Cloud-based mobile phone application performance management system and method and electronic equipment

Abstract

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.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of performance management, in particular to a cloud-based mobile application performance management system, method and electronic device. BACKGROUND

[0002] With the popularity of smartphones, people increasingly rely on mobile applications in communication, social interaction, entertainment and other activities. Improving the management of mobile application performance can provide users with a smooth, stable and efficient user experience.

[0003] However, traditional mobile application performance management systems lack flexibility in resource allocation and are difficult to dynamically adjust resources according to task requirements, resulting in insufficient resources for critical tasks and affecting user experience. SUMMARY

[0004] The present disclosure provides a cloud-based mobile application performance management system, method and electronic device to address the problem of lack of flexibility in resource allocation in traditional mobile application performance management systems, difficulty in dynamically adjusting resources according to task requirements, resulting in insufficient resources for critical tasks and affecting user experience.

[0005] In a first aspect, the present disclosure provides a cloud-based mobile application performance management system, comprising: a cloud virtualization module configured to obtain a virtual mobile instance group comprising a plurality of cloud virtual mobile instances based on cloud server resources; a dynamic priority module configured to obtain task execution order information based on data flow information of the mobile application and resource requirements of each task in the mobile application; a real-time monitoring module configured to obtain abnormal performance state information of the mobile application based on the task execution order information and performance parameters of the mobile application; a task scheduling module configured to obtain a task optimization scheduling scheme for the mobile application based on the abnormal performance state information, the task execution order information and the virtual mobile instance group.

[0006] In addition, according to the mobile application performance management system of the first aspect of the present disclosure, the system further comprises: a load prediction module configured to obtain predicted resource requirement data of the virtual mobile instance group based on the task optimization scheduling scheme, the virtual mobile instance group and historical load data of the virtual mobile instance group; a cooperative running module configured to establish a synchronous connection between the mobile terminal and the cloud based on the predicted resource requirement data and obtain task cooperation information of the mobile application between the mobile terminal and the cloud.

[0007] In addition, according to the cloud-based mobile application performance management system of the first aspect of the present disclosure, the cloud virtualization module comprises: The virtual phone creation submodule is used to establish the virtual environment configuration information of cloud virtual phone instances based on cloud server resources, and to obtain multiple cloud virtual phone instances; The resource isolation submodule is used to isolate multiple cloud virtual mobile phone instances based on virtual environment configuration information and resource requirement information of multiple cloud virtual mobile phone instances, and to obtain the resource isolation status of multiple cloud virtual mobile phone instances. The instance group creation submodule is used to obtain a virtual mobile phone instance group containing multiple cloud virtual mobile phone instances based on the resource isolation status of multiple cloud virtual mobile phone instances.

[0008] Furthermore, according to the cloud-based mobile application performance management system of the first aspect of this disclosure, the dynamic priority module includes: The resource sorting submodule is used to obtain resource consumption characteristic data corresponding to each task in the mobile application based on the data flow information of the mobile application. Based on the resource consumption characteristic data, it calculates the resource usage frequency and resource ratio of each task for each type of resource. Based on the resource usage frequency and resource ratio of each task for each type of resource, it prioritizes the various types of resources consumed by each task and obtains the basic resource order table. The task sorting submodule is used to determine the resource requirements of each task based on the basic resource sequence list and the task sorting algorithm. Based on the resource requirements, urgency and processing order of each task, the sorting of each task is adjusted to generate a task sequence list. The priority determination submodule is used to obtain the dependencies between tasks based on the task sequence table, adjust the task sequence table based on the dependencies, and generate task execution order information.

[0009] Furthermore, according to the cloud-based mobile application performance management system of the first aspect of this disclosure, the real-time monitoring module includes: The performance parameter submodule is used to obtain information on changes in the performance parameters of a mobile application based on task execution order information. The threshold judgment submodule is used to compare the actual value of the performance parameter with the preset threshold based on the change information of the performance parameter, and obtain the threshold analysis result of the performance parameter. The anomaly detection submodule is used to obtain abnormal performance status information of mobile applications based on threshold analysis results.

[0010] Furthermore, according to the cloud-based mobile application performance management system of the first aspect of this disclosure, the task scheduling module includes: The reallocation submodule is used to obtain the resource allocation plan for each task of the mobile application based on abnormal performance status information, task execution order information, and virtual mobile phone instance group. The task adjustment submodule is used to adjust the order of tasks in the mobile application based on the resource allocation plan, and to assign each task to the corresponding virtual mobile phone instance, and obtain task allocation information. The matching analysis submodule is used to determine the resources and key tasks of key cloud virtual mobile phone instances based on task allocation information and virtual mobile phone instance groups, adjust the matching relationship between the resources and key tasks of key cloud virtual mobile phone instances, and obtain a task optimization scheduling scheme for mobile applications.

[0011] Furthermore, according to the cloud-based mobile application performance management system of the first aspect of this disclosure, the load prediction module includes: The historical analysis submodule is used to obtain historical load data of virtual mobile phone instance groups based on task optimization scheduling schemes and virtual mobile phone instance groups, and to obtain load analysis results of virtual mobile phone instance groups based on historical load data. The trend analysis submodule is used to determine the resource usage trend of virtual mobile phone instance groups based on the load analysis results; The prediction and identification submodule is used to obtain predicted resource demand data for virtual mobile phone instance groups based on resource usage trends.

[0012] Furthermore, according to the cloud-based mobile application performance management system of the first aspect of this disclosure, the collaborative operation module includes: The data synchronization submodule is used to establish a synchronization connection between the mobile device and the cloud based on the predicted resource demand data, adjust the synchronization frequency of the mobile application between the mobile device and the cloud, and establish the data synchronization status between the mobile device and the cloud. The mobile task optimization submodule is used to obtain the running status of mobile tasks based on the data synchronization status, adjust the running order of mobile tasks based on the running status of mobile tasks, and obtain the running status of mobile tasks. The allocation and matching submodule is used to optimize the allocation of tasks in the mobile application between the mobile device and the cloud based on the task execution status, and to obtain task collaboration information of the mobile application between the mobile device and the cloud.

[0013] Secondly, this disclosure provides a cloud-based mobile application performance management method, including: By utilizing cloud virtualization modules based on cloud server resources, a virtual phone instance group containing multiple cloud virtual phone instances can be obtained; The dynamic priority module is used to obtain task execution order information based on the data flow information of the mobile application and the resource requirements of each task in the mobile application; The real-time monitoring module is used to obtain abnormal performance status information of mobile applications based on task execution sequence information and performance parameters of mobile applications; The task scheduling module is used to obtain an optimized task scheduling scheme for mobile applications based on abnormal performance status information, task execution order information, and virtual mobile phone instance groups.

[0014] Thirdly, this disclosure provides an electronic device, including: a memory for storing computer-readable instructions; and a processor for executing the computer-readable instructions, causing the electronic device to perform the cloud-based mobile application performance management method of the second aspect.

[0015] This disclosure provides a cloud-based mobile application performance management system, method, and electronic device. This disclosure establishes virtual mobile phone instances in the cloud using cloud virtualization technology. It dynamically obtains task execution order information by utilizing the data flow information and task resource requirements of the mobile application. Furthermore, it uses the task execution order information and the performance parameters of the mobile application to obtain abnormal performance status information. Combining the abnormal performance status information, task execution order information, and virtual mobile phone instance groups, it dynamically adjusts the task optimization scheduling scheme of the mobile application. This effectively overcomes the limitations of rigid resource allocation in traditional systems, flexibly adapts to task requirements, and prioritizes resource supply for critical tasks. Ultimately, it significantly improves the smoothness, stability, and efficiency of mobile applications, bringing users a better user experience.

[0016] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0017] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 A functional block diagram of a cloud-based mobile application performance management system provided in this embodiment of the disclosure; Figure 2 A schematic diagram of a cloud-based mobile application performance management system framework provided in this embodiment of the disclosure; Figure 3 A flowchart of a cloud virtualization module provided in an embodiment of this disclosure; Figure 4 A flowchart of the dynamic priority module provided in the embodiments of this disclosure; Figure 5 A flowchart of the real-time monitoring module provided in the embodiments of this disclosure; Figure 6A flowchart of a task scheduling module provided in an embodiment of this disclosure; Figure 7 A flowchart of the load prediction module provided in the embodiments of this disclosure; Figure 8 A flowchart of the collaborative operation module provided in the embodiments of this disclosure; Figure 9 A flowchart illustrating a cloud-based mobile application performance management method provided in this embodiment of the disclosure; Figure 10 This is a hardware block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this disclosure more apparent, exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments of this disclosure. It should be understood that this disclosure is not limited to the exemplary embodiments described herein.

[0020] Performance management technology focuses on the performance and efficiency of systems, devices, or applications under various environments. This area involves the monitoring, analysis, optimization, and control of system resources. Performance management includes monitoring system resource usage, detecting potential performance bottlenecks, analyzing error logs and causes of failures, and optimizing resource allocation to ensure efficient system operation. Technologies in this field are typically used to maintain large computing systems, cloud computing platforms, network architectures, and complex application infrastructures.

[0021] Traditional mobile application performance management systems cannot track application performance in real time, leading to delayed detection of performance bottlenecks and impacting application stability. In terms of resource allocation, traditional systems lack flexibility, failing to dynamically adjust resources according to task requirements, resulting in insufficient resources for critical tasks. Regarding anomaly detection, existing technologies are slow to react to situations exceeding thresholds, causing application performance degradation. Furthermore, challenges exist in the collaborative operation of mobile applications and the cloud, easily leading to uneven resource allocation or disordered task execution, resulting in reduced application efficiency and consequently causing problems such as overheating and excessive power consumption, negatively impacting user experience.

[0022] To address the aforementioned technical issues, this disclosure provides a cloud-based mobile application performance management system, a solution for monitoring and managing the performance of mobile applications. By leveraging cloud computing resources, it provides real-time performance monitoring and analysis capabilities, helping developers and operations teams understand application performance across different environments and devices. The system's uses include identifying performance issues, optimizing application performance, and ensuring consistent and stable user experience. Furthermore, by running a simulated mobile phone in the cloud, it enables the migration of applications from physical phones to the cloud, resolving issues such as overheating, excessive power consumption, device loss, and cluster management problems associated with physical phones.

[0023] Figure 1 This is a functional block diagram of a cloud-based mobile application performance management system provided in an embodiment of this disclosure. Figure 1 As shown, the cloud-based mobile application performance management system includes at least: a cloud virtualization module 101, a dynamic priority module 102, a real-time monitoring module 103, a task scheduling module 104, a load prediction module 105, and a collaborative operation module 106.

[0024] The cloud virtualization module 101 creates cloud virtual phone instances based on cloud server resources, isolates the cloud virtual phone instances, monitors the resource usage of each virtual phone instance, and obtains a virtual phone instance group, which contains multiple cloud virtual phone instances.

[0025] The dynamic priority module 102 analyzes the resource usage of the mobile application task queue based on the data flow information of the mobile application, sets the corresponding processing priority according to the resource requirements of each task in the mobile application, and arranges each task according to the priority to generate task execution order information.

[0026] The real-time monitoring module 103 monitors multiple performance parameters of the mobile application based on task execution sequence information, records CPU usage, memory consumption and network transmission rate indicators, and makes judgments based on changes in performance parameters and preset thresholds to identify abnormal situations that exceed the thresholds and obtain abnormal performance status information.

[0027] Based on abnormal performance status information, task execution order information, and virtual mobile phone instance groups, the task scheduling module 104 reallocates resources for each task in the mobile application. According to the resource utilization status, the mobile application tasks are assigned to matching virtual mobile phone instances, optimizing resource acquisition for key tasks and obtaining a task optimization scheduling scheme for the mobile application.

[0028] The load prediction module 105 collects historical load data of virtual mobile phone instances in the cloud based on the task optimization scheduling scheme and the virtual mobile phone instance group, judges the resource usage trend of the virtual mobile phone instance group, analyzes the patterns of historical load data, predicts future resource demand, and obtains the predicted resource demand data of the virtual mobile phone instance group.

[0029] Based on predicted resource demand data and cloud resource status, the collaborative operation module 106 dynamically adjusts the task execution on the mobile terminal, coordinates the task allocation of mobile applications on the mobile terminal and the cloud, implements collaborative work between the mobile application and the cloud, optimizes the efficiency of the mobile application during operation, and obtains collaborative information between the mobile terminal and the cloud.

[0030] The virtual phone instance group includes the created virtual phone instances, resource isolation policies, and instance group management methods; task execution order information includes task execution priority, inter-task dependencies, and task processing order; abnormal performance status information includes abnormal values ​​of CPU utilization, memory consumption, and network transmission rate; task optimization scheduling scheme includes reallocated task resources, adjusted task order, and tasks scheduled to differentiated virtual machine instances; predicted resource data includes resource usage trends and predicted resource requirements; and collaborative information includes data synchronization between the cloud and the mobile device, the execution order of tasks on the mobile device, and the resource allocation method in the cloud.

[0031] In summary, the cloud-based mobile application performance management system provided in this disclosure establishes virtual mobile phone instances in the cloud using cloud virtualization technology. It dynamically obtains task execution order information by utilizing the data flow information and task resource requirements of the mobile application. Furthermore, it obtains abnormal performance status information of the mobile application by using the task execution order information and the performance parameters of the mobile application. Combining the abnormal performance status information, task execution order information, and virtual mobile phone instance groups, it dynamically adjusts the task optimization scheduling scheme of the mobile application. This effectively overcomes the limitations of rigid resource allocation in traditional systems, flexibly adapts to task requirements, and prioritizes resource supply for critical tasks. Ultimately, it significantly improves the smoothness, stability, and efficiency of mobile applications, bringing users a better user experience.

[0032] Figure 2 This is a schematic diagram of a cloud-based mobile application performance management system framework provided in an embodiment of this disclosure.

[0033] like Figure 2 As shown, the cloud-based mobile application performance management system includes: cloud virtualization module 101, dynamic priority module 102, real-time monitoring module 103, task scheduling module 104, load prediction module 105, and collaborative operation module 106.

[0034] Furthermore, the cloud virtualization module 101 includes a virtual mobile phone creation submodule 1011, a resource isolation submodule 1012, and an instance group creation submodule 1013.

[0035] Furthermore, the dynamic priority module 102 includes a resource sorting submodule 1021, a task sorting submodule 1022, and a priority determination submodule 1023.

[0036] Furthermore, the real-time monitoring module 103 includes a performance parameter submodule 1031, a threshold judgment submodule 1032, and an anomaly detection submodule 1033.

[0037] Furthermore, the task scheduling module 104 includes a reallocation submodule 1041, a task adjustment submodule 1042, and a matching analysis submodule 1043.

[0038] Furthermore, the load prediction module 105 includes a historical analysis submodule 1051, a trend analysis submodule 1052, and a prediction identification submodule 1053.

[0039] Furthermore, the collaborative operation module 106 includes a data synchronization submodule 1061, a mobile task optimization submodule 1062, and an allocation and matching submodule 1063.

[0040] To illustrate the technical solutions provided in this disclosure in detail, the following references are made. Figures 3-8 This disclosure describes in detail the various sub-modules of the cloud virtualization module 101, dynamic priority module 102, real-time monitoring module 103, task scheduling module 104, load prediction module 105, and collaborative operation module 106.

[0041] Figure 3 A flowchart of a cloud virtualization module provided in an embodiment of this disclosure.

[0042] like Figure 3 As shown, the cloud virtualization module's functionality is implemented through the following steps: Step 301: The virtual phone creation submodule is used to obtain multiple cloud virtual phone instances based on cloud server resources.

[0043] In one embodiment of this disclosure, the virtual phone creation submodule creates a computing environment for cloud virtual phone instances based on cloud server resources, performs initial configuration of multiple cloud virtual phone instances, sets the operating system, hardware parameters and key software components, configures network connections and security policies, establishes virtual environment configuration, and then establishes multiple cloud virtual phone instances.

[0044] For example, the virtual phone creation submodule, based on cloud server resources, determines the currently available computing resources, such as processors, storage space, and network bandwidth, through the resource manager. According to the resource allocation strategy, dedicated resources are allocated to each cloud-based virtual phone instance to be created, to balance the load and improve resource utilization. The formula for calculating the optimal resource allocation scheme is: "Required Resources = Estimated Load / Resource Pool Capacity". For each virtual phone instance, the operating system and necessary software are installed and configured, and network connections are set up, including IP address allocation and network access control lists, to ensure the network independence and security of each virtual phone instance. The network connection settings can be expressed by the formula: "Network Bandwidth Configuration = Number of Instances × Single Instance Requirements". These configurations are integrated to establish a virtual environment configuration, thereby creating multiple cloud-based virtual phone instances.

[0045] Step 302: The resource isolation submodule is used to isolate multiple cloud virtual mobile phone instances based on the virtual environment configuration information and the resource requirement information of multiple cloud virtual mobile phone instances, and to obtain the resource isolation status of multiple cloud virtual mobile phone instances.

[0046] In one embodiment of this disclosure, the resource isolation submodule, based on the virtual environment configuration (i.e., the creation rules of multiple cloud virtual phone instances), allocates independent CPU and memory resources according to the demand information of multiple cloud virtual phone instances, configures resource isolation strategies, optimizes and avoids resource interference between each cloud virtual phone instance, monitors resource usage, adjusts resource conflicts between multiple cloud virtual phone instances, and obtains the resource isolation status of multiple cloud virtual phone instances.

[0047] For example, the resource isolation submodule, based on the virtual environment configuration, analyzes the resource requirements of each cloud-based virtual phone instance, including the number of CPU cores, memory size, and storage requirements. Utilizing resource management algorithms, it dynamically adjusts resource allocation to ensure each instance receives the necessary resources without affecting other instances. The formula for adjusting resource allocation is: "Resource Adjustment Value = Current Resource Allocation + (Resource Requirement - Current Resource Allocation) × Adjustment Coefficient." To improve resource utilization, it monitors real-time resource usage and automatically adjusts resource allocation based on preset performance indicators. If resource conflicts or performance bottlenecks are detected, resources are reallocated, including increasing processor allocation or expanding memory capacity. Advanced isolation technologies, such as virtual LANs and storage access control, are applied to prevent data interference and security threats between instances, ensuring the stability and efficiency of the cloud-based virtual phone instances and obtaining the resource isolation status of multiple cloud-based virtual phone instances.

[0048] Step 303: The instance group creation submodule is used to obtain a virtual mobile phone instance group containing multiple cloud virtual mobile phone instances based on the resource isolation status of multiple cloud virtual mobile phone instances.

[0049] In one embodiment of this disclosure, based on the resource isolation status of multiple cloud virtual phone instances, network connections are configured, communication channels are established between each cloud virtual phone instance, communication quality is monitored, the network topology of the cloud virtual phone instances is adjusted, the connection between each instance is optimized, and a virtual phone instance group containing multiple cloud virtual phone instances is obtained.

[0050] For example, the instance group establishment submodule, based on resource isolation status, designs a network architecture to support efficient communication between cloud-based virtual phone instances, assesses the communication needs and network topology between instances, and then configures network routing and switching policies to ensure efficient and secure data transmission. Network configuration adjustments can be expressed as: "Network latency adjustment = current latency / latency threshold". It monitors network quality, adjusts network configuration in real time to address potential congestion and connectivity issues, and introduces intelligent routing algorithms to dynamically optimize data flow and reduce latency. For network security, it deploys encryption protocols and security authentication measures to protect data transmission from external access and attacks, and obtains a virtual phone instance group containing multiple cloud-based virtual phone instances.

[0051] In summary, according to the technical solutions provided in the embodiments of this disclosure, this disclosure creates multiple fully configured virtual mobile phone instances through cloud virtualization technology, avoids interference between instances by combining dynamic resource isolation strategies (such as independent resource allocation and conflict adjustment), and achieves efficient communication and security management by building instance groups, ultimately enhancing the flexibility, isolation and collaboration of cloud resource management.

[0052] Figure 4 A flowchart of the dynamic priority module provided in the embodiments of this disclosure.

[0053] like Figure 4 As shown, the implementation of this dynamic priority module includes the following steps: Step 401: The resource sorting submodule is used to obtain resource consumption characteristic data corresponding to each task in the mobile application based on the data flow information of the mobile application. Based on the resource consumption characteristic data, it calculates the resource usage frequency and resource ratio of each task for various resources. Based on the resource usage frequency and resource ratio of each task for various resources, it prioritizes the various resources consumed by each task and obtains the basic resource order table.

[0054] In one embodiment of this disclosure, the resource sorting submodule analyzes the resource usage of various tasks in multiple mobile applications based on the data flow information of the mobile application, statistically analyzes the data of resource consumption and resource demand (i.e., resource consumption characteristic data), calculates the frequency and proportion of resource usage of various resource types for each task (i.e., resource usage ratio), sorts the various types of resources consumed by each task by resource priority, and generates a basic resource order table.

[0055] For example, the resource sorting submodule obtains relevant data from the mobile application based on the application's data stream information. This data includes the application's runtime, required processor time, and memory consumption. It then statistically analyzes resource consumption and demand data, examining the resource usage of each task within the mobile application. Through this analysis, it calculates the resource usage frequency and resource percentage for various resource types. The formula for calculating resource usage frequency is: "Resource usage frequency = Resource usage time / Total time," and the calculation of resource percentage can be described as: "Resource usage percentage = Resource consumption / Total resources." Through these calculations, the module summarizes the usage of different resources consumed by each task and prioritizes these resources, generating a basic resource order table.

[0056] Step 402: The task sorting submodule is used to determine the resource requirements of each task based on the basic resource sequence list and the task sorting algorithm. Based on the resource requirements, urgency and processing order of each task, the sorting of each task is adjusted to generate a task sequence list.

[0057] In one embodiment of this disclosure, the task sorting submodule uses a task sorting algorithm based on a basic resource sequence list to analyze various tasks in the task queue of the mobile application, determine the resource requirements of each task, and sort and adjust each task according to the urgency and processing order of the tasks to generate a task sequence list.

[0058] For example, the task sorting submodule, based on the basic resource sequence list, uses a task sorting algorithm to analyze various tasks in the mobile application task queue, determine the resource requirements of each task, and determine them by calculating the processor time, memory size, and possible external resources involved in the task. It then combines the urgency and processing order of the tasks to sort and adjust them. The urgency can be determined by calculating the ratio of task latency to tolerance. Based on the indicators, the processing order in the task queue is adjusted to generate a task sequence list.

[0059] The task sorting algorithm used is based on the following formula:

[0060] Calculate the priority value for each task, where, This is a priority value used to measure the processing order of tasks within the entire task queue. The higher the priority value, the greater the priority of the task during execution. It indicates the urgency of a task and is used to measure the timeframe for its completion. This indicates the resource requirements of a task, reflecting the amount of resources needed during its execution, such as CPU and memory. Indicates the task execution time, used to show the time required for the task to complete. The importance of a task is indicated by its location on the phone and its dependence on other tasks. This is a weighting coefficient for urgency, used to adjust the impact of task urgency on the ranking. This is a weighting coefficient for resource requirements, used to adjust the importance of task resource requirements in the ranking process. This is a weighting factor for execution time, used to adjust the impact of execution time on task sorting. This is a weighting coefficient for task importance, used to adjust the impact of task importance on the overall ranking.

[0061] The specific execution process of the above formula is as follows: By analyzing the task's processing timeframe, we determine the timeframe within which the task needs to be completed and calculate its urgency. Measure the amount of resources required for the task, including CPU, memory, etc., and determine the resource requirements. Statistically analyze the average execution time of tasks to understand the length of time required to complete a task and calculate the execution time. The importance of a task is assessed based on its position within the overall collection system and its dependence on other tasks. Set weight coefficients The weighting coefficient is used to adjust the influence of each parameter in task ranking. The value of the weighting coefficient can be confirmed through historical task data analysis and expert presets. The priority value is then calculated using a formula to obtain the task priority.

[0062] Step 403: The priority determination submodule is used to obtain the dependencies between tasks based on the task sequence table, adjust the task sequence table based on the dependencies, and generate task execution order information.

[0063] In one embodiment of this disclosure, the priority determination submodule evaluates each task in the task queue based on the task sequence list, identifies the dependencies between tasks, adjusts the processing list of each task, and generates task execution order information.

[0064] For example, the priority determination submodule evaluates each task in the task queue based on the task sequence list, identifies the dependencies of each task with other tasks, and analyzes the coordination between the completion order of each task and the entire queue. Identifying dependencies between tasks can be achieved by analyzing the interrelationships between tasks, determining the tasks to be processed first, expressed as: "Degree of Relationship = Number of Related Tasks × Relationship Factor". The task processing list is then adjusted to ensure that the execution order of each task conforms to resource priority and task urgency, generating task execution order information. This ensures that the mobile application's tasks are executed in a reasonable order, optimizing resource utilization efficiency and overall task processing performance.

[0065] In summary, according to the technical solution provided in the embodiments of this disclosure, by first sorting the resources consumed by the tasks, and then dynamically adjusting the priority based on the task attributes (i.e., the urgency and processing order) and dependencies, it is ensured that critical tasks are processed first, the task execution order and resource matching efficiency are optimized, thereby improving the overall running performance of the tasks.

[0066] Figure 5 A flowchart of a real-time monitoring module provided in an embodiment of this disclosure.

[0067] like Figure 5 As shown, the real-time monitoring module's functionality includes the following steps: Step 501: The performance parameter submodule is used to obtain the change information of the performance parameters of the mobile application based on the task execution order information.

[0068] In one embodiment of this disclosure, the performance parameter submodule monitors performance parameters of the mobile application, such as CPU usage, memory consumption, and network transmission rate, based on the task execution order, records the changes of each performance parameter, and statistically analyzes the peak value, average value, and fluctuation range of each performance parameter to generate parameter record information, thereby obtaining the change information of each performance parameter.

[0069] For example, the performance parameter submodule monitors performance parameters of the mobile application, such as CPU usage, memory consumption, and network transmission rate, based on the task execution order. During monitoring, changes in each performance parameter are recorded periodically, with sampling occurring on a second-by-second basis, recording the real-time values ​​of each parameter. The peak value, mean value, and fluctuation range of each performance parameter are calculated. The mean value is calculated using the formula: "Mean value = Sum of all sampled data / Number of samples," and the fluctuation range is calculated using the formula: "Fluctuation range = Peak value - Minimum value." Based on the calculation results, performance parameter record information is generated, thereby obtaining information on the changes in each performance parameter.

[0070] Step 502: The threshold judgment submodule is used to compare the actual value of the performance parameter with the preset threshold based on the change information of the performance parameter, and obtain the threshold analysis result of the performance parameter.

[0071] In one embodiment of this disclosure, the threshold determination submodule compares the actual values ​​of performance parameters such as CPU utilization, memory consumption, and network transmission rate with preset thresholds based on the performance parameter change information to determine whether the threshold is exceeded, and obtains the threshold analysis results of the performance parameters.

[0072] For example, the threshold judgment submodule compares the actual values ​​of performance parameters such as CPU utilization, memory consumption, and network transmission rate with preset thresholds based on the changes in performance parameters. A preset threshold is set for each performance parameter, including CPU utilization, memory consumption, and network transmission rate. These thresholds are determined based on system requirements and design goals, and are set as a reasonable upper limit for performance. The threshold judgment process can be described as "exceeding the threshold = performance parameter > preset threshold." When a performance parameter is detected to exceed the threshold, it is recorded, and a threshold analysis result for the performance parameter is generated. This threshold analysis result indicates the performance parameter that exceeds the threshold and related information.

[0073] Step 503: The anomaly detection submodule is used to obtain abnormal performance status information of mobile applications based on threshold analysis results.

[0074] In one embodiment of this disclosure, the anomaly detection submodule analyzes cases exceeding the threshold based on the threshold analysis results, performs anomaly detection, identifies the task corresponding to the application causing the anomaly, monitors the changing trend of the anomaly, and obtains anomaly performance status information.

[0075] For example, the anomaly detection submodule analyzes cases exceeding the threshold based on the threshold analysis results and performs anomaly detection. By identifying the tasks corresponding to the applications exceeding the threshold and analyzing the task's running status, it compares the tasks exceeding the threshold with tasks within the normal range to find the cause of the anomaly. The severity of the anomaly is quantified using the formula "anomaly severity = exceeding value / threshold". The task causing the anomaly is identified and monitored to determine the changing trend of the anomaly and obtain anomaly performance status information.

[0076] In summary, based on the technical solution provided in this disclosure, the closed-loop mechanism of "dynamic monitoring - threshold judgment - anomaly location" enables early detection, accurate identification, and detailed quantification of performance anomalies, providing precise diagnostic basis for the performance optimization of the entire system and indirectly improving the stability and reliability of mobile applications. Furthermore, by monitoring multiple performance parameters of mobile applications, such as CPU usage, memory consumption, and network transmission rate, and combining them with preset thresholds to judge anomalies in real time, real-time tracking and timely detection of performance bottlenecks are achieved, ensuring application stability.

[0077] Figure 6A flowchart of a task scheduling module provided in an embodiment of this disclosure.

[0078] like Figure 6 As shown, the specific implementation of this task scheduling module includes the following steps: Step 601: The reallocation submodule is used to obtain the resource allocation plan for each task of the mobile application based on abnormal performance status information, task execution order information, and virtual mobile phone instance group.

[0079] In one embodiment of this disclosure, the reallocation submodule allocates virtual mobile instance resources to each task of the mobile application based on abnormal performance status information, task execution order, and virtual mobile instance group, adjusts resource utilization strategies, sets resource acquisition for key tasks, and obtains resource allocation plans for each task of the mobile application.

[0080] For example, the reallocation submodule allocates virtual phone instance resources to each task of the mobile application based on abnormal performance status information, task execution order, and virtual phone instance groups. By evaluating abnormal performance status, it determines which mobile application tasks require more resources and which virtual phone instances need adjustment. Based on the task execution order, resources are allocated to critical tasks. The resource allocation plan can be represented by the formula: "Resource Allocation = Total Resources × Task Requirement Ratio". To ensure reasonable resource allocation, resource acquisition priorities for critical tasks are set, and resource utilization strategies for virtual phone instances are adjusted to ensure optimal resource allocation, thus generating a resource allocation plan.

[0081] Step 602: The task adjustment submodule is used to adjust the order of tasks in the mobile application based on the resource allocation plan, and to assign each task to the corresponding virtual mobile phone instance, and obtain task allocation information.

[0082] In one embodiment of this disclosure, the task adjustment submodule adjusts the order of tasks in the mobile application based on the resource allocation plan, rearranges the task queue, assigns the tasks to the corresponding virtual mobile phone instances, and generates task allocation information.

[0083] For example, the task adjustment submodule adjusts the order of tasks in the mobile application based on the resource allocation plan, rearranges the task queue, determines the new task order by calculating task priority and resource requirements, and the task order adjustment method can be represented by the formula: "Task order = task priority / resource requirements". The tasks are then reassigned to the corresponding virtual mobile phone instances to ensure that critical tasks receive sufficient resources, and the order of other tasks is adjusted to avoid resource conflicts and performance bottlenecks. Task allocation information is generated to ensure that each task is assigned to the most suitable virtual mobile phone instance.

[0084] Step 603: The matching analysis submodule is used to determine the key cloud virtual mobile phone instances and key tasks based on task allocation information and virtual mobile phone instance groups, adjust the matching relationship between key cloud virtual mobile phone instances and key tasks, and obtain the task optimization scheduling scheme for mobile applications.

[0085] In one embodiment of this disclosure, the matching analysis submodule checks the tasks assigned on each virtual mobile phone instance based on task allocation information and virtual mobile phone instance group, analyzes the matching degree between resource allocation and the task requirements of the mobile application, determines the key cloud virtual mobile phone instances and key tasks, matches the key virtual mobile phone instances with the key tasks, and adjusts the resource configuration and task order to obtain the task optimization scheduling scheme of the mobile application.

[0086] For example, the matching analysis submodule checks the tasks assigned on the virtual mobile phone instances based on task allocation information and virtual mobile phone instance groups, analyzes the matching degree between resource allocation and the requirements of each task, and identifies key cloud virtual mobile phone instances and key tasks by comparing the requirements of each task with the resource allocation. Then, it determines the matching relationship between key virtual mobile phone instances and key tasks. The matching degree can be measured by "matching degree = actual resources / required resources". Based on the matching analysis results, it determines the tasks and resources that need to be adjusted and reconfigures the resources. By adjusting the matching relationship between key tasks and key virtual mobile phone instances, it ensures that key tasks can be carried out smoothly and obtains a task optimization scheduling scheme for mobile applications.

[0087] In summary, according to the technical solution provided in this disclosure, through dynamic and precise resource allocation and task matching, while repairing anomalies, optimal collaboration between resources, tasks, and virtual mobile phone instance groups is achieved, ultimately improving the stability, efficiency, and rationality of mobile application task operation.

[0088] Figure 7 A flowchart of the load prediction module provided in an embodiment of this disclosure.

[0089] like Figure 7 As shown, the specific implementation of this load prediction module includes the following steps: Step 701: The historical analysis submodule is used to obtain historical load data of the virtual mobile phone instance group based on the task optimization scheduling scheme and the virtual mobile phone instance group, and to obtain the load analysis results of the virtual mobile phone instance group based on the historical load data.

[0090] In one embodiment of this disclosure, the historical analysis submodule collects historical load data of virtual mobile phone instances in the cloud based on the task optimization scheduling scheme and the virtual mobile phone instance group, analyzes the changing trend of historical load, classifies and statistically analyzes the data, judges the resource utilization of various virtual mobile phone instances, and generates load analysis results of the virtual mobile phone instance group.

[0091] For example, the historical analysis submodule, based on the task optimization scheduling scheme and the virtual phone instance group, collects historical load data of each cloud-based virtual phone instance (i.e., historical load data of the virtual phone instance group). It extracts historical load information from the logs and monitoring data of the virtual phone instances, including processor, memory, storage, and network usage. Through statistical analysis, it analyzes the changing trends of historical load and further subdivides it according to time, date, and task type. Data classification and statistics can be calculated using the formula "Load Classification = Load Data / Classification Criteria" to determine the resource utilization of various virtual phone instances, identify peak and off-peak periods of resource utilization, and generate load analysis results. This determines the overall resource utilization of the virtual phone instance group, i.e., the load analysis results of the virtual phone instance group.

[0092] Step 702: The trend analysis submodule is used to determine the resource usage trend of the virtual mobile phone instance group based on the load analysis results.

[0093] In one embodiment of this disclosure, the trend analysis submodule analyzes the resource usage patterns of each cloud-based virtual mobile phone instance based on the load analysis results, counts the peak and off-peak periods of resources, calculates the fluctuation range of resource usage, judges the changing trend of resource usage, and then determines the resource usage trend of the virtual mobile phone instance group.

[0094] For example, the trend analysis submodule analyzes the resource usage patterns of each cloud virtual phone instance based on the load analysis results, and counts the peak and off-peak periods for each resource type, including processor utilization, memory consumption, and network bandwidth usage. By comparing the time periods of peak and off-peak periods, the fluctuation range of resource usage can be calculated using the formula: "Resource fluctuation = peak value - off-peak value". Based on the statistical results, the trend of resource usage changes is judged, and thus the resource usage trend of the virtual phone instance group is determined.

[0095] Step 703: The prediction and identification submodule is used to obtain the predicted resource demand data of the virtual mobile phone instance group based on the resource usage trend.

[0096] In one embodiment of this disclosure, the prediction and identification submodule predicts the future resource requirements of each cloud-based virtual mobile phone instance based on resource usage trends, analyzes future load changes, identifies the type and quantity of future resource requirements, and obtains predicted resource requirement data for the virtual mobile phone instance group.

[0097] For example, the prediction and identification submodule predicts the future resource requirements of each cloud-based virtual mobile phone instance based on resource usage trends. The prediction process can be calculated using the formula: "Future resource requirements = Past trends × Prediction coefficient". Multiple factors are considered during the prediction process, including seasonal variations, user growth, and task complexity. Based on the prediction results, future load changes can be identified, resource prediction results can be obtained, and the predicted resource requirements data for the virtual mobile phone instance group can be determined. This allows for advance planning of resource allocation and adjustments, ensuring that the virtual mobile phone instance group maintains stable performance despite future load changes.

[0098] In summary, according to the technical solution provided in the embodiments of this disclosure, by analyzing the historical load data of the cloud virtual mobile phone instance group, extracting resource usage trends, and combining multiple factors to predict future resource demand, resource allocation can be planned in advance, thereby reducing overload risks and ensuring the stable operation of the virtual mobile phone instance group. Figure 8 A flowchart of the collaborative operation module provided in the embodiments of this disclosure.

[0099] like Figure 8 As shown, the specific steps for implementing the collaborative operation module are as follows: Step 801: The data synchronization submodule is used to establish a synchronization connection between the mobile terminal and the cloud based on the predicted resource demand data, adjust the synchronization frequency of the mobile application between the mobile terminal and the cloud, and establish the data synchronization status between the mobile terminal and the cloud.

[0100] In one embodiment of this disclosure, the data synchronization submodule establishes a synchronization connection between the mobile device and the cloud based on predicted resource demand data, performs data synchronization transmission, adjusts the synchronization frequency of the mobile application between the mobile device and the cloud, optimizes the data consistency and stability between the cloud and the mobile device, and establishes a data synchronization state between the mobile device and the cloud.

[0101] For example, the data synchronization submodule establishes a synchronization connection between the mobile device and the cloud based on resource prediction results, ensuring stable data transmission between the two and determining the synchronization connection configuration between the mobile device and the cloud, including network protocols, bandwidth requirements, and synchronization intervals. To ensure the efficiency and stability of data synchronization, the synchronization frequency between the mobile application and the cloud is adjusted to avoid performance issues caused by excessive synchronization. The method for adjusting the synchronization frequency can be described as: "Synchronization Frequency = Resource Requirements × Synchronization Factor," optimizing the data consistency and stability between the cloud and the mobile device, ensuring that data is not lost or corrupted during synchronization, and establishing a data synchronization status between the mobile device and the cloud.

[0102] Step 802: The mobile task optimization submodule is used to obtain the running status of mobile tasks based on the data synchronization status, adjust the running order of mobile tasks based on the running status of mobile tasks, and obtain the running status of mobile tasks.

[0103] In one embodiment of this disclosure, task running status refers to the result label of a task at a certain moment, reflecting the core stage or final outcome of execution. Task running condition, on the other hand, refers to the complete set of information from task initiation to the present (or end), encompassing status, details, and issues. The mobile task optimization submodule, based on data synchronization status, identifies the current running status of each task on the mobile device in real time (e.g., result labels such as "normal execution," "stuck," "waiting for resources," etc.). For the identified status, a running order adjustment mechanism is initiated for tasks marked "stuck" or "resource contention." By analyzing the dependencies between tasks (e.g., task A depends on the output of task B) and resource demand characteristics (e.g., compute-intensive / network-intensive), the task execution sequence is dynamically optimized (e.g., prioritizing high-priority tasks with low resource requirements to reduce resource contention with data synchronization tasks). During the adjustment process, changes in task running status are continuously monitored, and a complete task running condition is formed based on the accumulated status data (including: resource usage curves for each task from initiation to the present, state transition nodes, and the extent of performance improvement due to order adjustment, etc.). By following this logic of "adjusting order based on state, optimizing performance based on order, and summarizing the situation based on full data", the smooth execution of tasks on the mobile device is ultimately achieved, avoiding performance loss caused by task scheduling conflicts and ensuring the overall operating efficiency of the application.

[0104] For example, the mobile task optimization submodule determines the running status of mobile tasks based on data synchronization status, adjusts the running order of mobile tasks according to the running status, monitors the running status of mobile tasks, and analyzes the dependencies and smoothness of operation between tasks. When it detects stuttering or delays during task execution, it optimizes performance by adjusting the task running order. The task running status monitoring process can be calculated using the formula "Running order = Task priority / Resource utilization". After adjusting the running order, it ensures the smooth execution of mobile tasks, prevents performance degradation caused by task order issues, obtains information on the running status of mobile tasks, and ensures the overall operating efficiency of the mobile application.

[0105] Step 803: The allocation and matching submodule is used to optimize the allocation of tasks in the mobile application on the mobile device and the cloud based on the task running status, and to obtain task collaboration information of the mobile application on the mobile device and the cloud.

[0106] In one embodiment of this disclosure, the allocation and matching submodule coordinates the task allocation of each task of the mobile application on the mobile device and the cloud based on the task running status, checks the resource matching between the mobile device and the cloud, adjusts the task coordination strategy between the mobile device and the cloud, optimizes the rationality of task allocation between the mobile device and the cloud, and obtains task collaboration information of the mobile application on the mobile device and the cloud.

[0107] For example, the allocation and matching submodule coordinates the task allocation of various tasks of the mobile application on the mobile device and in the cloud based on task execution status. By checking the resource matching between the mobile device and the cloud, it analyzes whether the resource usage between the two is balanced. The matching status can be calculated as: "Resource Matching = Mobile Device Resources / Cloud Resources". If unreasonable resource allocation is found, the task coordination strategy between the mobile device and the cloud will be adjusted to optimize the rationality of task allocation. After adjusting the strategy, task collaboration between the mobile device and the cloud is ensured, achieving optimal resource utilization and obtaining task collaboration information of the mobile application on the mobile device and in the cloud.

[0108] In summary, according to the technical solution provided in the embodiments of this disclosure, by dynamically adjusting task operation and coordinating task allocation, collaborative work between the cloud and the mobile terminal is achieved, optimizing operating efficiency, improving application performance and user experience, reducing the operating pressure on the mobile terminal, fundamentally alleviating the problems of overheating and excessive power consumption, and improving overall operating efficiency and user experience.

[0109] The aforementioned cloud-based mobile application performance management system is a solution for monitoring and managing the performance of mobile applications. By leveraging cloud computing resources, it provides real-time performance monitoring and analysis capabilities, helping developers and operations teams understand how their applications perform across different environments and devices. The system's uses include identifying performance issues, optimizing application performance, and ensuring a consistent and stable user experience. Furthermore, by running a simulated mobile phone in the cloud, it enables the migration of applications from physical phones to the cloud, resolving issues such as overheating, excessive power consumption, device loss, and cluster management problems associated with physical phones.

[0110] Figure 9 This is a flowchart illustrating a cloud-based mobile application performance management method provided in an embodiment of the present disclosure.

[0111] like Figure 9 As shown, this cloud-based mobile application performance management method specifically includes the following steps: Step 901: Using the cloud virtualization module based on cloud server resources, obtain a virtual phone instance group containing multiple cloud virtual phone instances; Step 902: Using the dynamic priority module, obtain task execution order information based on the data flow information of the mobile application and the resource requirements of each task in the mobile application; Step 903: Use the real-time monitoring module to obtain abnormal performance status information of the mobile application based on task execution sequence information and performance parameters of the mobile application; Step 904: Using the task scheduling module, obtain the task optimization scheduling scheme for the mobile application based on abnormal performance status information, task execution order information, and virtual mobile phone instance group.

[0112] Specifically, a cloud virtualization module is used to create cloud-based virtual phone instances based on cloud server resources. These instances are isolated, and their resource usage is monitored to create a virtual phone instance group containing multiple cloud-based virtual phone instances. A dynamic priority module analyzes the resource usage of the mobile application's task queue based on the application's data flow information. Based on the resource requirements of each task, corresponding processing priorities are set, and tasks are arranged according to these priorities to generate task execution order information. A real-time monitoring module monitors multiple performance parameters of the mobile application based on the task execution order information, recording CPU utilization, memory consumption, and network transmission rate. Changes in these performance parameters are analyzed against preset thresholds to identify abnormal situations exceeding the thresholds and obtain abnormal performance status information. Finally, a task scheduling module reallocates resources for each task in the mobile application based on the abnormal performance status information, task execution order information, and the virtual phone instance group. Tasks are assigned to matching virtual phone instances based on resource utilization status, optimizing resource acquisition for critical tasks and resulting in an optimized task scheduling scheme for the mobile application. The load prediction module, based on task optimization scheduling schemes and virtual mobile phone instance groups, collects historical load data of cloud-based virtual mobile phone instances to determine resource usage trends, analyzes historical load data patterns, and predicts future resource demands, thus obtaining predicted resource demand data for the virtual mobile phone instance groups. Based on the predicted resource demand data and cloud resource status, the collaborative operation module dynamically adjusts mobile task execution, coordinates task allocation between mobile applications on the mobile device and in the cloud, implements collaborative work between mobile applications in the cloud and on the mobile device, optimizes the efficiency of mobile application operation, and obtains collaborative information between the mobile device and the cloud.

[0113] The virtual phone instance group includes the created virtual phone instances, resource isolation policies, and instance group management methods; task execution order information includes task execution priority, inter-task dependencies, and task processing order; abnormal performance status information includes abnormal values ​​of CPU utilization, memory consumption, and network transmission rate; task optimization scheduling scheme includes reallocated task resources, adjusted task order, and tasks scheduled to differentiated virtual machine instances; predicted resource data includes resource usage trends and predicted resource requirements; and collaborative information includes data synchronization between the cloud and the mobile device, the execution order of tasks on the mobile device, and the resource allocation method in the cloud.

[0114] In summary, according to the cloud-based mobile application performance management method provided in this disclosure, this disclosure establishes virtual mobile phone instances in the cloud through cloud virtualization technology. It dynamically obtains task execution order information by utilizing the data flow information and task resource requirements of the mobile application, and obtains abnormal performance status information of the mobile application by utilizing the task execution order information and the performance parameters of the mobile application. Combining the abnormal performance status information, task execution order information, and virtual mobile phone instance groups, it dynamically adjusts the task optimization scheduling scheme of the mobile application. This effectively breaks through the limitations of rigid resource allocation in traditional systems, flexibly adapts to task requirements, and prioritizes resource supply for critical tasks, ultimately significantly improving the smoothness, stability, and efficiency of mobile applications, bringing users a better user experience.

[0115] Figure 10 This is a hardware block diagram of an electronic device provided according to an embodiment of the present disclosure. The electronic device 1000 according to an embodiment of the present disclosure includes at least a processor and a memory for storing computer-readable instructions. When the computer-readable instructions are loaded and executed by the processor, the processor performs the cloud-based mobile application performance management method described above in this disclosure.

[0116] Figure 10 The illustrated electronic device 1000 specifically includes a central processing unit (CPU) 1001, a graphics processing unit (GPU) 1002, and a memory 1003. These units are interconnected via a bus 1004. The CPU 1001 and / or GPU 1002 can function as the aforementioned processor, and the memory 1003 can function as the aforementioned memory storing computer-readable instructions. Furthermore, the electronic device 1000 may also include a communication unit 1005, a storage unit 1006, an output unit 1007, an input unit 1008, and an external device 1009, all of which are also connected to the bus 1004.

[0117] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0118] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0119] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0120] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0121] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0122] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0123] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0124] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A cloud-based mobile application performance management system, characterized in that, include: The cloud virtualization module is used to obtain a virtual phone instance group containing multiple cloud virtual phone instances based on cloud server resources; The dynamic priority module is used to obtain task execution order information based on the data flow information of the mobile application and the resource requirements of each task in the mobile application. The real-time monitoring module is used to obtain abnormal performance status information of the mobile application based on the task execution order information and the performance parameters of the mobile application. The task scheduling module is used to obtain a task optimization scheduling scheme for the mobile application based on the abnormal performance status information, the task execution order information, and the virtual mobile phone instance group.

2. The cloud-based mobile application performance management system according to claim 1, characterized in that, The system also includes: The load prediction module is used to obtain the predicted resource demand data of the virtual mobile phone instance group based on the task optimization scheduling scheme, the virtual mobile phone instance group, and the historical load data of the virtual mobile phone instance group. The collaborative operation module is used to establish a synchronous connection between the mobile terminal and the cloud based on the predicted resource demand data, and to obtain the task collaboration information of the mobile application on the mobile terminal and the cloud.

3. The cloud-based mobile application performance management system according to claim 2, characterized in that, The cloud virtualization module includes: The virtual phone creation submodule is used to establish the virtual environment configuration information of the cloud virtual phone instance based on the cloud server resources, and to obtain multiple cloud virtual phone instances; The resource isolation submodule is used to isolate the multiple cloud virtual mobile phone instances based on the virtual environment configuration information and the resource requirement information of the multiple cloud virtual mobile phone instances, and to obtain the resource isolation status of the multiple cloud virtual mobile phone instances. An instance group establishment submodule is used to obtain a virtual mobile phone instance group containing multiple cloud virtual mobile phone instances based on the resource isolation status of the multiple cloud virtual mobile phone instances.

4. The cloud-based mobile application performance management system according to claim 2, characterized in that, The dynamic priority module includes: The resource sorting submodule is used to obtain resource consumption characteristic data corresponding to each task in the mobile application based on the data flow information of the mobile application, calculate the resource usage frequency and resource ratio of each task for each type of resource based on the resource consumption characteristic data, prioritize the various types of resources consumed by each task based on the resource usage frequency and resource ratio of each task for each type of resource, and obtain a basic resource order table. The task sorting submodule is used to determine the resource requirements of each task based on the basic resource sequence table and a task sorting algorithm, and to adjust the sorting of each task based on the resource requirements, urgency and processing order of each task to generate a task sequence table. The priority determination submodule is used to obtain the dependencies between tasks based on the task sequence table, adjust the task sequence table based on the dependencies, and generate the task execution order information.

5. The cloud-based mobile application performance management system according to claim 2, characterized in that, The real-time monitoring module includes: The performance parameter submodule is used to obtain the change information of the performance parameters of the mobile application based on the task execution order information; The threshold judgment submodule is used to compare the actual value of the performance parameter with a preset threshold based on the change information of the performance parameter, and obtain the threshold analysis result of the performance parameter. The anomaly detection submodule is used to obtain the abnormal performance status information of the mobile application based on the threshold analysis results.

6. The cloud-based mobile application performance management system according to claim 2, characterized in that, The task scheduling module includes: The reallocation submodule is used to obtain the resource allocation plan for each task of the mobile application based on the abnormal performance status information, the task execution order information, and the virtual mobile phone instance group. The task adjustment submodule is used to adjust the order of each task of the mobile application based on the resource allocation plan, and to allocate each task to the corresponding virtual mobile phone instance, and obtain task allocation information; The matching analysis submodule is used to determine the key resources and key tasks of the cloud virtual mobile phone instance based on the task allocation information and the virtual mobile phone instance group, adjust the matching relationship between the key resources of the cloud virtual mobile phone instance and the key tasks, and obtain the task optimization scheduling scheme of the mobile application.

7. The cloud-based mobile application performance management system according to claim 2, characterized in that, The load prediction module includes: The historical analysis submodule is used to obtain historical load data of the virtual mobile phone instance group based on the task optimization scheduling scheme and the virtual mobile phone instance group, and to obtain load analysis results of the virtual mobile phone instance group based on the historical load data. The trend analysis submodule is used to determine the resource usage trend of the virtual mobile phone instance group based on the load analysis results; The prediction and identification submodule is used to obtain the predicted resource demand data of the virtual mobile phone instance group based on the resource usage trend.

8. The cloud-based mobile application performance management system according to claim 2, characterized in that, The collaborative operation module includes: The data synchronization submodule is used to establish a synchronization connection between the mobile terminal and the cloud based on the predicted resource demand data, adjust the synchronization frequency of the mobile application between the mobile terminal and the cloud, and establish the data synchronization status between the mobile terminal and the cloud. The mobile task optimization submodule is used to obtain the running status of the mobile task based on the data synchronization status, adjust the running order of the mobile task based on the running status of the mobile task, and obtain the running status of the mobile task. The allocation and matching submodule is used to optimize the allocation of each task in the mobile application on the mobile device and the cloud based on the task running status, and to obtain the task collaboration information of the mobile application on the mobile device and the cloud.

9. A cloud-based mobile application performance management method, characterized in that, include: By utilizing cloud virtualization modules based on cloud server resources, a virtual phone instance group containing multiple cloud virtual phone instances can be obtained; The dynamic priority module is used to obtain task execution order information based on the data stream information of the mobile application and the resource requirements of each task in the mobile application; The real-time monitoring module uses the task execution order information and the performance parameters of the mobile application to obtain abnormal performance status information of the mobile application. The task scheduling module uses the abnormal performance status information, the task execution order information, and the virtual mobile phone instance group to obtain the task optimization scheduling scheme for the mobile application.

10. An electronic device, characterized in that, include: Memory, used to store computer-readable instructions; as well as A processor for executing the computer-readable instructions, causing the electronic device to perform the cloud-based mobile application performance management method as described in claim 9.

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