Abstract: The present disclosure provides a multi-queue multi-cluster task scheduling method and system. The method includes: constructing a training data set; training and optimizing a plurality of parallel deep neural networks (DNN) by using the training data set to obtain a plurality of trained and optimized parallel DNNs; setting a reward function, where the reward function minimizes the sum of a task delay and energy consumption by adjusting a reward value proportion of the task delay and a reward value proportion of the energy consumption; inputting a to-be-scheduled state space into the plurality of trained and optimized parallel DNNs to obtain a plurality of to-be-scheduled action decisions; determining an optimal action decision among the plurality of to-be-scheduled action decisions based on the reward function for output; and scheduling the plurality of task attribute groups to a plurality of clusters based on the optimal action decision.

Abstract: The present disclosure provides a multi-queue multi-cluster task scheduling method and system, and relates to the technical field of cloud computing. The method includes: constructing a training data set; training and optimizing a plurality of parallel deep neural networks (DNN) by using the training data set to obtain a plurality of trained and optimized parallel DNNs; setting a reward function, where the reward function minimizes the sum of a task delay and energy consumption by adjusting a reward value proportion of the task delay and a reward value proportion of the energy consumption; inputting a to-be-scheduled state space into the plurality of trained and optimized parallel DNNs to obtain a plurality of to-be-scheduled action decisions; determining an optimal action decision among the plurality of to-be-scheduled action decisions based on the reward function for output; and scheduling the plurality of task attribute groups to a plurality of clusters based on the optimal action decision.

Abstract: The edge-cloud collaboration platform for intelligent coking monitoring of cracking furnace tubes includes an edge layer and a cloud layer, which can store and analyze big data, propose suggestions on optimization and improvement, and feed the suggestions back to the edge layer. The edge layer includes an intelligent temperature measuring device for an outer surface of a cracking furnace tube and/or an ethylene DCS/data acquisition device; the cloud layer includes a cracking furnace safety warning device, an intelligent coking diagnosis and prediction device for a cracking furnace tube, a hybrid job scheduling device, a multi-workflow scheduling device, a virtualized resource scheduling device, and a virtual resource optimization device; and the intelligent temperature measuring device for an outer surface of a cracking furnace tube includes an identification device for furnace tube and overlapped tube, and an abnormal data detection device.

Abstract: The edge-cloud collaboration platform for intelligent coking monitoring of cracking furnace tubes includes an edge layer and a cloud layer, which can store and analyze big data, propose suggestions on optimization and improvement, and feed the suggestions back to the edge layer. The edge layer includes an intelligent temperature measuring device for an outer surface of a cracking furnace tube and/or an ethylene DCS/data acquisition device; the cloud layer includes a cracking furnace safety warning device, an intelligent coking diagnosis and prediction device for a cracking furnace tube, a hybrid job scheduling device, a multi-workflow scheduling device, a virtualized resource scheduling device, and a virtual resource optimization device; and the intelligent temperature measuring device for an outer surface of a cracking furnace tube includes an identification device for furnace tube and overlapped tube, and an abnormal data detection device.

Abstract: The present invention discloses a cooperative scheduling method and system for a computing resource and a network resource of a container cloud platform. The method includes: obtaining a load value of a container in a physical machine of a data center; calculating a load margin of a current container; if the load margin of the current container is less than 0, generating a first container sequence; if the load margin of the current container is greater than 0, obtaining a load value of a next container managed by a current physical machine, calculating a load margin of the next container, and updating the calculated load margin of the next container to the load margin of the current container. According to the method and the system of the present invention, resource utilization can be effectively improved.

Abstract: The present invention discloses a cooperative scheduling method and system for a computing resource and a network resource of a container cloud platform. The method includes: obtaining a load value of a container in a physical machine of a data center; calculating a load margin of a current container; if the load margin of the current container is less than 0, generating a first container sequence; if the load margin of the current container is greater than 0, obtaining a load value of a next container managed by a current physical machine, calculating a load margin of the next container, and updating the calculated load margin of the next container to the load margin of the current container. According to the method and the system of the present invention, resource utilization can be effectively improved.