4 results about "Application performance management" patented technology

The embodiment of the invention provides an application performance intelligent diagnosis method and system, and belongs to the technical field of application performance detection. The method comprises the following steps: constructing a standardized data set based on performance data collected by an application performance management tool; inputting the standardized data set into a tool capability graph constructed by a model context protocol, and establishing a mapping index from data to a tool; analyzing performance abnormity or a diagnosis request triggered by the standardized data set based on a large language model, selecting and calling a diagnosis tool according to the mapping index, and executing a dynamic diagnosis plan to obtain an analysis result; multiple rounds of reasoning and confidence evaluation are carried out based on the analysis result, the tool calling sequence is adjusted according to the calling characteristics, and the diagnosis plan is updated until a root cause diagnosis conclusion is output; and generating a diagnosis evidence packet based on the root cause diagnosis conclusion, and outputting the diagnosis evidence packet as a final diagnosis result. According to the scheme of the invention, high-precision, traceable and self-adaptive evolution performance root cause intelligent identification is realized in a complex application environment.

Systems, computer program products, and methods for an automated mesh service-based deployment intelligence system are provided. This system is designed to streamline the deployment of software by integrating a processing device and a non-transitory storage device. The storage device contains instructions that, when executed by the processing device, enable the ingestion of data from various sources such as monitoring systems, databases, and application performance management tools. Once ingested, the data is stored and processed to discern deployment patterns and detect any anomalies. Utilizing a machine learning model, the system anticipates potential deployment issues by analyzing this data. It then orchestrates the deployment of software artifacts accordingly, taking into account the insights gained from the machine learning model. Furthermore, the system is capable of real-time optimization of the deployment process to preemptively resolve any predicted issues, thereby enhancing the efficiency and reliability of software deployment operations.

The invention provides a cloud-based mobile phone application performance management system and method and electronic equipment, and the management system comprises a cloud virtualization module which is used for obtaining a virtual mobile phone instance group comprising a plurality of cloud virtual mobile phone instances based on cloud server resources; the dynamic priority module is used for acquiring task execution sequence information based on the data flow information of the mobile phone application and the resource demand of each task in the mobile phone application; the real-time monitoring module is used for acquiring abnormal performance state information of the mobile phone application based on the task execution sequence information and the performance parameters of the mobile phone application; and the task scheduling module is used for obtaining a task optimization scheduling scheme of the mobile phone application based on the abnormal performance state information, the task execution sequence information and the virtual mobile phone instance group. According to the method and the device, the running fluency, stability and efficiency of the mobile phone application can be remarkably improved, and better use experience is brought to a user.

The application relates to a public cloud multi-tenant performance monitoring method, device, equipment, storage medium and program product. The method comprises the following steps: collecting a plurality of low-level indexes of a cloud application runtime on a host of a target virtual machine node and a target virtual machine; the low-level indexes comprise at least one of CPU utilization, a hardware counter, a host resource state, resource usage characteristics, a hardware event vector and interference intensity; inputting the plurality of low-level indexes into a proxy model for performance evaluation to obtain a first evaluation result; the first evaluation result comprises a target performance degradation estimation value, a performance degradation reason of the cloud application and a contribution degree of the degradation reason; the target performance degradation estimation value is used for representing a degradation degree of the cloud application; and performing degradation evaluation according to the first evaluation result to obtain a second evaluation result. The above method improves the resource utilization rate and cost efficiency of the public cloud infrastructure, and further enhances the foresight and accuracy of cloud application performance management.