Architecture for quantifying avoided carbon emissions in a digital engineering platform
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for quantifying avoided carbon emissions. The approach includes receiving, from a client device in communication with a digital platform, a request to execute a task by the digital platform. A token is assigned by the digital platform, the token is configured to uniquely identify the received request to execute the task using the digital platform. The digital platform receives a first value representing a baseline execution cost associated with executing the task. The digital platform determines, using the assigned token and based on execution of the task using the digital platform, a second value representing a reduction in execution cost attained by using the digital platform. The digital platform generates, based on a comparison of the second value to the first value, an attained execution reduction cost associated with the execution of the task by the digital platform.
1. A computer-implemented method comprising:
receiving, from a client device in communication with a digital platform, a request to execute a task by the digital platform;
assigning, by the digital platform, a token to the received request, wherein the token is configured to uniquely identify the received request to execute the task using the digital platform, wherein the token is an idempotency token configured to identify one or more tasks having operations common to an operation of the task for the received request;
receiving, by the digital platform, a first value representing a baseline execution cost associated with executing the task;
determining, using the assigned token and based on execution of the task using the digital platform, a second value representing a reduction in execution cost attained by using the digital platform;
generating, based on a comparison of the second value to the first value, an attained execution reduction cost associated with the execution of the task by the digital platform;
identifying one or more tasks each having an operation common to an operation of the task for the received request; and
executing, by the digital platform, the operation of the identified one or more tasks that is common to the operation of the task for the received request to reduce execution redundancy by the digital platform.
2. The computer-implemented method of claim 1 , further comprising:
determining the second value representing the reduced execution cost attained by using the digital platform based on the execution of the operation of the identified one or more tasks that is common to the operations of the task for the received request.
3. The computer-implemented method of claim 1 , further comprising:
generating, based on the attained execution reduction cost associated with the execution of the task by the digital platform, a certified carbon credit; and
providing the certified carbon credit to a computing device in communication with the digital platform.
4. The computer-implemented method of claim 1 , wherein the task is one or more of (i) an inference task, or (ii) a training task, of a digital model, and wherein the digital platform is configured to monitor execution of the task for a period of time.
5. The computer-implemented method of claim 4 , wherein the digital model is one or more of (i) a machine learning model, or (ii) a model configured to apply artificial intelligence techniques.
6. The computer-implemented method of claim 1 , further comprising:
generating, based on the request to execute the task, a digital thread of the digital platform, wherein the digital thread comprises a plurality of connected modules.
7. The computer-implemented method of claim 6 , further comprising:
monitoring, by the digital platform, an update to a module from the plurality of connected modules of the digital thread; and
determining, based on the update to the module, a cost impact to one or more other modules from the plurality of connected modules of the digital thread.
8. The computer-implemented method of claim 7 , wherein the token is configured to:
collect data related to one or both of (i) the update to the module, or (ii) the cost impact to the one or more other modules; and
determine, based on the collected data, a third value representing an additional reduction in execution cost attained by using the digital platform.
9. The computer-implemented method of claim 1 , wherein the digital platform is configured to utilize an engine configured to determine a cost associated with performing a workflow by the digital platform, wherein determining the cost comprises applying at least one of (i) machine learning or (ii) artificial intelligence techniques, to data related to the execution of the task collected by the token.
10. The computer-implemented method of claim 1 , wherein the token that uniquely identifies the received request comprises a unique identifier for monitoring the task by the digital platform.
11. The computer-implemented method of claim 1 , wherein the attained execution reduction cost is (i) a computational processing cost, or (ii) an amount of carbon emissions reduced.
12. The computer-implemented method of claim 1 , further comprising:
in response to receiving the request to execute the task using the digital platform, selecting, a set of tools for executing the task using the digital platform;
generating, by the digital platform, a set of tokens for the execution of the task on the digital platform, wherein each token of the set of tokens comprises data that identifies the selected tool for the set of tools;
associating, by the digital platform, the token for the received request to the set of tokens for the execution of the task using the digital platform; and
determining, by the set of tokens, the attained execution reduction cost associated with the execution of the task on the digital platform.
13. The computer-implemented method of claim 12 , wherein selecting the tool for executing the task by the digital platform comprises:
identifying, by the digital platform and from a tools database, a set of tools;
for each tool of the set of tools, identifying by the digital platform, a cost associated with performing the execution of the task using the tool, wherein the cost is at least one of (i) an amount of carbon emissions, or (ii) a computational load of the digital platform; and
selecting, by the digital platform, the tool whose cost associated with performing the execution of the task by the digital platform satisfies a threshold value.
14. The computer-implemented method of claim 1 , further comprising:
generating, using the digital platform and for a plurality of tasks, a plurality of values corresponding to the plurality of tasks, each of the plurality of values representing an attained execution reduction cost associated with the execution by the digital platform of the respective task from the plurality of tasks.
15. A system, comprising:
one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
receiving, from a client device in communication with a digital platform, a request to execute a task by the digital platform;
assigning, by the digital platform, a token to the received request, wherein the token is configured to uniquely identify the received request to execute the task using the digital platform, wherein the token is an idempotency token configured to identify one or more tasks having operations common to an operation of the task for the received request;
receiving, by the digital platform, a first value representing a baseline execution cost associated with executing the task;
determining, using the assigned token and based on execution of the task using the digital platform, a second value representing a reduction in execution cost attained by using the digital platform;
generating, based on a comparison of the second value to the first value, an attained execution reduction cost associated with the execution of the task by the digital platform;
identifying one or more tasks each having an operation common to an operation of the task for the received request; and
executing, by the digital platform, the operation of the identified one or more tasks that is common to the operation of the task for the received request to reduce execution redundancy by the digital platform.
16. The system of claim 15 , wherein the digital platform is configured to utilize an engine configured to determine a cost associated with performing a workflow by the digital platform, wherein determining the cost comprises applying at least one of (i) machine learning or (ii) artificial intelligence techniques, to data related to the execution of the task collected by the token.
17. The system of claim 15 , the operations further comprising:
generating, based on the request to execute the task, a digital thread of the digital platform, wherein the digital thread comprises a plurality of connected modules.
18. The system of claim 17 , the operations further comprising:
monitoring, by the digital platform, an update to a module from the plurality of connected modules of the digital thread; and
determining, based on the update to the module, a cost impact to one or more other modules from the plurality of connected modules of the digital thread.
19. The system of claim 18 , wherein the token is configured to:
collect data related to one or both of (i) the update to the module, or (ii) the cost impact to the one or more other modules; and
determine, based on the collected data, a third value representing an additional reduction in execution cost attained by using the digital platform.
20. The system of claim 15 , wherein the task is one or more of (i) an inference task, or (ii) a training task, of a digital model, and wherein the digital platform is configured to monitor execution of the task for a period of time.
21. The system of claim 15 , the operations further comprising:
generating, based on the attained execution reduction cost associated with the execution of the task by the digital platform, a certified carbon credit; and
providing the certified carbon credit to a computing device in communication with the digital platform.
22. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:
receiving, from a client device in communication with a digital platform, a request to execute a task by the digital platform;
assigning, by the digital platform, a token to the received request, wherein the token is configured to uniquely identify the received request to execute the task using the digital platform, wherein the token is an idempotency token configured to identify one or more tasks having operations common to an operation of the task for the received request;
receiving, by the digital platform, a first value representing a baseline execution cost associated with executing the task;
determining, using the assigned token and based on execution of the task using the digital platform, a second value representing a reduction in execution cost attained by using the digital platform;
generating, based on a comparison of the second value to the first value, an attained execution reduction cost associated with the execution of the task by the digital platform;
identifying one or more tasks each having an operation common to an operation of the task for the received request; and
executing, by the digital platform, the operation of the identified one or more tasks that is common to the operation of the task for the received request to reduce execution redundancy by the digital platform.
23. The non-transitory computer-readable medium of claim 22 , wherein the digital platform is configured to utilize an engine configured to determine a cost associated with performing a workflow by the digital platform, wherein determining the cost comprises applying at least one of (i) machine learning or (ii) artificial intelligence techniques, to data related to the execution of the task collected by the token.
24. The non-transitory computer-readable medium of claim 22 , the operations further comprising:
generating, based on the request to execute the task, a digital thread of the digital platform, wherein the digital thread comprises a plurality of connected modules.
25. The non-transitory computer-readable medium of claim 24 , the operations further comprising:
monitoring, by the digital platform, an update to a module from the plurality of connected modules of the digital thread; and
determining, based on the update to the module, a cost impact to one or more other modules from the plurality of connected modules of the digital thread.
26. The non-transitory computer-readable medium of claim 25 , wherein the token is configured to:
collect data related to one or both of (i) the update to the module, or (ii) the cost impact to the one or more other modules; and
determine, based on the collected data, a third value representing an additional reduction in execution cost attained by using the digital platform.
27. The non-transitory computer-readable medium of claim 22 , wherein the task is one or more of (i) an inference task, or (ii) a training task, of a digital model, and wherein the digital platform is configured to monitor execution of the task for a period of time.
28. The non-transitory computer-readable medium of claim 22 , the operations further comprising:
generating, based on the attained execution reduction cost associated with the execution of the task by the digital platform, a certified carbon credit; and
providing the certified carbon credit to a computing device in communication with the digital platform.