Method, electronic device, and computer program product for information-centric networking

An electronic device, comprising:

• at least one processor;
• and
• at least one memory storing computer-executable instructions, the at least one memory and the computer-executable instructions being configured to cause, together with the at least one processor, the electronic device to perform operations comprising:
• performing forward processing on a first state obtained from information-centric networking (ICN) at a first moment using a memory layer in a machine learning model, and determining a forward hidden state associated with a memory layer corresponding to the first moment, wherein the first state comprises first node information and first topological information about the ICN;
• performing backward processing on a second state obtained from the ICN at a second moment using the memory layer, and determining a backward hidden state associated with a memory layer corresponding to the second moment, the second moment being later than the first moment;
• determining a third state at the second moment using the forward hidden state and the backward hidden state;
• and
• training the machine learning model using the second state and the third state.

A computer program product that is tangibly stored on a non-transitory computer-readable medium and comprises computer-executable instructions, wherein the computer-executable instructions, when executed by a device, cause the device to perform operations comprising:

• performing forward processing on a first state obtained from information-centric networking (ICN) at a first moment using a memory layer in a machine learning model, and determining a forward hidden state associated with a memory layer corresponding to the first moment, wherein the first state comprises first node information and first topological information about the ICN;
• performing backward processing on a second state obtained from the ICN at a second moment using the memory layer, and determining a backward hidden state associated with a memory layer corresponding to the second moment, the second moment being later than the first moment;
• determining a third state at the second moment using the forward hidden state and the backward hidden state;
• and
• training the machine learning model using the second state and the third state.