Patent ID: 11954526
Assignee: GUANGDONG UNIVERSITY OF PETROCHEMICAL TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 6:
7. A system comprising:
a training and optimization module, configured to train and optimize a plurality of parallel deep neural networks (DNN) by using a training data set to obtain a plurality of trained and optimized parallel DNNs, wherein the training data set comprises training state spaces and training action decisions that are in one-to-one correspondence, each training state space comprises a plurality of attribute groups in a plurality of queues arranged in sequence for a task, and each attribute group comprises a data amount of the task and a number of CPU cycles required by the task;
an action decision obtaining module, configured to input a target state space into the plurality of trained and optimized parallel DNNs to obtain a plurality of target action decisions; and
a sample storage module, configured to store the target state space and an optimal action decision as a sample in a replay memory, wherein the optimal action decision is based on a reward function and is one of the plurality of target action decisions, and wherein the reward function adjusts a reward value proportion of a task delay and a reward value proportion of energy consumed to minimize a sum of the task delay and the energy consumed, wherein the energy consumed is an amount of energy consumed by a computation process of the task and a transmission process of the task, wherein the task delay is an amount of time consumed by the computation process and the transmission process, wherein a plurality of task attribute groups in the target state space is scheduled on a plurality of computer clusters based on the optimal action decision, and wherein the action decision obtaining module and the sample storage module are executed repeatedly until the number of samples in the replay memory reaches a threshold.