Computer server computing resource allocation system based on edge collaboration

Abstract

The application discloses a computer server computing resource allocation system based on edge cooperation and relates to the technical field of resource allocation.The computer server computing resource allocation system comprises a data acquisition module, an environment evaluation module, a load evaluation module, an edge cooperation module and a visualization module.An overload judgment unit obtains overload nodes and lightly loaded nodes.A migration-out priority evaluation unit obtains the migration-out priority score of an edge node i.A migration-in priority evaluation unit obtains the migration-in priority score of the edge node i.The environment and load evaluation modules are used to quantitatively evaluate the influence of the environment on the performance attenuation, improve the load analysis accuracy, evaluate the migration-out and migration-in priorities of the node tasks based on the service importance, the user level and the load, ensure the priority migration of key tasks, reduce the system interruption risk and optimize the resource allocation.Through the quantitative load deviation degree and the dynamic adjustment coefficient, the lightly loaded and performance priority strategies are balanced, the dynamic demand can be coped with and the overall operation efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of resource allocation technology, specifically to a computer server computing resource allocation system based on edge collaboration. Background Technology

[0002] Edge servers are a distributed computing architecture used to perform collaborative computing tasks at the edge. These servers are typically located at the network edge, close to the data source and end users. They are designed to deploy computing resources at the network edge to process and analyze data close to where it is generated, thereby reducing data transmission latency and providing lower latency and higher real-time performance for data centers.

[0003] Currently, edge servers typically use static allocation strategies when allocating resources, without considering changes in the edge server environment and load. This makes it difficult to cope with constantly changing dynamic demands. Furthermore, the lack of performance difference analysis among different edge servers can easily lead to underutilization of tasks on weaker edge servers, reducing resource utilization and overall operating efficiency. When dealing with node overload, the strategy of migrating to the nearest node is often adopted, without analyzing task priorities, which increases the risk of interruption of critical services and fails to leverage the advantages of edge collaborative computing. Summary of the Invention

[0004] The purpose of this invention is to provide a computer server computing resource allocation system based on edge collaboration, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a computer server computing resource allocation system based on edge collaboration, comprising a data acquisition module, an environment assessment module, a load assessment module, an edge collaboration module, and a visualization module;

[0006] The data acquisition module is used to collect environmental data and operational data of the server, including extracted CPU information, memory information and network usage information.

[0007] The environmental assessment module analyzes the impact of the environment on performance based on the environmental data of each server, and obtains the environmental score F of edge node i. env,i ;

[0008] The load assessment module is based on CPU information, memory information, network usage information, and the environmental score F of edge node i. env,i Fusion analysis yields the load L of edge node i. i ;

[0009] The edge collaboration module includes an overload judgment unit, a migration-out priority evaluation unit, and a migration-in priority evaluation unit.

[0010] The overload judgment unit is based on the load L of edge node i. i By analyzing the average load of all edge nodes, the load deviation value LD of each edge node i is obtained. i And based on the load deviation value LD of each edge node i i The analysis identified overloaded and lightly loaded nodes.

[0011] The migration priority assessment unit is based on the importance of the service, user level, and the load L of edge node i. i The importance of overloaded nodes is evaluated to obtain the migration priority score CP of edge node i. i ;

[0012] The migration priority evaluation unit is based on CPU performance and the load L of edge node i. i The receiving capability of lightly loaded nodes is evaluated to obtain the migration priority score RP of edge node i. i .

[0013] Optionally, the environmental data includes temperature information, humidity information, and energy consumption information. The environmental assessment module performs a weighted fusion analysis based on the temperature information, humidity information, and energy consumption information of each server to obtain the environmental score F of edge node i. env,i This is used to assess the impact of the environment on server performance;

[0014] The environmental score F for edge node i env,i Set environmental thresholds Y1 and Y2, and when the environmental score F of edge node i... env,i <Environmental threshold Y1, or environmental score F of edge node i env,i >When the environmental threshold Y2 is reached, the server is checked.

[0015] Optionally, the load assessment module first normalizes the CPU information, memory information, and network usage information of each server to obtain the normalized CPU utilization C of edge node i. i Memory usage A after normalization of edge node i i The bandwidth utilization of edge node i is normalized to allow for comparison of servers of different specifications;

[0016] Then, the CPU utilization C after normalizing the edge node i is... i Memory usage A after normalization of edge node i i The normalized bandwidth utilization of edge node i is weighted and fused together, while the environmental score F of edge node i is also introduced. env,i The analysis ultimately yielded the load L of edge node i. act,i .

[0017] Optionally, the overload judgment unit first calculates the average load of all edge nodes to obtain the average total load L of the edge nodes. avg ;

[0018] Then, based on the load L of edge node i i and the average total load L of edge nodes avg We analyze the deviation of edge node i and quantify the deviation of edge node i to obtain the load deviation value LD of edge node i. i ;

[0019] Set the overload threshold to Y3 and the light load threshold to Y4. When the load of edge node i deviates from the value LD... i When the overload threshold Y3 is reached, the edge node i is determined to be overloaded. When the load of edge node i deviates from the value LD... i When the light load threshold Y4 is less than 4, the edge node i is determined to be lightly loaded.

[0020] Optionally, the migration priority evaluation unit analyzes the edge nodes that are determined to be overloaded and determines the task migration order of the overloaded edge nodes;

[0021] First, the importance of each edge node is analyzed based on its different business applications, resulting in an importance score G for edge node i. i ;

[0022] Then, compare the user's level with the highest level to obtain the user level score;

[0023] Finally, based on the importance score G of edge node i... i User rating and load L of edge node i i The fusion analysis yields the migration priority score CP for edge node i. i The order in which tasks migrate out of overloaded edge nodes is determined by their migration priority scores.

[0024] Optionally, the migration priority evaluation unit analyzes the edge nodes that are determined to be lightly loaded and determines the migration order of tasks for the lightly loaded edge nodes. The evaluation process is as follows:

[0025]

[0026]

[0027] Where RP i Score the migration priority of edge node i;

[0028] Give the light load score to edge node i;

[0029] θ1 is the influence coefficient of the light load score;

[0030] CH i Let i be the number of CPU cores in edge node i;

[0031] CH avg The average number of CPU cores across all edge nodes;

[0032] This indicates the performance deviation score of edge node i;

[0033] θ2 is the performance impact coefficient;

[0034] The light load rating influence coefficient θ1 + performance influence coefficient θ2 = 1. Under the initial condition, both the light load rating influence coefficient θ1 and the performance influence coefficient θ2 are 0.5.

[0035] Optionally, the average total load L of the edge nodes avg A threshold range is set to dynamically balance the strategies of prioritizing light loads and prioritizing performance, as follows:

[0036] When the average total load of edge nodes is L avg When the load is greater than 70%, the light load rating influence coefficient θ1 is 0.7, and the performance influence coefficient θ2 is 0.3.

[0037] When the average total load of edge nodes is L avg When the load is less than 50%, the light load rating influence coefficient θ1 is 0.3 and the performance influence coefficient θ2 is 0.7.

[0038] When 50% ≤ the average total load L of edge nodes avg For loads ≤ 70%, the light load rating influence coefficient θ1 is 0.5, and the performance influence coefficient θ2 is 0.5, in order to balance the light load level and performance.

[0039] Optionally, the visualization module is used to visualize the data from each module, including load heatmaps and priority distribution pie charts.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] I. This invention analyzes the environmental data of each server through an environmental assessment module to determine the impact of the server's environment on its performance, and performs quantitative analysis on the results to obtain the environmental score of edge node i. Then, through a load assessment module, it analyzes the load of edge node i based on normalized CPU information, memory information, and network usage information. By introducing environmental influencing factors, the load analysis can fully consider the changes in the edge server environment and load, dynamically assess the impact of the environment on performance degradation, and improve the accuracy of load analysis.

[0042] Second, this invention uses a migration priority assessment unit to evaluate the importance of overloaded nodes based on the importance of the service, user level, and load of edge node i. Tasks are migrated out according to the migration priority score of each edge node, ensuring that edge nodes with important data or heavy loads are migrated first, reducing the risk of critical service interruption. Then, a migration priority assessment unit evaluates the receiving capacity of lightly loaded nodes based on CPU performance and the load of edge node i, allowing comparison of servers of different specifications to obtain the migration priority score of edge node i. This fully considers the impact of the actual performance of each edge node on system resource allocation, making system resource allocation more even, improving overall operating efficiency, and fully leveraging the advantages of edge collaborative computing.

[0043] Third, this invention determines overload and light load by quantifying the deviation between the load of a single edge node and the average load of all edge nodes, and by setting a threshold for the deviation. This avoids relying solely on the load percentage of a single server, prevents ineffective scheduling, and improves the quality of load optimization. Furthermore, by dynamically adjusting the light load scoring influence coefficient and performance influence coefficient based on the actual situation of the average load of all edge nodes, the system can dynamically balance the light load priority and performance priority strategies, enabling the system to cope with constantly changing dynamic demands, distribute resources more evenly, and improve overall operating efficiency. Attached Figure Description

[0044] Figure 1 This is a block diagram of the system modules of the present invention;

[0045] Figure 2 This is a schematic diagram of the system flow of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Example 1, please refer to Figure 1 and Figure 2 This implementation provides a computer server computing resource allocation system based on edge collaboration, including a data acquisition module, an environment assessment module, a load assessment module, an edge collaboration module, and a visualization module;

[0048] The data acquisition module is used to collect environmental and operational data from the server. The operational data includes extracting CPU information, memory information, and network usage information.

[0049] The environment assessment module analyzes the impact of the environment on performance based on the environmental data of each server, and obtains the environment score F of edge node i. env,i ;

[0050] The load assessment module is based on CPU information, memory information, network usage information, and the environmental score F of edge node i. env,i Fusion analysis yields the load L of edge node i. i ;

[0051] The edge collaboration module includes an overload judgment unit, a migration-out priority evaluation unit, and a migration-in priority evaluation unit;

[0052] The overload judgment unit is based on the load L of edge node i. i By analyzing the average load of all edge nodes, the load deviation value LD of each edge node i is obtained. i And based on the load deviation value LD of each edge node i i The analysis identified overloaded and lightly loaded nodes.

[0053] The migration priority assessment unit is based on the importance of the service, user level, and the load L of edge node i. i The importance of overloaded nodes is evaluated to obtain the migration priority score CP of edge node i. i ;

[0054] The migration priority evaluation unit is based on CPU performance and the load L of edge node i. i The receiving capability of lightly loaded nodes is evaluated to obtain the migration priority score RP of edge node i. i CP is scored according to the migration priority of edge node i. i The migration priority score RP of edge node i i Determine the order of resource allocation;

[0055] The visualization module is used to visualize the data from various modules, including load heatmaps and priority distribution pie charts. The load heatmap displays the load of each edge node in the form of a heatmap, while the priority distribution pie chart sorts the priorities of overloaded and lightly loaded nodes according to their percentage and displays them in a pie chart. This allows system administrators to quickly understand the load situation and the order of task migration, thereby improving the overall management efficiency of the system.

[0056] Specifically, the environmental assessment module first analyzes the environmental data of each server to determine the impact of the server's environment on its performance, and then performs quantitative analysis on the results to obtain the environmental score F of edge node i. env,iThen, the load assessment module analyzes the server's load based on its actual operating conditions, including CPU, memory, and network usage, to obtain the load L of edge node i. i Furthermore, by incorporating environmental factors, the load analysis can take into account dynamic environmental changes, thereby improving the accuracy of the load analysis.

[0057] Then, the overload judgment unit in the edge collaboration module analyzes the difference between each edge node and the overall average, quantifies the difference between a single edge node and the overall load average, and obtains the load deviation value LD of each edge node i. i To obtain the load deviation value LD of each edge node i i Set a threshold to classify all nodes into overloaded nodes, lightly loaded nodes, and normal nodes. Normal nodes do not need to be adjusted. By judging the overall resource stress based on the overall load average, we can avoid the local optimization trap caused by isolated evaluation of the status of a single node, and at the same time avoid frequent ineffective scheduling, thus improving the quality of load optimization.

[0058] After dividing nodes into overloaded and lightly loaded nodes, tasks from overloaded nodes need to be migrated to lightly loaded nodes to achieve load balancing. This is done by using a migration priority evaluation unit based on the importance of the service, user level, and the load L of edge node i. i The importance of overloaded nodes is evaluated and the results are quantified to obtain the migration priority score CP of edge node i. i Tasks are migrated out according to the migration priority score of each edge node, thereby ensuring that edge nodes with important data or heavy load are migrated first, reducing the risk of interruption of critical services.

[0059] Then, the migration priority evaluation unit assesses CPU performance and the load L of edge node i. i The receiving capability of lightly loaded nodes is evaluated to obtain the migration priority score RP of edge node i. i It can fully consider the impact of the actual performance of each edge node on the allocation of system resources, and not just analyze based on the load ratio. This allows for comparison of servers of different specifications, avoids over-allocating tasks to servers with weak performance, makes the allocation of system resources more even, improves the overall operating efficiency, and fully leverages the advantages of edge collaborative computing.

[0060] Furthermore, the environmental assessment module analyzes the impact of environmental data on server performance to obtain an environmental impact coefficient. The analysis process of the environmental assessment module is as follows:

[0061]

[0062] Where F env,iThe environment score for edge node i is given. The closer the score is to 1, the closer the environment is to the ideal state and the smaller the server performance degradation. The lower the score is to 1, the worse the environment is and the more severe the performance degradation. When the score is slightly greater than 1, the range is 1 to 1.3, which means that the environment is better than the ideal state. The number of edge nodes i depends on the number of servers.

[0063] T act,i The actual temperature of edge node i;

[0064] T sta Standard temperature;

[0065] For temperature rating, T act,i -T sta For temperature difference;

[0066] W1 is the temperature influence coefficient;

[0067] Because server hardware is sensitive to temperature, if the actual temperature T of edge node i... act,i Exceeding the standard temperature T sta This can lead to performance degradation, such as the CPU triggering frequency reduction protection due to overheating, and an increase in chip leakage current. Therefore, when T... act,i -T sta Temperature score when temperature difference > 0 >0, by setting a minus sign, makes When it becomes a negative contribution, the environment score F of edge node i is reduced. env,i It will change with temperature difference T act,i -T sta The value decreases as the temperature difference T increases, reflecting how rising temperatures cause a decrease in server performance. act,i -T sta Temperature score when <0 <0, making When it becomes a positive contributor, the environment score F of edge node i is... env,i Will follow T act,i -T sta The decrease in temperature difference is increased to reflect the improved performance potential of the server under low temperature conditions. However, in practical applications, lower temperature is not always better. The minimum and maximum temperature thresholds can be set according to the actual situation of the server.

[0068] H act,i The actual humidity of edge node i;

[0069] H sta Standard humidity;

[0070] For humidity rating, H sta -H act,i Humidity difference;

[0071] W2 is the humidity influence coefficient;

[0072] Unlike temperature, both excessively high and low humidity can negatively impact servers. High humidity increases the risk of short circuits and reduces heat dissipation efficiency, while low humidity can generate static electricity, damaging chips. Therefore, setting an absolute value H reflects the humidity difference. act,i -H sta Impact on server performance;

[0073] E act,i This represents the actual energy consumption of edge node i.

[0074] E sta Standard energy consumption;

[0075] For energy efficiency rating, E act,i -E sta Poor energy efficiency;

[0076] W3 is the energy consumption impact coefficient;

[0077] Temperature influence coefficient W1 + humidity influence coefficient W2 + energy consumption influence coefficient W3 = 1, and temperature influence coefficient W1 > energy consumption influence coefficient W3 > humidity influence coefficient W2;

[0078] If E act,i -E sta Energy consumption difference < 0 indicates that the system is in a high-efficiency, low-load state, with minimal server performance degradation. >0, improve the environmental score F of edge node i by using the plus sign. env,i If E act,i -E sta A power consumption difference greater than 0 indicates significant server performance degradation. <0, by adding a value less than 0, the environmental score F of edge node i can be reduced. env,i F represents the environmental score F of edge node i, indicating the actual energy consumption. env,i The impact.

[0079] The environmental score F for edge node i env,i Set environmental thresholds Y1 and Y2, with Y1 set to 0.6 and Y2 set to 1.3. When the environmental score F of edge node i... env,i <Environmental threshold Y1, or environmental score F of edge node i env,i When the environmental threshold Y2 is reached, it indicates that the environmental conditions have deteriorated, the server performance has been severely damaged, and there is a risk of major system failures or long-term instability. At this time, the computer server should be checked.

[0080] Furthermore, the load assessment module's assessment process is as follows:

[0081]

[0082] Where L i The load of edge node i is the load. The larger the result, the higher the load, and vice versa. The value range is from 0 to 1, and the value is 1 even if the result is greater than 1.

[0083] C i CPU utilization of edge node i after normalization;

[0084] β1 is the CPU utilization impact coefficient;

[0085] A i The normalized memory usage of edge node i;

[0086] β2 is the impact coefficient of memory utilization;

[0087] D i The normalized bandwidth utilization of edge node i can eliminate the interference of hardware performance differences on load assessment by normalizing the data. The original load of low-configuration edge nodes, such as quad-core CPUs, may be higher than that of high-configuration edge nodes, such as eight-core CPUs, in the long run. This allows server hardware such as CPUs of different specifications to be compared, thus improving the accuracy of load assessment.

[0088] β3 is the bandwidth utilization impact coefficient;

[0089] CPU utilization impact coefficient β1 + memory utilization impact coefficient β2 + bandwidth utilization impact coefficient β3 = 1;

[0090] Represents the environmental correction factor, when the environmental score F of edge node i... env,i The smaller the value of L is compared to 1, the higher the load L of edge node i. act,i The higher the environmental score F of edge node i, the better. env,i The closer L is to 1, the higher the load L of edge node i. act,i The lower.

[0091] By weighted fusion and normalization of CPU utilization, memory utilization, and bandwidth utilization, comparisons between servers of different specifications can be made. Furthermore, weighted fusion of several core server metrics reduces misjudgments, such as situations where the CPU is idle but memory is a bottleneck. Introducing environmental correction factors to comprehensively assess server load allows for dynamic evaluation of the degree of environmental degradation on performance, improving the load on edge nodes. act,i The accuracy.

[0092] Furthermore, the overload judgment unit's judgment process is as follows:

[0093]

[0094]

[0095] LD i The load deviation value of edge node i;

[0096] L avg This represents the average total load of the edge nodes.

[0097] N is the number of edge nodes;

[0098] Set the overload threshold to Y3 and the light load threshold to Y4. When the load of edge node i deviates from the value LD... i When the overload threshold Y3 is reached, the edge node i is determined to be overloaded. When the load of edge node i deviates from the value LD... i When the load threshold Y4 is less than the light load threshold, the edge node i is determined to be lightly loaded. When making subsequent adjustments, tasks in overloaded edge nodes are transferred to lightly loaded edge nodes. By quantifying the deviation of the load of a single edge node from the average load of all edge nodes and setting a threshold for the deviation, overload and light load are determined. This avoids judging based solely on the load percentage of a single server, avoids frequent and ineffective scheduling, and allows for overall consideration to achieve load balancing and improve system operating efficiency.

[0099] Furthermore, the migration priority assessment unit evaluates all edge nodes identified as overloaded. The evaluation process is as follows:

[0100]

[0101] Among them CP i The migration priority of edge node i is scored. The higher the score, the more important the edge node is and the more severe the load situation.

[0102] G i The importance score for edge node i is determined by system presets, with a value ranging from 0 to 1. The closer the score is to 1, the more important the edge node. For example, if edge node i is used for financial transactions, then G... i The value is 1

[0103] γ1 is the influence coefficient of the importance score;

[0104] LV i The user level of edge node i is determined based on the user's information in the system. The user level can be determined based on the system usage time or the user's identity. For example, the system administrator can be defaulted to the highest user level to ensure the administrator's management efficiency of the system.

[0105] LV maxThe highest user level;

[0106] Assess user ratings for edge node i;

[0107] γ2 is the user level influence coefficient;

[0108] L i The load of edge node i;

[0109] L i,max This represents the highest load of edge node i, and its value is 1.

[0110] The load score for edge node i is determined when the load L of edge node i is... i The higher the load score of edge node i, the better. The higher the score, the higher the migration priority score (CP) of edge node i. i Increase;

[0111] γ3 is the load influence coefficient;

[0112] Importance score influence coefficient γ1 + User level influence coefficient γ2 + Load influence coefficient γ3 = 1;

[0113] Specifically, the migration priority score CP for each edge node i is calculated. i The edge node i is sorted according to its score, with a higher score indicating a higher priority. When migrating out edge node tasks in the future, the migration will be carried out in order of priority score to ensure that edge nodes with important data and high load can be prioritized, thereby reducing the risk of interruption of critical services and ensuring system stability.

[0114] Simultaneously, the migration priority evaluation unit evaluates all edge nodes identified as lightly loaded. The evaluation process is as follows:

[0115]

[0116]

[0117] Where RP i The migration priority of edge node i is scored. The higher the score, the lower the load and the stronger the performance of the node.

[0118] Give the light load score to edge node i;

[0119] θ1 is the influence coefficient of the light load score;

[0120] CH i The number of CPU cores in edge node i represents the processing power of edge node i.

[0121] CH avg The average number of CPU cores across all edge nodes;

[0122] The performance deviation score of edge node i represents the performance deviation score. A higher score indicates that edge node i performs better among all edge nodes, and vice versa. The migration priority score RP of edge node i represents the performance deviation score. i Will follow The value increases with the increase of edge nodes to fully consider the impact of the actual performance of edge nodes on system resource allocation;

[0123] θ2 is the performance impact coefficient;

[0124] The light load score influence coefficient θ1 + performance influence coefficient θ2 = 1, and the light load score influence coefficient θ1 and performance influence coefficient θ2 are both 0.5 in the initial state;

[0125] Specifically, by introducing the number of CPU cores, we can avoid over-allocating tasks to nodes with low but weak performance, or blindly allocating tasks to edge nodes with strong performance but close to overload, thereby achieving a balance between resource utilization efficiency and task execution efficiency and ensuring the accuracy of load adjustment.

[0126] Meanwhile, the light load scoring influence coefficient θ1 and performance influence coefficient θ2 can be dynamically adjusted according to the overall overload situation of the system, as detailed below;

[0127] When the average total load of edge nodes is L avg When the load is greater than 70%, it indicates that the overall system resources are strained. The light load score influence coefficient θ1 can be increased to prioritize ensuring that light load nodes are not overloaded. At this time, the light load score influence coefficient θ1 is 0.7 and the performance influence coefficient θ2 is 0.3.

[0128] Average total load L of edge nodes avg When the load is less than 50%, it indicates that the overall system resources are sufficient, and tasks are prioritized to be allocated to high-performance nodes to reduce latency. At this time, the light load score impact coefficient θ1 is 0.3 and the performance impact coefficient θ2 is 0.7.

[0129] When 50% ≤ the average total load L of edge nodes avg ≤ 70% indicates that the system load is balanced. At this time, the light load score influence coefficient θ1 is 0.5 and the performance influence coefficient θ2 is 0.5 to balance the light load level and performance.

[0130] By using the average total load L of edge nodes avg In practice, dynamically adjusting the light load scoring influence coefficient θ1 and the performance influence coefficient θ2 can enable the system to dynamically balance the light load priority and performance priority strategies, making the system resource allocation more even and improving the system operating efficiency.

[0131] By cooperating with the migration-out priority assessment unit and the migration-in priority assessment unit, the load difference and allocation weight can be dynamically quantified, reducing manual intervention and ensuring that the system can automatically adjust the task allocation of each edge node in abnormal scenarios such as node failure and sudden increase in tasks, maintain stable system operation, reduce the risk of service interruption, and give full play to the advantages of edge collaborative computing.

[0132] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A computer server computing resource allocation system based on edge collaboration, characterized in that: It includes a data acquisition module, an environmental assessment module, a load assessment module, an edge collaboration module, and a visualization module; The data acquisition module is used to collect environmental data and operational data of the server, including extracted CPU information, memory information and network usage information. The environmental assessment module analyzes the impact of the environment on performance based on the environmental data of each server, and obtains the environmental score F of edge node i. env,i The environmental assessment module analysis process is as follows: ； Where F env,i For the environment score of edge node i, T act,i T represents the actual temperature of edge node i. sta The standard temperature is given by W1, where W1 is the temperature influence coefficient and H is the temperature. act,i H represents the actual humidity of edge node i. sta Where W2 is the standard humidity, E is the humidity influence coefficient, and W2 is the humidity influence coefficient. act,i E represents the actual energy consumption of edge node i. sta W3 is the standard energy consumption, and W3 is the energy consumption impact coefficient. The load assessment module is based on CPU information, memory information, network usage information, and the environmental score F of edge node i. env,i Fusion analysis yields the load L of edge node i. i The load assessment module performs the following assessment: ； Where L i For the load of edge node i, C i Let A be the normalized CPU utilization of edge node i, β1 be the CPU utilization impact coefficient, and A be the normalized CPU utilization of edge node i. i Let β2 be the normalized memory utilization rate of edge node i, and D be the memory utilization impact coefficient. i Let F be the normalized bandwidth utilization of edge node i, β3 be the bandwidth utilization impact coefficient, and F be the bandwidth utilization normalization factor. env,i Give the environment score to edge node i; The edge collaboration module includes an overload judgment unit, a migration-out priority evaluation unit, and a migration-in priority evaluation unit. The overload judgment unit is based on the load L of edge node i. i By analyzing the average load of all edge nodes, the load deviation value LD of each edge node i is obtained. i And based on the load deviation value LD of each edge node i i The analysis identified overloaded and lightly loaded nodes. The migration priority assessment unit is based on the importance of the service, user level, and the load L of edge node i. i The importance of overloaded nodes is evaluated to obtain the migration priority score CP of edge node i. i The migration priority evaluation unit evaluates all edge nodes determined to be overloaded. The evaluation process is as follows: ； Among them CP i G scores the migration priority of edge node i. i Let γ1 be the importance score for edge node i, and LV be the influence coefficient of the importance score. i For the user level of edge node i, LV max The highest user level is represented by γ2, which is the user level influence coefficient, and L is the highest user level. i For the load of edge node i, L i,max γ3 represents the highest load of edge node i, and γ3 is the load influence coefficient. The migration priority evaluation unit is based on CPU performance and the load L of edge node i. i The receiving capability of lightly loaded nodes is evaluated to obtain the migration priority score RP of edge node i. i The migration priority evaluation unit analyzes the edge nodes that are determined to be lightly loaded and determines the migration order of tasks for the lightly loaded edge nodes. The evaluation process is as follows: ； ； Where RP i The migration priority score for edge node i is given, θ1 is the light load score influence coefficient, and CH i CH represents the number of CPU cores in edge node i. avg θ2 represents the average number of CPU cores across all edge nodes, and θ2 is the performance impact coefficient.

2. The computer server computing resource allocation system based on edge collaboration according to claim 1, characterized in that: The environmental data includes temperature, humidity, and energy consumption information. The environmental assessment module performs a weighted fusion analysis based on the temperature, humidity, and energy consumption information of each server to obtain the environmental score F of edge node i. env,i This is used to assess the impact of the environment on server performance; The environmental score F for edge node i env,i Set environmental thresholds Y1 and Y2, and when the environmental score F of edge node i... env,i <Environmental threshold Y1, or environmental score F of edge node i env,i >When the environmental threshold Y2 is reached, the server is checked.

3. The computer server computing resource allocation system based on edge collaboration according to claim 2, characterized in that: The load assessment module first normalizes the CPU, memory, and network usage information of each server to obtain the normalized CPU utilization C of edge node i. i Memory usage A after normalization of edge node i i The bandwidth utilization of edge node i is normalized to allow for comparison of servers of different specifications; Then, the CPU utilization C after normalizing the edge node i is... i Memory usage A after normalization of edge node i i The normalized bandwidth utilization of edge node i is weighted and fused together, while the environmental score F of edge node i is also introduced. env,i The analysis ultimately yielded the load L of edge node i. act,i .

4. The edge-collaboration-based computer server computing resource allocation system according to claim 3, characterized in that: The overload judgment unit first calculates the average load of all edge nodes to obtain the average total load L of the edge nodes. avg ; Then, based on the load L of edge node i i and the average total load L of edge nodes avg We analyze the deviation of edge node i and quantify the deviation of edge node i to obtain the load deviation value LD of edge node i. i ; Set the overload threshold to Y3 and the light load threshold to Y4. When the load of edge node i deviates from the value LD... i When the overload threshold Y3 is reached, the edge node i is determined to be overloaded. When the load of edge node i deviates from the value LD... i When the light load threshold Y4 is less than 4, the edge node i is determined to be lightly loaded.

5. The edge-collaboration-based computer server computing resource allocation system according to claim 4, characterized in that: The migration priority evaluation unit analyzes the edge nodes that are determined to be overloaded and determines the task migration order of the overloaded edge nodes. First, the importance of each edge node is analyzed based on its different business applications, resulting in an importance score G for edge node i. i ; Then, compare the user's level with the highest level to obtain the user level score; Finally, based on the importance score G of edge node i... i User rating and load L of edge node i i The fusion analysis yields the migration priority score CP for edge node i. i The order in which tasks migrate out of overloaded edge nodes is determined by their migration priority scores.

6. The computer server computing resource allocation system based on edge collaboration according to claim 5, characterized in that: The average total load L of the edge nodes avg A threshold range is set to dynamically balance the strategies of prioritizing light loads and prioritizing performance, as follows: When the average total load of edge nodes is L avg When the load is greater than 70%, the light load rating influence coefficient θ1 is 0.7, and the performance influence coefficient θ2 is 0.

3. When the average total load of edge nodes is L avg When the load is less than 50%, the light load rating influence coefficient θ1 is 0.3 and the performance influence coefficient θ2 is 0.

7. When 50% ≤ the average total load L of edge nodes avg For loads ≤ 70%, the light load rating influence coefficient θ1 is 0.5, and the performance influence coefficient θ2 is 0.5, in order to balance the light load level and performance.

7. The computer server computing resource allocation system based on edge collaboration according to claim 1, characterized in that: The visualization module is used to visualize the data from each module, including load heatmaps and priority distribution pie charts.

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