Container cluster resource scheduling method and system based on deep reinforcement learning
A container cluster and reinforcement learning technology, applied in neural learning methods, resource allocation, instruments, etc.
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Embodiment 1
[0054] see Figure 1-Figure 2 , this embodiment proposes a container cluster resource scheduling method based on deep reinforcement learning, including the following steps:
[0055] S1: Establish a deep reinforcement learning agent;
[0056] S2: When there is a container cluster node that can allocate resources to meet the resource request of a task to be scheduled in the task queue, input the resource usage status of the container cluster node and the characteristic value of the task to be scheduled into the deep reinforcement learning agent, and get Action probability distribution for task scheduling;
[0057] S3: The container cluster scheduler schedules the tasks to be scheduled to the container cluster nodes for execution according to the action probability distribution of the task scheduling, calculates rewards, and updates the network parameters of the deep reinforcement learning agent according to the rewards;
[0058] S4: Repeat S2 to S3 to train the deep reinforcem...
Embodiment 2
[0062] This embodiment proposes a container cluster resource scheduling method based on deep reinforcement learning, including:
[0063] S1: Build a deep reinforcement learning agent.
[0064] In this embodiment, the deep reinforcement learning agent interacts with the system environment, and it needs to take action, that is, select a task from the task queue, instruct the container cluster scheduler to schedule the task to the corresponding container cluster node for execution, and pass Observe the environment and internal state to maximize long-term reward. More specifically, assume that the initial maintenance state of the deep reinforcement learning agent is s 0 , when there is a node that can allocate resources to meet the resource requirements of a task to be scheduled in the task queue, the deep reinforcement learning agent and the scheduling environment will interact. When the c-th scheduling is performed, the maintenance state of the deep reinforcement learning agen...
Embodiment 4
[0143] see Figure 4 , this embodiment proposes a container cluster resource scheduling system based on deep reinforcement learning, including a deep reinforcement learning agent, a container cluster scheduler, and an optimization module.
[0144] In the specific implementation process, when there is an allocatable resource of a container cluster node that meets the resource request of a task to be scheduled in the task queue, the resource usage status of the container cluster node and the characteristic value of the task to be scheduled are input into deep reinforcement learning In the agent, the policy network of the deep reinforcement learning agent outputs the action probability distribution of task scheduling. The container cluster scheduler schedules the tasks to be scheduled to the corresponding container cluster nodes according to the action probability distribution, the optimization module calculates rewards, updates the network parameters of the deep reinforcement le...
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