2results about How to "Guaranteed resource utilization" patented technology

According to the heterogeneous computing network resource abnormity monitoring and early warning method, unified modeling, unified acquisition processing and unified monitoring are carried out on heterogeneous computing network resources, so that mutual separation of monitoring systems can be avoided, root cause positioning can be assisted, abnormity checking efficiency can be improved, abnormity influence range deduction is carried out based on root causes and topology, and the method has the advantages of being high in practicability and the like. The method is favorable for defining the influence range of exception on tenant services, and comprises the following steps: step 1, carrying out unified collection and processing on heterogeneous resource monitoring data based on a unified monitoring model and an index processing rule; 2, monitoring index anomaly detection is carried out based on an index monitoring threshold value; 3, searching a multi-index anomaly joint detection rule, performing joint detection analysis on a single anomaly monitoring index in combination with the resource topology, and if a matching rule does not exist, directly outputting an anomaly index; 4, auditing resource topology, inferring an abnormal influence range and outputting an inference result; and step 5, performing unified abnormity early warning according to the inference result.

The application discloses a device resource application method and device, an electronic device and a storage medium. The method comprises the following steps: determining a single container demand quantity for a GPU resource card indicated by a task request in a current task request set; determining a whole machine card quantity of a GPU device in a device cluster; based on the whole machine card quantity and the single container demand quantity indicated by the task request, constructing a task request group in which the sum of the indicated single container demand quantities matches the whole machine card quantity, so as to apply for corresponding GPU device resources based on the task request group. The application can guarantee that the whole machine resources are applied to the device as much as possible at the task request level, thereby reducing the GPU resource cards in the form of fragments, effectively guaranteeing the device resource utilization rate and load balancing. Especially for the case where the task request is sufficient, the task execution efficiency can be improved.