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Patent US11063881
Issued 2021-07-13
Methods And Apparatus For Network Delay And Distance Estimation, Computing Resource Selection, And Related Techniques
The techniques described herein relate to methods, apparatus, and computer readable media configured to select a computing resource from a plurality of computing resources to perform a computing process. A request is received from a remote computing device to perform the computing process. A first set of estimated metrics is accessed that includes an estimated metric for each computing resource and the first remote computing device. The second data is processed using a machine learning algorithm to select a candidate computing resource to perform the process. The machine learning algorithm selects the candidate computing resource based on a second estimated metric between at least one second remote computing device and an associated computing resource from the plurality of computing resources performing a second computing process for the at least one second remote computing device, and a capacity of each computing resource of the plurality of computing resources.
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- 1. A computerized method for selecting a computing resource from a plurality of computing resources to perform a computing process, the method comprising:
receiving, from a first remote computing device, first data indicative of a request to perform the computing process; accessing second data indicative of a first set of estimated metrics comprising, for each computing resource of the plurality of computing resources, a first estimated metric between the first remote computing device and the computing resource, wherein:
the first estimated metric comprises an estimated delay between the first remote computing device and the computing resource or an estimated distance between the first remote computing device and the computing resource; and
the first estimated metric is computed using a trained machine learning model that takes as input identifying information for the first remote computing device and the computing resource to determine the first estimated metric; and
processing the second data using a machine learning algorithm to select a candidate computing resource from the plurality of computing resources to perform the process, wherein:
the machine learning algorithm selects the candidate computing resource based on:
a second estimated metric between at least one second remote computing device and an associated computing resource from the plurality of computing resources performing a second computing process for the at least one second remote computing device; and
a capacity of each computing resource of the plurality of computing resources; and
processing the second data using the machine learning algorithm comprises processing the second data using a q-learning algorithm, comprising:
executing, for at least a subset of the plurality of computing resources, a reward function to determine a reward value for each computing resource of the subset of computing resources, comprising computing, based on the reward function, data indicative of a quality for each computing resource of the subset of computing resources; and
selecting the candidate computing resource from the subset of computing resources based on the determined reward values.
- 16. A non-transitory computer-readable media comprising instructions that, when executed by one or more processors on a computing device, are operable to cause the one or more processors to select a computing resource from a plurality of computing resources to perform a computing process, comprising:
receiving, from a first remote computing device, first data indicative of a request to perform the computing process; accessing second data indicative of a first set of estimated metrics comprising, for each computing resource of the plurality of computing resources, a first estimated metric between the first remote computing device and the computing resource, wherein: the first estimated metric comprises an estimated delay between the first remote computing device and the computing resource or an estimated distance between the first remote computing device and the computing resource; and the first estimated metric is computed using a trained machine learning model that takes as input identifying information for the first remote computing device and the computing resource to determine the first estimated metric; and processing the second data using a machine learning algorithm to select a candidate computing resource from the plurality of computing resources to perform the process, wherein:
the machine learning algorithm selects the candidate computing resource based on:
a second estimated metric between at least one second remote computing device and an associated computing resource from the plurality of computing resources performing a second computing process for the at least one second remote computing device; and
a capacity of each computing resource of the plurality of computing resources; and
processing the second data using the machine learning algorithm comprises processing the second data using a q-learning algorithm, comprising:
executing, for at least a subset of the plurality of computing resources, a reward function to determine a reward value for each computing resource of the subset of computing resources, comprising computing, based on the reward function, data indicative of a quality for each computing resource of the subset of computing resources; and
selecting the candidate computing resource from the subset of computing resources based on the determined reward values.
- 17. A system comprising a memory storing instructions, and a processor configured to execute the instructions to select a computing resource from a plurality of computing resources to perform a computing process by performing:
receiving, from a first remote computing device, first data indicative of a request to perform the computing process; accessing second data indicative of a first set of estimated metrics comprising, for each computing resource of the plurality of computing resources, a first estimated metric between the first remote computing device and the computing resource, wherein:
the first estimated metric comprises an estimated delay between the first remote computing device and the computing resource or an estimated distance between the first remote computing device and the computing resource; and
the first estimated metric is computed using a trained machine learning model that takes as input identifying information for the first remote computing device and the computing resource to determine the first estimated metric; and
processing the second data using a machine learning algorithm to select a candidate computing resource from the plurality of computing resources to perform the process, wherein:
the machine learning algorithm selects the candidate computing resource based on:
a second estimated metric between at least one second remote computing device and an associated computing resource from the plurality of computing resources performing a second computing process for the at least one second remote computing device; and
a capacity of each computing resource of the plurality of computing resources; and
processing the second data using the machine learning algorithm comprises processing the second data using a q-learning algorithm, comprising:
executing, for at least a subset of the plurality of computing resources, a reward function to determine a reward value for each computing resource of the subset of computing resources, comprising computing, based on the reward function, data indicative of a quality for each computing resource of the subset of computing resources; and
selecting the candidate computing resource from the subset of computing resources based on the determined reward values.
