the training comprises:
adjusting a configuration of the machine learning algorithm so as to decrease a difference between the identifier of the edge location for migrating the training workload and a second identifier that is output by the machine learning component.
14 . The computer program product of claim 11 , wherein the machine learning algorithm comprises a neural network and wherein the training is based on a loss function for a gradient of the neural network.
15 . The computer program product of claim 11 , wherein the description of the training workload comprises a time series of a plurality of variables.
16 . A computer program product for workload migration at an edge computing environment, the computer program product comprising one or more computer readable storage media having program instructions embodied therewith, the program instructions executable by a processing unit to cause the processing unit to:
provide a description of a workload to a machine learning algorithm for identifying an edge computing environment location as a target for workload migration; and
obtain a prediction result from the machine learning algorithm based on the description of the workload, the prediction result including an identifier of an edge location.
17 . The computer program product of claim 16 , wherein the machine learning algorithm is trained using a training algorithm configured to receive an array of training inputs and known outputs, wherein the training inputs comprise descriptions of training workloads and the known outputs comprise identifiers of edge locations for migrating the training workloads.
18 . The computer program product of claim 16 , wherein the program instructions are executable to communicate the workload to an edge computing environment associated with the identifier of an edge location included in the prediction result.
19 . The computer program product of claim 16 , wherein the program instructions are executable to:
prior to providing the description of the workload to the machine learning algorithm, process the description of the workload so as to meet a predetermined formatting requirement.
20 . The computer program product of claim 16 , wherein the program instructions are executable to:
receive feedback on the prediction result; and
modify the configuration of the machine learning algorithm based on the received feedback.
21 . A system for training a machine learning algorithm for identifying an edge computing environment location as a target for workload migration, the system comprising:
a processor arrangement configured to:
receive: i) a description of a training workload; and ii) an identifier of an edge location for migrating the training workload; and
train the machine learning algorithm based on the description of a training workload as a training input for the machine learning algorithm and the identifier of the edge location for migrating the training workload as a training output for the machine learning algorithm.
22 . The system of claim 21 , wherein the training comprises adjusting a configuration of the machine learning algorithm so as to decrease an outage time objective.
23 . The system of claim 21 , wherein the training comprises adjusting a configuration of the machine learning algorithm so as to decrease a difference between the identifier of the edge location for migrating the training workload and a second identifier that is output by the machine learning component.
24 . The system of claim 21 , wherein the machine learning algorithm comprises a neural network and wherein the training is based on a loss function for a gradient of the neural network.
25 . The system of claim 21 , wherein the description of the training workload comprises a time series of a plurality of variables.