CN118819843A - A business monitoring method and system based on cloud computing platform - Google Patents

Abstract

The present invention relates to a business monitoring method and system based on a cloud computing platform, and to the field of data processing technology. The method comprises: determining a dynamic adjustment factor according to usage data of various resources and services on the cloud computing platform; calculating the fitness value of each particle according to a preset optimization target and a dynamic adjustment factor; updating the speed and position of each particle according to the fitness value of each particle and the dynamic adjustment factor to determine a final resource allocation scheme, repeatedly evaluating the particle fitness and updating the particle state until a preset number of iterations is reached to obtain a final resource allocation scheme; and automatically adjusting the resource configuration of the cloud computing platform according to the final resource allocation scheme to obtain an optimized resource configuration and a current operating status. The present invention can not only monitor the operating status of the cloud computing platform in real time, but also perform intelligent resource allocation according to actual needs, thereby improving resource utilization.

Description

A business monitoring method and system based on cloud computing platform

Technical Field

The present invention relates to the field of data processing technology, and in particular to a business monitoring method and system based on a cloud computing platform.

Background Art

Cloud computing platforms use virtualization technology to achieve flexible allocation and efficient use of resources, providing powerful computing power and storage space for enterprises and individual users. However, with the continuous expansion of cloud computing platforms and the increasing number of service types, how to effectively monitor and manage these resources has become particularly important.

For example, traditional cloud computing platform monitoring methods usually focus on monitoring a single resource or service, and may not be able to fully and real-time reflect the operation status and performance indicators of the entire cloud computing platform. In addition, these methods also have certain limitations in resource allocation and optimization, and may not be able to dynamically adjust according to the real-time status of the cloud computing platform and user needs, resulting in resource waste or performance bottlenecks.

Summary of the invention

The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a business monitoring method and system based on a cloud computing platform, which can not only monitor the operating status of the cloud computing platform in real time, but also perform intelligent resource allocation according to actual needs, thereby improving resource utilization.

In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

In a first aspect, a business monitoring method based on a cloud computing platform includes:

Obtain usage data of various resources and services on the cloud computing platform;

Automated analysis of usage data of various resources and services on the cloud computing platform to determine the operating status and performance indicators of the cloud computing platform;

According to the operation status and performance indicators of the cloud computing platform, a group of particles are initialized, each particle represents a resource allocation scheme;

Determine dynamic adjustment factors based on usage data of various resources and services on the cloud computing platform;

Calculate the fitness value of each particle according to the preset optimization goal and dynamic adjustment factor;

According to the fitness value of each particle and the dynamic adjustment factor, the speed and position of each particle are updated to determine the final resource allocation plan. The particle fitness is evaluated and the particle state is updated repeatedly until the preset number of iterations is reached to obtain the final resource allocation plan.

According to the final resource allocation plan, the resource configuration of the cloud computing platform is automatically adjusted to obtain the optimized resource configuration and current operating status;

The optimized resource configuration and current operating status are sent to the user end via monitoring signals.

Preferably, the usage data of various resources and services on the cloud computing platform are automatically analyzed to determine the operating status and performance indicators of the cloud computing platform, including:

Filter out relevant features of the cloud computing platform's operating status and performance indicators from the resource and service usage data;

According to the relevant features, the number of clusters K is determined by the elbow rule;

Cluster the resource and service usage data according to the number of clusters K. In the clustering process, the resource and service usage data are divided into K clusters, each cluster representing a similar operating state, until a preset number of iterations is reached to obtain a clustering result;

Based on the clustering results, the operating status and performance indicators of the cloud computing platform are determined, including the average or median of each cluster.

The dynamic adjustment factor is calculated based on the usage data of various resources and services on the cloud computing platform.

Resource and service usage data includes CPU usage, memory usage, network bandwidth, and storage usage.

In a second aspect, a business monitoring system based on a cloud computing platform includes:

The acquisition module is used to obtain the usage data of various resources and services on the cloud computing platform; automatically analyze the usage data of various resources and services on the cloud computing platform to determine the operating status and performance indicators of the cloud computing platform; initialize a group of particles according to the operating status and performance indicators of the cloud computing platform, each particle represents a resource allocation plan;

The processing module is used to determine the dynamic adjustment factor according to the usage data of various resources and services on the cloud computing platform; calculate the fitness value of each particle according to the preset optimization target and the dynamic adjustment factor; update the speed and position of each particle according to the fitness value of each particle and the dynamic adjustment factor to determine the final resource allocation plan, repeatedly evaluate the particle fitness and update the particle state until the preset number of iterations is reached to obtain the final resource allocation plan; according to the final resource allocation plan, automatically adjust the resource configuration of the cloud computing platform to obtain the optimized resource configuration and the current operating status; send the optimized resource configuration and the current operating status to the user end through the monitoring signal.

According to a third aspect, a computing device includes:

one or more processors;

The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art:

By acquiring and analyzing the usage data of various resources and services on the cloud computing platform, the overall operation status and performance indicators of the cloud computing platform can be monitored in real time. By setting the particle swarm optimization algorithm and the dynamic adjustment factor, the present invention can dynamically adjust the resource allocation scheme according to the actual situation. This dynamic optimization mechanism can significantly improve the resource utilization of the cloud computing platform, avoid resource waste, and ensure the efficient operation of the platform.

The fitness value of each particle is calculated according to the preset optimization target and dynamic adjustment factor, so that the resource allocation scheme can be automatically adjusted as the state of the cloud computing platform changes, enhancing the adaptability and flexibility of the system. By continuously optimizing resource allocation, the present invention can ensure that the cloud computing platform is always in the best state, thereby providing users with more stable and faster services, greatly improving the user experience.

Through real-time monitoring and dynamic adjustment, the present invention can timely discover and solve problems that may arise in the cloud computing platform, such as resource bottlenecks, performance degradation, etc., thereby enhancing the stability and reliability of the system. Through automated resource allocation and optimization, the need for manual intervention is reduced, reducing the operation and maintenance costs of the cloud computing platform.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. Some specific embodiments of the present application will be described in detail in an illustrative and non-restrictive manner with reference to the drawings. The same reference numerals in the drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the drawings:

FIG1 is a schematic diagram of a process flow of a business monitoring method based on a cloud computing platform according to the present invention.

FIG. 2 is a schematic diagram of a business monitoring system based on a cloud computing platform according to the present invention.

DETAILED DESCRIPTION

In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

The following embodiment of the present application takes the business monitoring method based on the cloud computing platform as an example to explain the solution of the present application in detail, but this embodiment cannot limit the protection scope of the present application.

As shown in FIG1 , the present invention provides a service monitoring method based on a cloud computing platform, the method comprising the following steps:

Step 11, obtaining usage data of various resources and services on the cloud computing platform; the usage data of resources and services includes CPU usage, memory usage, network bandwidth, and storage usage;

Step 12, automatically analyzing the usage data of various resources and services on the cloud computing platform to determine the operating status and performance indicators of the cloud computing platform;

Step 13, according to the operation status and performance indicators of the cloud computing platform, a group of particles are initialized, each particle represents a resource allocation scheme;

Step 14, determining a dynamic adjustment factor based on usage data of various resources and services on the cloud computing platform;

Step 15, calculating the fitness value of each particle according to the preset optimization target and the dynamic adjustment factor;

Step 16, according to the fitness value of each particle and the dynamic adjustment factor, the speed and position of each particle are updated to determine the final resource allocation plan, and the particle fitness is evaluated and the particle state is updated repeatedly until the preset number of iterations is reached to obtain the final resource allocation plan;

Step 17, according to the final resource allocation plan, automatically adjust the resource configuration of the cloud computing platform to obtain the optimized resource configuration and current operating status;

Step 18: Send the optimized resource configuration and current operation status to the user end via a monitoring signal.

In an embodiment of the present invention, by acquiring and analyzing the usage data of various resources and services on the cloud computing platform, the overall operation status and performance indicators of the cloud computing platform can be monitored in real time. By setting the particle swarm optimization algorithm and the dynamic adjustment factor, the present invention can dynamically adjust the resource allocation scheme according to the actual situation. This dynamic optimization mechanism can significantly improve the resource utilization of the cloud computing platform, avoid resource waste, and ensure the efficient operation of the platform. The fitness value of each particle is calculated according to the preset optimization target and the dynamic adjustment factor, so that the resource allocation scheme can be automatically adjusted as the state of the cloud computing platform changes, thereby enhancing the adaptability and flexibility of the system. By continuously optimizing resource configuration, the present invention can ensure that the cloud computing platform is always kept in the best state, thereby providing users with more stable and faster services, greatly improving the user experience. Through real-time monitoring and dynamic adjustment, the present invention can timely discover and solve problems that may occur in the cloud computing platform, such as resource bottlenecks, performance degradation, etc., thereby enhancing the stability and reliability of the system. Through automated resource allocation and optimization, the need for manual intervention is reduced, and the operation and maintenance cost of the cloud computing platform is reduced.

In another preferred embodiment of the present invention, a set of data collection interfaces is designed or existing monitoring tools (such as Zabbix, Prometheus, etc.) are used to collect the required data; these interfaces should be closely integrated with various components of the cloud computing platform (such as virtual machines, containers, physical servers, etc.) so that resource usage can be obtained in real time and accurately. The CPU usage of each computing node is collected regularly (such as every second or every minute) through the monitoring tool or interface. The CPU usage is usually expressed as the ratio of the time the CPU is occupied in a certain time period to the total time of the time period; the collected data may include user-state CPU time, system-state CPU time, and idle time, etc., so as to more comprehensively analyze the CPU usage. The monitoring tool will regularly record the memory usage of each computing node, including used memory, free memory, cache memory, etc. These data are helpful for analyzing the efficiency of memory usage and whether there is memory leakage or overuse.

The network bandwidth usage is collected through the network interface card (NIC) or other network monitoring tools, including key indicators such as inbound and outbound data traffic, network latency, packet loss rate, etc. Monitoring of network bandwidth helps identify network bottlenecks and optimize data transmission efficiency. The monitoring tool records the capacity, used space, remaining space, and I/O performance (such as read and write speed, IOPS, etc.) of the storage device. The collected resource usage data will be integrated and stored in a central database or time series database. This data can be indexed by timestamp for subsequent data analysis and visualization. When collecting and using this data, it is necessary to ensure the security and privacy of the data, and appropriate access control and encryption measures should be implemented to prevent data leakage or unauthorized access.

In a preferred embodiment of the present invention, the usage data of various resources and services on the cloud computing platform are automatically analyzed to determine the operating status and performance indicators of the cloud computing platform, including:

Filter out relevant features of the cloud computing platform's operating status and performance indicators from the resource and service usage data;

According to the relevant features, the number of clusters K is determined by the elbow rule;

Cluster the resource and service usage data according to the number of clusters K. In the clustering process, the resource and service usage data are divided into K clusters, each cluster representing a similar operating state, until a preset number of iterations is reached to obtain a clustering result;

Based on the clustering results, the operating status and performance indicators of the cloud computing platform are determined, including the average or median of each cluster.

In the embodiment of the present invention, the collected resource and service usage data are preprocessed, including data cleaning, deduplication, outlier processing, etc. Then, the features related to the operation status and performance indicators of the cloud computing platform are screened out from these data, including CPU usage, memory occupancy, network bandwidth utilization, storage I/O performance, etc. The clustering costs (such as WSS, Within-Cluster Sum of Squares) curve, find the "elbow point" of the curve (that is, the point where the cost reduction rate suddenly slows down), and the K value corresponding to this point is the optimal number of clusters. According to the K value determined in the previous step, the K-means clustering algorithm is used to cluster the resource and service usage data. During the clustering process, the algorithm randomly selects K initial centroids, and then assigns each data point to the nearest centroid to form K clusters; recalculates the centroid of each cluster and repeats the data point assignment process until the position of the centroid no longer changes significantly or reaches the preset number of iterations. After clustering is completed, each cluster represents a similar operating state. For each cluster, the average or median of its internal data points can be calculated to represent the performance indicators under the operating state. For example, the average CPU usage and average memory occupancy of each cluster can be calculated to obtain the performance of the cloud computing platform under different operating states. According to the clustering results, several typical operating states of the cloud computing platform can be determined (such as idle state, light load state, heavy load state, etc.). For each operating state, by calculating the statistical characteristics of the data points in the cluster (such as the average value, median, etc.), the specific performance indicators in the state are obtained. These performance indicators can be used to evaluate the performance of the cloud computing platform. Through the above steps, the automatic analysis of the usage data of various resources and services on the cloud computing platform can be realized, and then the operating state and performance indicators of the cloud computing platform can be determined.

In a preferred embodiment of the present invention, a group of particles are initialized according to the operating status and performance indicators of the cloud computing platform, each particle represents a resource allocation scheme, including:

Each particle will contain two main attributes: position (representing the resource allocation plan) and speed. The position can be a multidimensional vector, and each dimension represents the allocation amount of a resource, such as the number of CPU cores, memory capacity, network bandwidth, etc. The speed is also a multidimensional vector, indicating the adjustment direction and rate of the particle in each resource dimension.

The number of particles to be initialized is determined based on the complexity of the problem and the required search accuracy. The more particles there are, the wider the search space is, but the higher the computational cost is. Based on the current operating status and performance indicators of the cloud computing platform, the position vector of each particle is given an initial value. These initial values can be generated based on historical data, empirical rules or randomly, but they must be ensured to be within a reasonable resource allocation range. For example, if the current CPU usage is high, the position of the initialized particle may be in an area where more CPU resources are allocated. The initial velocity of the particle can be set to zero or randomly generated according to a certain distribution (such as uniform distribution or normal distribution). The initialization of the velocity should take into account the scale of the search space and the characteristics of the problem to avoid premature convergence or deviation of the particle from the search space. For each initialized particle (i.e., resource allocation scheme), a predetermined evaluation criterion (such as cost, performance, resource utilization, etc.) is used for evaluation. This evaluation will serve as the basis for particle update and selection in subsequent iterations. After completing the initialization and evaluation of the particles, the system is ready to enter the iteration process to search for the optimal resource allocation scheme by continuously updating the position and velocity of the particles. Through this step, an initial solution space is provided for the subsequent particle swarm optimization algorithm, and each particle represents a possible resource allocation scheme. In the following iterations, the algorithm will adjust the position and speed of each particle according to its performance and fitness to find the optimal resource allocation strategy.

In a preferred embodiment of the present invention, the dynamic adjustment factor is determined based on the usage data of various resources and services on the cloud computing platform, including:

Based on the usage data of various resources and services on the cloud computing platform,

Calculate dynamic adjustment factors;

Where DAF is the dynamic adjustment factor; γ, α, β, δ and ∩ are weight coefficients, satisfying 0≤γ, α, β, δ,∈≤1, and α+β＝1, δ+∈＝1; n is the number of resource types; _wi is the weight of the i-th resource; _Ui is the utilization rate of the i-th resource; _Ri is the current request volume or demand volume of the i-th resource; _Rmax is the maximum request volume or capacity of the i-th resource; m is the number of service types considered; _Lj is the load indicator of the j-th service; _Sj is the current concurrency or number of sessions of the j-th service; _Smax is the maximum concurrency or number of sessions of the j-th service; p is the number of time periods; _Tk is the average response time or delay in the k-th time period; q is the number of user feedback; _Vl is the score or satisfaction of the l-th user feedback; i, j, l and k are indexes.

In an embodiment of the present invention, the dynamic adjustment factor (DAF) comprehensively considers the usage of multiple resources and services, including resource utilization, service load, response time, and user feedback, so as to more comprehensively and accurately reflect the actual operation status and demand of the cloud computing platform. Since DAF is calculated based on real-time resource and service usage data, it can dynamically adapt to changes in the cloud computing platform. When resource demand, service load or user feedback changes, DAF will adjust accordingly to provide timely guidance for resource allocation. The weight coefficients in the formula (such as γ, α, β, δ and ε) can be adjusted according to actual conditions, which makes DAF more flexible and customizable. By incorporating user feedback (such as ratings or satisfaction), DAF can more directly reflect the user's feelings about cloud computing services. By dynamically adjusting the resource allocation scheme, the optimization strategy based on DAF can help the cloud computing platform improve resource utilization efficiency, reduce resource waste, and enhance system stability while ensuring performance.

In a preferred embodiment of the present invention, the fitness value of each particle is calculated according to the preset optimization target and the dynamic adjustment factor, including:

According to the preset optimization goals and dynamic adjustment factors, Calculate the fitness value of each particle, where Fit _i is the fitness value of the i-th particle; DAF _i is the dynamic adjustment factor calculated based on the position of the i-th particle; DAF _t is the dynamic adjustment factor value expected to be achieved in the preset optimization goal; Cost _i is the cost associated with the i-th particle.

In an embodiment of the present invention, the design of the fitness function takes into account the preset optimization goal (i.e., the dynamic adjustment factor value expected to be achieved). This ensures that the optimization process is goal-oriented and can focus on improving the particles that are most critical to achieving the optimization goal. The calculation of the fitness value not only takes into account the proximity of the dynamic adjustment factor to the goal, but also balances the relationship between performance improvement and resource cost through the introduction of the cost term. This helps to avoid excessive investment of resources in pursuit of performance improvement, thereby achieving a balance between economic benefits and performance optimization. Since the dynamic adjustment factor is calculated in real time based on the position of each particle, the fitness value can also dynamically reflect the pros and cons of the particle in the current environment. This dynamic adaptability helps the algorithm to continuously and effectively optimize resource allocation in a changing cloud computing environment. The fitness function adopts a smooth transition form (implemented in a fractional form), which avoids mutations or jumps during the optimization process and helps the stable convergence of the algorithm. The calculation scheme of the fitness value is not only applicable to specific cloud computing environments or application scenarios, but also can be flexibly applied to a variety of different cloud computing resource allocation problems by adjusting the preset optimization goals and cost functions. By comprehensively considering the dynamic adjustment factor and cost, the fitness function helps guide the particle swarm algorithm to search for the optimal solution globally instead of just falling into the local optimum.

In a preferred embodiment of the present invention, when updating the speed and position of each particle according to the fitness value of each particle and the dynamic adjustment factor, the speed update formula is:

in, and are the velocities of the ith particle at time t and t+1 respectively; w is the inertia weight, _c1 and _c2 are learning factors, _r1 and _r2 are random numbers between 0 and 1; _pi is the optimal position of the ith particle; gb is the global optimal position of all particles.

In an embodiment of the present invention, the inertia weight w helps to balance the capabilities of global search and local search. A larger w value is conducive to global search, while a smaller w value is more inclined to local search. By adjusting the size of w, the exploration and development capabilities of particles in the search space can be controlled. The learning factor c ₁ determines the extent to which the particle learns from its own historical optimal position. The random number r ₁ introduces randomness to this learning, which helps the particles to explore more widely in the search space. The learning factor c ₂ determines the extent to which the particle learns from the global optimal position. The random number r ₂ also introduces randomness to this learning, which helps to share information between particles and promote the convergence of the entire particle swarm to the global optimal solution. The coefficient c ₃ introduces the difference between the dynamic adjustment factor and the preset target to affect the speed update of the particle, which enables the particle to adjust its search direction and speed according to the gap between the current dynamic adjustment factor value and the target value, so as to move more directly towards the optimization goal.

In a preferred embodiment of the present invention, when updating the speed and position of each particle according to the fitness value of each particle and the dynamic adjustment factor, the updating formula of the position is:

in, and are the positions of the i-th particle at time t and t+1 respectively.

In the embodiment of the present invention, due to the speed It is calculated based on multiple factors such as the individual optimal position of the particle, the global optimal position, and the dynamic adjustment factor. Therefore, it reflects the dynamic behavior of the particle in the search space, which enables the position update to adapt to the progress of the search process and helps the particle move to a better position. The position update formula provides continuous movement of particles in the search space. This continuity helps the algorithm maintain stability and smoothness during the search process, avoid mutations or jumps, and thus helps find a better solution. By considering the individual optimal and global optimal positions of the particles, as well as the influence of the dynamic adjustment factor, the position update formula enables the particle to adaptively adjust its movement direction and step size according to the progress of the search process. This adaptability helps the algorithm adopt different strategies at different search stages, thereby better balancing exploration and development capabilities.

Step 17, after the previous optimization algorithm (such as particle swarm optimization algorithm) runs, a final resource allocation plan will be obtained. This plan details how much resources (such as the number of CPU cores, memory size, storage capacity, network bandwidth, etc.) should be allocated to each computing node or service; before the resource allocation plan is actually applied, the system will perform a series of verification checks to ensure the rationality and feasibility of the plan. For example, check whether the total amount of resources exceeds the actual carrying capacity of the cloud platform and whether it meets the requirements of various service level agreements (SLAs); once the plan is verified, the management system of the cloud computing platform (such as Kubernetes, OpenStack, etc.) will start to execute resource adjustment commands, which include starting or shutting down virtual machines, adjusting container resource quotas, reallocating network bandwidth, etc.; during the resource adjustment process, the system will monitor the changes of various indicators in real time, such as resource utilization, service response time, etc., to ensure that the adjustment process is carried out smoothly and without adverse effects on the service; after the adjustment is completed, the system will record the new resource configuration and update the operating status of the cloud computing platform.

In step 18, the system will collect detailed information about the optimized resource configuration, including the specific amount of resources allocated to each node or service, and the current operating status data of the cloud computing platform (such as CPU usage, memory occupancy, network throughput, etc.); the collected data will be formatted into reports or charts that are easy for users to understand. This helps users intuitively understand the effect of resource optimization and the operating status of the cloud platform; according to user preferences and system configuration, select the appropriate communication protocol and method to send data to the user end, which may be through email, SMS notification, Web interface update, API call or other customized communication methods; send the formatted data to the user end as a monitoring signal. Users can evaluate the effect of resource optimization and the current performance and health of the cloud platform based on this information; after receiving the monitoring signal, users can provide feedback based on the actual situation, and may need to fine-tune the resource allocation plan to better meet business needs or performance requirements. These feedback and adjustments will serve as input for a new round of optimization to continuously improve the resource configuration of the cloud computing platform.

As shown in FIG2 , an embodiment of the present invention further provides a business monitoring system 20 based on a cloud computing platform, comprising:

The acquisition module 21 is used to acquire the usage data of various resources and services on the cloud computing platform; automatically analyze the usage data of various resources and services on the cloud computing platform to determine the operation status and performance indicators of the cloud computing platform; initialize a group of particles according to the operation status and performance indicators of the cloud computing platform, each particle represents a resource allocation scheme;

The processing module 22 is used to determine the dynamic adjustment factor according to the usage data of various resources and services on the cloud computing platform; calculate the fitness value of each particle according to the preset optimization target and the dynamic adjustment factor; update the speed and position of each particle according to the fitness value of each particle and the dynamic adjustment factor to determine the final resource allocation plan, repeatedly evaluate the particle fitness and update the particle state until the preset number of iterations is reached to obtain the final resource allocation plan; according to the final resource allocation plan, automatically adjust the resource configuration of the cloud computing platform to obtain the optimized resource configuration and the current operating status; send the optimized resource configuration and the current operating status to the user end through the monitoring signal.

It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

In addition, it should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and/or recombined. These decompositions and/or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using basic programming skills after reading the description of the present invention.

Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and/or recombined. These decompositions and/or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims (7)

1. The service monitoring method based on the cloud computing platform is characterized by comprising the following steps of:

acquiring service condition data of various resources and services on a cloud computing platform;

Carrying out automatic analysis on the service condition data of various resources and services on the cloud computing platform to determine the running state and performance index of the cloud computing platform;

Initializing a group of particles according to the running state and performance index of the cloud computing platform, wherein each particle represents a resource allocation scheme;

determining a dynamic adjustment factor according to the service condition data of various resources and services on the cloud computing platform;

calculating the fitness value of each particle according to a preset optimization target and a dynamic adjustment factor;

Updating the speed and the position of each particle according to the fitness value and the dynamic adjustment factor of each particle to determine a final resource allocation scheme, and repeatedly evaluating the fitness of the particle and updating the state of the particle until the preset iteration times are reached to obtain the final resource allocation scheme;

According to the final resource allocation scheme, automatically adjusting the resource allocation of the cloud computing platform to obtain the optimized resource allocation and the current running state;

And sending the optimized resource configuration and the current running state to the user side through a monitoring signal.

2. The cloud computing platform-based business monitoring method of claim 1, wherein automatically analyzing usage data of various resources and services on the cloud computing platform to determine operational status and performance indicators of the cloud computing platform comprises:

Screening relevant characteristics of the running state and performance index of the cloud computing platform from the service condition data of the resources and the services;

Determining the number K of clusters through an elbow rule according to the related features;

Clustering the service condition data of the resources and the services according to the number K of clusters, dividing the service condition data of the resources and the services into K clusters in the clustering process, wherein each cluster represents a similar running state until the preset iteration times are reached, so as to obtain a clustering result;

and determining the running state and performance index of the cloud computing platform according to the clustering result, wherein the running state and performance index comprise the average value or the median of each cluster.

3. The cloud computing platform-based traffic monitoring method of claim 2, wherein determining the dynamic adjustment factor based on usage data of various resources and services on the cloud computing platform comprises:

and according to the service condition data of various resources and services on the cloud computing platform, calculating a dynamic adjustment factor.

4. The cloud computing platform based traffic monitoring method of claim 3, wherein the usage data of resources and services comprises CPU usage, memory occupancy, network bandwidth, and storage usage.

5. A cloud computing platform-based business monitoring system, comprising:

the acquisition module is used for acquiring the service condition data of various resources and services on the cloud computing platform; carrying out automatic analysis on the service condition data of various resources and services on the cloud computing platform to determine the running state and performance index of the cloud computing platform; initializing a group of particles according to the running state and performance index of the cloud computing platform, wherein each particle represents a resource allocation scheme;

The processing module is used for determining dynamic adjustment factors according to the service condition data of various resources and services on the cloud computing platform; calculating the fitness value of each particle according to a preset optimization target and a dynamic adjustment factor; updating the speed and the position of each particle according to the fitness value and the dynamic adjustment factor of each particle to determine a final resource allocation scheme, and repeatedly evaluating the fitness of the particle and updating the state of the particle until the preset iteration times are reached to obtain the final resource allocation scheme; according to the final resource allocation scheme, automatically adjusting the resource allocation of the cloud computing platform to obtain the optimized resource allocation and the current running state; and sending the optimized resource configuration and the current running state to the user side through a monitoring signal.

6. A computing device, comprising:

One or more processors;

Storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the method of any of claims 1-4.

7. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program which, when executed by a processor, implements the method according to any of claims 1-4.