Real-time resource allocation framework

A method comprising:

• identifying, via a crawler, changed first executable code stored in a version control system that is associated with a first service of a plurality of services;
• identifying, automatically by a natural language processing engine, a plurality of atomic code blocks from the first executable code, wherein the plurality of atomic code blocks, when executed, cause the first service to be executed;
• predicting, by a machine learning-based prediction algorithm, values for a plurality of computing resource parameters corresponding to computing resources consumed by each atomic code block of the plurality of atomic code blocks;
• storing, in a smart contract associated with the first service, the computing resource parameter values;
• based on approval of the smart contract:
• pre-loading cache information based on runtime information associated with each atomic code block;
• executing, by a computing device, the first service, wherein computing resources for the first service are allocated based on a sequence of operation of the atomic code blocks and the predicted computing resource parameter values;
• generating, in a blockchain associated with the first service, one or more new blocks storing information corresponding to actual runtime computing resources consumed by each atomic code block when executing the first service;
• and
• adapting, based on the one or more new blocks, the machine learning-based prediction algorithm to improve prediction for computing resources consumed by each atomic code block of the first service.

A computing device, comprising:

• a processor;
• and
• non-transitory computer readable media storing instructions that, when executed by the processor, cause the computing device to:
• identify, in a version control system, first executable code associated with a first service of a plurality of services;
• identify, automatically by a natural language processing engine, a plurality of atomic code blocks from the first executable code, wherein the plurality of atomic code blocks, when executed by the processor, cause the first service to be executed;
• predict, by a machine learning-based prediction algorithm, values for a plurality of computing resource parameters corresponding to computing resources consumed by each atomic code block of the plurality of atomic code blocks;
• store, in a smart contract associated with the first service, the computing resource parameter values;
• based on approval of the smart contract:
• pre-load cache information based on runtime information associated with each atomic code block;
• execute, by a computing device, the first service, wherein computing resources for the first service are allocated based on a sequence of operation of the atomic code blocks and the predicted computing resource parameter values;
• generate, in a blockchain associated with the first service, one or more new blocks storing information corresponding to actual runtime computing resources consumed by each atomic code block when executing the first service;
• and
• adapt, based on the one or more new blocks, the machine learning-based prediction algorithm to improve prediction for computing resources consumed by each atomic code block of the first service.