Multi-core server control method and system based on data security

Abstract

The application discloses a multi-core server control method and system based on data security, relates to the technical field of multi-core servers, and comprises the following steps: acquiring a plurality of tasks and task characteristics currently to be processed by a multi-core server, and obtaining a plurality of security requirement parameters; acquiring processing logs of a plurality of server cores in a preset time range, and obtaining a plurality of core external interaction rates; performing task allocation control optimization according to the plurality of security requirement parameters and the plurality of core external interaction rates, and obtaining an optimal task allocation control scheme; and controlling the plurality of server cores to process the plurality of tasks according to the optimal task allocation control scheme. The technical problems that the existing multi-core server has a data leakage risk and cannot reasonably allocate tasks according to task security requirements and server core external interaction conditions are solved.

Description

Technical Field

[0001] This application relates to the field of multi-core server technology, specifically to a multi-core server control method and system based on data security. Background Technology

[0002] With the development of information technology, multi-core servers are being used more and more widely in various fields. Multi-core servers, with their powerful computing and multi-tasking capabilities, can meet the needs of large-scale data processing and complex business operations. However, when traditional multi-core servers handle multiple tasks, the data for different tasks may have different security requirements. If tasks are not allocated reasonably, it can easily lead to the risk of data leakage.

[0003] On the other hand, existing multi-core server task allocation methods often only focus on task processing efficiency while ignoring data security factors. They cannot reasonably allocate tasks based on the security requirements of the tasks and the external interactions of the server core, resulting in significant data security risks during server operation. Summary of the Invention

[0004] This application provides a data security-based multi-core server control method and system, which solves the technical problems of existing multi-core servers having data leakage risks and being unable to reasonably allocate tasks according to task security requirements and external interactions of the server core.

[0005] The technical solution to the above-mentioned technical problems in this application is as follows:

[0006] In a first aspect, this application provides a multi-core server control method based on data security, the method comprising:

[0007] The system retrieves multiple tasks currently pending processing on a multi-core server, collects characteristics of these tasks, performs data security requirements analysis, and obtains multiple security requirements parameters.

[0008] Obtain the processing logs of multiple server cores within the most recent preset time range, perform interactive analysis, and obtain the external interaction rate of multiple cores;

[0009] Based on multiple security requirement parameters and multiple core external interaction rates, task allocation control is optimized to obtain the optimal task allocation control scheme. This involves analyzing the data leakage security value and buffer attack security value of the task allocation control scheme and optimizing them. The optimal task allocation control scheme includes the optimal allocation relationship between multiple tasks and multiple server cores.

[0010] According to the optimal task allocation control scheme, multiple server cores are controlled to process multiple tasks.

[0011] Secondly, this application provides a multi-core server control system based on data security, including:

[0012] The information acquisition module is used to acquire multiple tasks currently pending processing on the multi-core server, collect the characteristics of multiple tasks, perform data security requirement analysis, and obtain multiple security requirement parameters.

[0013] The interaction analysis module is used to obtain the processing logs of multiple server cores within the most recent preset time range, perform interaction analysis, and obtain the external interaction rate of multiple cores.

[0014] The scheme optimization module is used to optimize task allocation control based on multiple security requirement parameters and multiple core external interaction rates to obtain the optimal task allocation control scheme. In this process, the data leakage security value and buffer attack security value of the task allocation control scheme are analyzed and optimized. The optimal task allocation control scheme includes the optimal allocation relationship between multiple tasks and multiple server cores.

[0015] The task processing module is used to control multiple server cores to process multiple tasks according to the optimal task allocation control scheme.

[0016] This application provides one or more technical solutions, which have at least the following technical effects or advantages:

[0017] This application provides a multi-core server control method and system based on data security. First, considering the data security requirements of multi-core server tasks and the external interactions of the server cores, it obtains the security requirement parameters of the tasks and the external interaction rate of the cores, laying the foundation for subsequent reasonable task allocation. Second, by optimizing task allocation control and analyzing data leakage security values ​​and buffer attack security values, it effectively reduces the risk of data leakage and the possibility of buffer attacks, improving the security of server operation. Third, it processes tasks according to the optimal task allocation control scheme, ensuring that each task is assigned to the most suitable server core, thus guaranteeing data security while improving task processing efficiency to a certain extent. Finally, it dynamically adjusts task allocation based on different tasks and server conditions to adapt to constantly changing business needs and security challenges, providing strong support for the stable and secure operation of multi-core servers in various fields.

[0018] Through the above technical solution, this application achieves a scientific and reasonable allocation of tasks on multi-core servers, improving the overall performance of the server while ensuring data security. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the multi-core server control method based on data security provided in an embodiment of this application;

[0021] Figure 2 This is a schematic diagram of the structure of a multi-core server control system based on data security provided in an embodiment of this application.

[0022] The components represented by each number in the attached diagram are explained below:

[0023] Information acquisition module 11, interactive analysis module 12, scheme optimization module 13, task processing module 14. Detailed Implementation

[0024] This application provides a multi-core server control method and system based on data security, which addresses the technical problems of existing multi-core servers having data leakage risks and being unable to reasonably allocate tasks according to task security requirements and external interactions with the server core.

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0027] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid unnecessarily obscuring the description of this application. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0028] Example 1, as Figure 1 As shown, this application provides a multi-core server control method based on data security, including:

[0029] S10: Obtain multiple tasks currently pending processing on the multi-core server, collect multiple task characteristics, perform data security requirement analysis, and obtain multiple security requirement parameters;

[0030] In this embodiment of the application, some cores within the multi-core server interact with the external network. During the processing, they may be attacked, leading to data leakage and affecting data security.

[0031] First, the characteristics of multiple tasks are acquired to analyze their data security requirements, resulting in various security requirement parameters. These parameters are then determined by evaluating characteristics such as task data type, sensitivity, and access permissions. For example, tasks involving sensitive data such as user privacy information or trade secrets have relatively high security requirement parameters, while tasks processing public data have relatively low security requirement parameters.

[0032] Specifically, when collecting task characteristics, one can start with the task's metadata, execution code, and input / output data. Analyzing these characteristics allows for a more accurate assessment of the task's data security requirements. After obtaining multiple security requirement parameters, these serve as crucial criteria for subsequent task allocation and control, ensuring data security during task processing.

[0033] Specifically, step S10 in the method includes:

[0034] Get multiple tasks currently pending processing on the multi-core server and collect multiple task characteristics, where each task characteristic includes task source and task type;

[0035] The security requirement classifier is invoked, and the multiple task features are input respectively to obtain multiple security requirement parameters.

[0036] In this embodiment, the characteristics of multiple tasks currently pending processing on the multi-core server, such as task source and task type, are first obtained. Task sources include different business systems, user terminals, etc., while task types cover various categories such as data processing, file transfer, and system maintenance. Different task sources and task types have different data security requirements.

[0037] Secondly, a security requirement classifier is invoked. This classifier, trained on a large amount of data, can accurately output the corresponding security requirement parameters based on the characteristics of the input task. By analyzing the source and type of the task, the classifier outputs security requirement parameters. For example, tasks from untrusted external sources or tasks involving critical business operations will receive higher security requirement parameters; while tasks from stable internal systems and with simple operations will receive relatively lower security requirement parameters.

[0038] By using the above methods, task characteristics are transformed into specific security requirement parameters, providing data support for subsequent task allocation and control.

[0039] The construction steps of the security requirement classifier include:

[0040] Based on the server task processing records over a historical period, a set of sample task features is collected, and the task security requirement parameters of different sample task features are labeled to obtain a set of sample security requirement parameters.

[0041] Using task source and task type as classification features, and based on a decision tree, a security requirement classifier is constructed using the sample task feature set and sample security requirement parameter set. The classification nodes of the security requirement classifier are task source or task type, and the classification result is the security requirement parameter.

[0042] In this embodiment, firstly, a set of sample task features is collected from server task processing records over a historical period. Simultaneously, task security requirement parameters corresponding to different sample task features are labeled, forming a set of sample security requirement parameters. The labeling process requires comprehensive consideration of various factors, such as the sensitivity of the data involved in the task and potential security risks.

[0043] Secondly, using task source and task type as classification features, a security requirement classifier is constructed based on the decision tree algorithm, utilizing the collected set of sample task features and the labeled set of sample security requirement parameters. The construction of the decision tree is a continuous process of dividing the samples, with classification nodes representing task source or task type. By continuously comparing and judging the source and type of the task, the samples are progressively divided into different branches, and the final classification result for each branch is the security requirement parameter.

[0044] During the construction of the decision tree, the optimal classification features and split points are selected based on metrics such as information gain to ensure that the decision tree can accurately classify the security requirements of the task. For example, when encountering a new task, the security requirement classifier will match and judge the task in the decision tree according to the task's source and type, thereby outputting the corresponding security requirement parameters.

[0045] For example, the steps to construct a security requirements classifier are as follows:

[0046] First, data preparation involves extracting historical task processing records from the server's log system and formatting them for machine learning algorithms. Task features are extracted from each record, including task source and type, and parameters are labeled according to the actual security requirements of the task.

[0047] Secondly, data preprocessing involves cleaning the collected sample task feature set to remove noisy data and missing values. Categorical features such as task source and task type are encoded and converted into numerical form so that the decision tree algorithm can process them. For example, task source is encoded as different numbers, such as 1 for internal systems and 2 for external partners; task type is encoded similarly.

[0048] Next, the model is trained. The decision tree model is trained using a preprocessed set of sample task features and a set of sample security requirement parameters. During training, the optimal classification features and split points are selected based on metrics such as information gain, and the samples are continuously split to construct the decision tree. For example, when using task source as the classification node, samples are divided into different branches according to different task sources, and then further divided according to task type within each branch until the samples in each branch have the same or similar security requirement parameters.

[0049] Finally, model evaluation and optimization are performed. The trained security requirement classifier is evaluated using a subset of untrained sample data, and metrics such as classification accuracy and recall are calculated. If the evaluation results are unsatisfactory, the parameters of the decision tree are adjusted, such as limiting the tree depth, the minimum number of samples, or reselecting classification features to improve the classifier's performance. After multiple evaluations and optimizations, it is ensured that the security requirement classifier can accurately output the corresponding security requirement parameters based on the task's source and type.

[0050] This provides a basis for subsequent task allocation control, enabling multi-core servers to allocate tasks reasonably according to the security requirements of the tasks, effectively reducing the risk of data leakage and improving the security and stability of server operation.

[0051] S20: Obtain the processing logs of multiple server cores within the most recent preset time range, perform interactive analysis, and obtain the external interaction rate of multiple cores;

[0052] In this embodiment, the interaction between the server core and the external network affects data security. Processing logs from multiple server cores within a recent preset time range are obtained, and information such as the frequency and amount of data exchanged with the external network is analyzed. Specifically, based on the server's recent processing logs, the percentage of direct interactions with the external network is analyzed to obtain the core external interaction rate. Through detailed analysis of the processing logs, the number of times each server core transmits data with the external network within a preset time period, and the size of the transmitted data, are understood.

[0053] Specifically, when analyzing and processing logs, the number of times each core establishes connections with the external network is counted, serving as an indicator of interaction frequency. Simultaneously, the amount of data transmitted in each interaction is calculated, and the data volumes of all interactions are summed to obtain the total amount of data interacted with the external network by that core within a preset time period. A higher external interaction rate indicates more frequent interactions between the core and the external network, a greater likelihood of external attacks, and a relatively higher risk of data leakage.

[0054] Specifically, step S20 in the method includes:

[0055] Obtain processing logs from multiple server cores within a recent preset time range. Each processing log contains the data source for the tasks processed within the recent preset time range.

[0056] Based on external data sources within multiple processing logs, multiple core external interaction rates are obtained.

[0057] In this embodiment, firstly, processing logs from multiple server cores within a recent preset time range are obtained. These logs record the data source information for each core's processing task. Data sources are categorized into internal and external sources. Internal data sources refer to data from other cores or storage areas within the multi-core server, while external data sources refer to data from external networks.

[0058] Then, the core external interaction rate is calculated based on the external data sources in multiple processing logs. The external interaction rate of a core is obtained by statistically analyzing the proportion of external data source tasks processed by each core relative to the total number of tasks. For example, if a core processes 100 tasks within a preset time, and 30 of those tasks have data sources from external networks, then the external interaction rate of that core is 30%.

[0059] Furthermore, after obtaining the external interaction rates of multiple cores, more caution should be exercised when assigning tasks to cores with high external interaction rates, avoiding tasks with high security requirements as much as possible to reduce the risk of data leakage; while for cores with low external interaction rates, tasks with relatively low security requirements can be appropriately assigned to make full use of server resources and improve overall task processing efficiency.

[0060] Furthermore, based on external data sources within multiple processing logs, several core external interaction rates are obtained, including:

[0061] Calculate the proportion of internal and external data sources in multiple processing logs as multiple core external interaction rates, where the data source is either external or internal.

[0062] In this embodiment, when calculating the core external interaction rate, it is first determined whether the data source in each processing log is external or internal. For each core's processing log, the number of external data sources and the total number of data sources are counted. The result of dividing the number of external data sources by the total number of data sources is the external interaction rate of that core.

[0063] After obtaining the external interaction rates of multiple cores, the server cores are categorized into high external interaction rate cores, medium external interaction rate cores, and low external interaction rate cores. For high external interaction rate cores, due to their frequent interaction with external networks and high risk of data leakage, tasks with low security requirements, such as public data processing tasks, should be prioritized. For medium external interaction rate cores, tasks with different security requirements are assigned based on the specific nature of the tasks. For low external interaction rate cores, because the possibility of external attacks is relatively small, some tasks with relatively high but not extremely high security requirements are appropriately assigned to them to fully utilize the server's performance.

[0064] S30: Based on multiple security requirement parameters and multiple core external interaction rates, optimize task allocation control to obtain the optimal task allocation control scheme. This includes analyzing the data leakage security value and buffer attack security value of the task allocation control scheme and optimizing it. The optimal task allocation control scheme includes the optimal allocation relationship between multiple tasks and multiple server cores.

[0065] In this embodiment, multiple security requirement parameters and multiple core external interaction rates are considered to optimize task allocation and obtain an optimal task allocation control scheme. A processor core may be assigned no tasks or multiple tasks. The data leakage security value reflects the likelihood of data leakage during task processing, while the buffer attack security value reflects the risk of buffer attacks during task processing.

[0066] When analyzing data leakage security values, consider the previously obtained security requirement parameters and core external interaction rate. A combination of high security requirement parameters and a high core external interaction rate indicates a greater risk of data leakage and should be avoided as much as possible during task allocation.

[0067] The analysis of buffer attack security values ​​considers the execution characteristics of the task and the processing capabilities of the core. Some tasks may frequently perform memory read and write operations during execution, making them susceptible to buffer attacks. Furthermore, the security levels of different server cores vary during processing.

[0068] Security is improved by optimizing the ratio of security requirements parameters for each task to the external interaction rate of the allocated server cores. If a server core is completely occupied by multiple tasks, causing processing to stall and halt, the second optimization goal is to reduce the computing power consumption of each server core to avoid buffered attacks. The optimization process employs various algorithms and strategies; for example, it utilizes machine learning algorithms, trained based on historical task allocation data and security event data, to allow the model to automatically learn the optimal task allocation strategy.

[0069] The optimal task allocation control scheme was ultimately obtained, clarifying the optimal allocation relationship between multiple tasks and multiple server cores. Through this allocation relationship, the task processing efficiency of multi-core servers is maximized while ensuring data security.

[0070] Specifically, step S30 in the method includes:

[0071] Multiple tasks are randomly assigned to multiple server cores to obtain the first task allocation control scheme;

[0072] Based on multiple security requirement parameters and multiple core external interaction rates, the first control fitness of the first task allocation control scheme is calculated.

[0073] Continue iterative optimization of task allocation control. After convergence, obtain the optimal task allocation control scheme with the highest control fitness. The optimal task allocation control scheme includes the optimal allocation relationship between multiple tasks and multiple server cores.

[0074] In this embodiment, multiple tasks are first randomly assigned to multiple server cores to form a first task allocation control scheme. This random allocation serves as an initial trial, providing a foundation for subsequent optimization. Random allocation can cover various possible combinations of tasks and cores.

[0075] Secondly, based on the multiple security requirement parameters and multiple core external interaction rates obtained above, the first control fitness of the first task allocation control scheme is calculated. Control fitness is an indicator of the scheme's quality, reflecting the security requirements of the task and the interaction between the server core and the external environment. For example, if a task has high security requirement parameters but is assigned to a core with a high external interaction rate, the control fitness of this combination may be low due to the greater risk of data leakage.

[0076] Next, iterative optimization of task allocation control begins. Iterative optimization is a continuous improvement process; each iteration adjusts the task allocation based on the current situation. In each iteration, the control fitness is recalculated, and changes in fitness are used to determine if the system is moving towards a better direction. As iterations proceed, the control fitness gradually converges. When the convergence condition is met, the optimal task allocation control scheme with the highest control fitness is considered to have been found.

[0077] Furthermore, the final optimal task allocation control scheme clarifies the optimal allocation relationship between multiple tasks and multiple server cores. Through this allocation relationship, the task processing efficiency of multi-core servers is maximized while ensuring data security. For example, tasks with high security requirements are preferentially allocated to cores with low external interaction rates, satisfying both data security requirements and fully utilizing the server cores' processing power. Conversely, tasks with low security requirements can be allocated to cores with higher external interaction rates to improve core utilization.

[0078] The calculation of the first control fitness of the first task allocation control scheme, based on multiple security requirement parameters and multiple core external interaction rates, includes:

[0079] Based on the first allocation relationship between multiple tasks and multiple server cores within the first task allocation control scheme, calculate the ratio of the security requirement parameters of each task to the core external interaction rate of the allocated server core to obtain multiple first data security coefficients.

[0080] The first data leakage security value is obtained by weighting the multiple first data security coefficients according to the magnitude of multiple security requirement parameters.

[0081] The processing power required for multiple tasks and the spare computing power of multiple server cores are obtained. The computing power occupancy ratio of multiple server cores under the first task allocation control scheme is obtained, and the maximum computing power occupancy ratio is selected as the basic first cache attack coefficient.

[0082] Based on multiple task characteristics, the basic first cache attack coefficient is calculated and corrected to obtain the first cache attack security value.

[0083] The first control fitness is calculated based on the first data leakage security value and the first cache attack security value.

[0084] In this embodiment, firstly, based on the first allocation relationship between multiple tasks and multiple server cores within the first task allocation control scheme, the server core assigned to each task is determined. Then, by calculating the ratio of the security requirement parameters of each task to the core external interaction rate of the assigned server core, multiple first data security coefficients are obtained. This ratio reflects the data security level of each task under the current allocation; the larger the ratio, the more secure the task's data security is under this allocation method.

[0085] Secondly, weights are assigned based on the magnitude of multiple security requirement parameters. Tasks with higher security requirement parameters should receive greater weights. A first data leakage security value is obtained by weighting multiple first data security coefficients.

[0086] For example, suppose there are three tasks A, B, and C, with security requirement parameters of 8, 5, and 3 respectively, and corresponding first data security coefficients of 0.6, 0.4, and 0.2 respectively. Task A is assigned a weight of 0.5, task B a weight of 0.3, and task C a weight of 0.2. Then the first data leakage security value is 0.6 × 0.5 + 0.4 × 0.3 + 0.2 × 0.2 = 0.46.

[0087] Next, the processing power requirements for multiple tasks are obtained, i.e., the computing resources needed to complete each task. Simultaneously, the available computing power of multiple server cores is obtained, i.e., the computing resources currently available from each core, resulting in multiple computing power occupancy ratios for multiple server cores under the first task allocation control scheme. The maximum computing power occupancy ratio is selected as the base first cache attack coefficient. Since server cores are more vulnerable to cache attacks when their computing power occupancy is too high, using the maximum value as the base coefficient can more effectively assess the risk of cache attacks.

[0088] Then, based on multiple task characteristics, the basic first cache attack coefficient is calculated and corrected. Different tasks have different requirements for memory read and write operations during execution, affecting the probability of cache attacks. By combining task characteristics to correct the basic coefficient, a more accurate first cache attack security value can be obtained.

[0089] Finally, based on the first data leakage security value and the first cache attack security value, the first control fitness is obtained by calculating the average. The first fitness value integrates security factors from both data leakage and cache attack perspectives, and can comprehensively measure the merits of the first task allocation control scheme.

[0090] For example, if the first data leakage security value is 0.46 and the first cache attack security value is 0.38, then the first control fitness is (0.46+0.38) / 2=0.42.

[0091] Furthermore, the basic first cache attack coefficient is modified to obtain a first cache attack security value, including:

[0092] The basic first cache attack security value is calculated based on the basic first cache attack coefficient.

[0093] Calculate the repetition of the multiple task features as the task concentration.

[0094] Obtain the average task concentration of task processing within the historical events of a multi-core server;

[0095] The first cache attack security value is obtained by correcting the basic first cache attack security value based on the ratio of the task concentration to the average task concentration.

[0096] In this embodiment, firstly, the basic first cache attack security value is calculated based on the basic first cache attack coefficient. The basic first cache attack security value can be obtained by calculating "1 - basic first cache attack coefficient".

[0097] Secondly, the redundancy of multiple task features is calculated to obtain task concentration. Task features include task type, execution time, memory usage patterns, etc. By analyzing and comparing these features, the proportion of tasks with the same features out of the total number of tasks is counted. This proportion serves as a measure of task concentration; the higher the proportion of duplicate task features, the more likely it is a cache attack task launched by an attacker.

[0098] Next, obtain the average task concentration of task processing within the historical events of the multi-core server. Statistical analysis is performed on all tasks processed by the server over a past period to calculate the task concentration for each task, and then the average concentration is calculated. The average task concentration reflects the task distribution of the server during its historical operation.

[0099] Finally, a correction calculation is performed by multiplying the ratio of task concentration to average task concentration by the base first cache attack security value. If the ratio is greater than 1, it indicates that the current task concentration is higher than the historical average, which may increase the risk of buffer attacks. In this case, the base first cache attack security value should be appropriately reduced. Conversely, if the ratio is less than 1, it indicates that the current task distribution is relatively dispersed, and the risk of buffer attacks is relatively low. In this case, the base first cache attack security value can be appropriately increased. Through this correction calculation, a more realistic first cache attack security value can be obtained, thereby more accurately evaluating the merits of the first task allocation control scheme.

[0100] For example, assuming the base first cache attack coefficient is 0.2, then the base first cache attack security value is 1 - 0.2 = 0.8. Analysis of multiple task characteristics yields a task concentration of 0.6, while the average task concentration of a multi-core server in historical events is 0.5. The ratio of task concentration to the average task concentration is 0.6 ÷ 0.5 = 1.2, which is greater than 1, indicating that the current task concentration is higher than the historical average, increasing the risk of cache attacks.

[0101] The ratio is multiplied by the base first cache attack security value for correction calculation, resulting in a first cache attack security value of 0.8 × 1.2 = 0.96. Combined with the first data leakage security value calculated earlier, the mean (0.46 + 0.96) ÷ 2 = 0.71 is calculated, and finally the first control fitness is obtained as 0.71.

[0102] This fitness value can be compared with the control fitness obtained in subsequent iterations to determine whether the task allocation control scheme is moving towards a better direction. As the iteration continues, task allocation is constantly adjusted and control fitness is recalculated until the convergence condition is met, thereby determining the optimal task allocation control scheme and further improving the task processing efficiency of multi-core servers under data security protection.

[0103] S40: Control multiple server cores to process multiple tasks according to the optimal task allocation control scheme.

[0104] In this embodiment, after obtaining the optimal task allocation control scheme, multiple server cores are controlled to process multiple tasks according to this scheme. During the control process, an effective task scheduling mechanism is established. When a task enters the system, the scheduler accurately allocates the task to the corresponding server core according to the optimal task allocation control scheme.

[0105] Simultaneously, the operational status of the server cores is monitored in real time. By monitoring indicators such as server core load, temperature, and energy consumption, it is ensured that the server cores operate in a safe and stable state. If an anomaly is detected in a server core, such as excessive load or excessive temperature, adjustments need to be made promptly. For example, some tasks can be migrated to other cores with lower loads to ensure the stability and reliability of the entire system.

[0106] Specifically, step S40 in the method includes:

[0107] According to the optimal task allocation control scheme, multiple tasks are allocated to multiple server cores for task processing.

[0108] In this embodiment, multiple tasks are allocated to multiple server cores according to the optimal task allocation control scheme. During the allocation process, it is ensured that each task is assigned to the most suitable server core. For tasks with high security requirements, they are assigned to cores with low external interaction rates and appropriate processing capabilities to ensure data security; for tasks with low security requirements, they are assigned to cores with higher external interaction rates to improve core utilization.

[0109] In summary, compared to existing technologies, this application comprehensively considers multiple factors such as security requirements parameters for various tasks, core external interaction rates, task execution characteristics, and core processing capabilities. It employs various algorithms and strategies for iterative optimization of task allocation to obtain the optimal task allocation control scheme. This scheme not only ensures data security and reduces the risk of data leakage and buffer attacks, but also maximizes the task processing efficiency of multi-core servers.

[0110] In summary, the embodiments of this application have at least the following technical effects:

[0111] This application provides a multi-core server control method based on data security. First, considering the data security requirements of multi-core server tasks and the external interactions of the server core, it obtains the security requirement parameters of the tasks and the external interaction rate of the cores, laying the foundation for subsequent reasonable task allocation. Second, by optimizing task allocation control and analyzing data leakage security values ​​and buffer attack security values, it effectively reduces the risk of data leakage and the possibility of buffer attacks, improving the security of server operation. Third, it processes tasks according to the optimal task allocation control scheme, ensuring that each task is assigned to the most suitable server core, thus guaranteeing data security while improving task processing efficiency to a certain extent. Finally, it dynamically adjusts task allocation based on different tasks and server conditions to adapt to constantly changing business needs and security challenges, providing strong support for the stable and secure operation of multi-core servers in various fields. Through the above technical solutions, this application achieves the scientific and reasonable allocation of multi-core server tasks, improving the overall performance of the server while ensuring data security.

[0112] Example 2, as Figure 2 As shown, based on the same inventive concept as the data-secure multi-core server control method provided in Embodiment 1, this application also provides a data-secure multi-core server control system, including:

[0113] The information acquisition module 11 is used to acquire multiple tasks currently to be processed by the multi-core server, collect the characteristics of multiple tasks, perform data security requirement analysis, and obtain multiple security requirement parameters.

[0114] Interaction analysis module 12 is used to obtain the processing logs of multiple server cores within the most recent preset time range, perform interaction analysis, and obtain the external interaction rate of multiple cores;

[0115] The scheme optimization module 13 is used to optimize task allocation control based on multiple security requirement parameters and multiple core external interaction rates to obtain the optimal task allocation control scheme. In this scheme, the data leakage security value and buffer attack security value of the task allocation control scheme are analyzed and optimized. The optimal task allocation control scheme includes the optimal allocation relationship between multiple tasks and multiple server cores.

[0116] The task processing module 14 is used to control multiple server cores to process multiple tasks according to the optimal task allocation control scheme.

[0117] In one embodiment, the information acquisition module 11 is specifically used for:

[0118] Get multiple tasks currently pending processing on the multi-core server and collect multiple task characteristics, where each task characteristic includes task source and task type;

[0119] The security requirement classifier is invoked, and the multiple task features are input respectively to obtain multiple security requirement parameters.

[0120] Furthermore, in one embodiment of the application, the construction step of the security requirement classifier includes:

[0121] Based on the server task processing records over a historical period, a set of sample task features is collected, and the task security requirement parameters of different sample task features are labeled to obtain a set of sample security requirement parameters.

[0122] Using task source and task type as classification features, and based on a decision tree, a security requirement classifier is constructed using the sample task feature set and sample security requirement parameter set. The classification nodes of the security requirement classifier are task source or task type, and the classification result is the security requirement parameter.

[0123] In one embodiment, the interactive analysis module 12 is specifically used for:

[0124] Obtain processing logs from multiple server cores within a recent preset time range. Each processing log contains the data source for the tasks processed within the recent preset time range.

[0125] Based on external data sources within multiple processing logs, multiple core external interaction rates are obtained.

[0126] Furthermore, in one embodiment of the application, multiple core external interaction rates are obtained based on external data sources within multiple processing logs, including:

[0127] Calculate the proportion of internal and external data sources in multiple processing logs as multiple core external interaction rates, where the data source is either external or internal.

[0128] In one embodiment, the scheme optimization module 13 is specifically used for:

[0129] Multiple tasks are randomly assigned to multiple server cores to obtain the first task allocation control scheme;

[0130] Based on multiple security requirement parameters and multiple core external interaction rates, the first control fitness of the first task allocation control scheme is calculated.

[0131] Continue iterative optimization of task allocation control. After convergence, obtain the optimal task allocation control scheme with the highest control fitness. The optimal task allocation control scheme includes the optimal allocation relationship between multiple tasks and multiple server cores.

[0132] Furthermore, in one embodiment, the first control fitness of the first task allocation control scheme is calculated based on multiple security requirement parameters and multiple core external interaction rates, including:

[0133] Based on the first allocation relationship between multiple tasks and multiple server cores within the first task allocation control scheme, calculate the ratio of the security requirement parameters of each task to the core external interaction rate of the allocated server core to obtain multiple first data security coefficients.

[0134] The first data leakage security value is obtained by weighting the multiple first data security coefficients according to the magnitude of multiple security requirement parameters.

[0135] The processing power required for multiple tasks and the spare computing power of multiple server cores are obtained. The computing power occupancy ratio of multiple server cores under the first task allocation control scheme is obtained, and the maximum computing power occupancy ratio is selected as the basic first cache attack coefficient.

[0136] Based on multiple task characteristics, the basic first cache attack coefficient is calculated and corrected to obtain the first cache attack security value.

[0137] The first control fitness is calculated based on the first data leakage security value and the first cache attack security value.

[0138] Furthermore, in one embodiment, the basic first cache attack coefficient is modified to obtain a first cache attack security value, including:

[0139] The basic first cache attack security value is calculated based on the basic first cache attack coefficient.

[0140] Calculate the repetition of the multiple task features as the task concentration.

[0141] Obtain the average task concentration of task processing within the historical events of a multi-core server;

[0142] The first cache attack security value is obtained by correcting the basic first cache attack security value based on the ratio of the task concentration to the average task concentration.

[0143] In one embodiment of the application, the task processing module 14 is specifically used for:

[0144] According to the optimal task allocation control scheme, multiple tasks are allocated to multiple server cores for task processing.

[0145] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0146] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0147] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A multi-core server control method based on data security, characterized in that, The method includes: The system retrieves multiple tasks currently pending processing on a multi-core server, collects characteristics of these tasks, performs data security requirements analysis, and obtains multiple security requirements parameters. Obtain the processing logs of multiple server cores within the most recent preset time range, perform interactive analysis, and obtain the external interaction rate of multiple cores; Based on multiple security requirement parameters and multiple core external interaction rates, task allocation control is optimized to obtain the optimal task allocation control scheme. This includes analyzing the data leakage security value and cache attack security value of the task allocation control scheme and optimizing them. The optimal task allocation control scheme includes the optimal allocation relationship between multiple tasks and multiple server cores. According to the optimal task allocation control scheme, multiple server cores are controlled to process multiple tasks. Among these, task allocation control is optimized based on multiple security requirement parameters and multiple core external interaction rates to obtain the optimal task allocation control scheme, including: Multiple tasks are randomly assigned to multiple server cores to obtain the first task allocation control scheme; Based on multiple security requirement parameters and multiple core external interaction rates, the first control fitness of the first task allocation control scheme is calculated. Continue iterative optimization of task allocation control, and after convergence, obtain the optimal task allocation control scheme with the highest control fitness. The calculation of the first control fitness of the first task allocation control scheme, based on multiple security requirement parameters and multiple core external interaction rates, includes: Based on the first allocation relationship between multiple tasks and multiple server cores within the first task allocation control scheme, calculate the ratio of the security requirement parameters of each task to the core external interaction rate of the allocated server core to obtain multiple first data security coefficients. The first data leakage security value is obtained by weighting the multiple first data security coefficients according to the magnitude of multiple security requirement parameters. The processing power required for multiple tasks and the spare computing power of multiple server cores are obtained. The computing power occupancy ratio of multiple server cores under the first task allocation control scheme is obtained, and the maximum computing power occupancy ratio is selected as the basic first cache attack coefficient. Based on multiple task characteristics, the basic first cache attack coefficient is calculated and corrected to obtain the first cache attack security value. The first control fitness is calculated based on the first data leakage security value and the first cache attack security value.

2. The multi-core server control method based on data security according to claim 1, characterized in that, The system retrieves multiple tasks currently pending processing on a multi-core server, collects characteristics of these tasks, performs data security requirements analysis, and obtains multiple security requirement parameters, including: Get multiple tasks currently pending processing on the multi-core server and collect multiple task characteristics, where each task characteristic includes task source and task type; The security requirement classifier is invoked, and the multiple task features are input respectively to obtain multiple security requirement parameters.

3. The multi-core server control method based on data security according to claim 2, characterized in that, The steps for constructing the security requirement classifier include: Based on the server task processing records over a historical period, a set of sample task features is collected, and the task security requirement parameters of different sample task features are labeled to obtain a set of sample security requirement parameters. Using task source and task type as classification features, and based on a decision tree, a security requirement classifier is constructed using the sample task feature set and sample security requirement parameter set. The classification nodes of the security requirement classifier are task source or task type, and the classification result is the security requirement parameter.

4. The multi-core server control method based on data security according to claim 1, characterized in that, Obtain processing logs from multiple server cores within a recent preset time range, perform interactive analysis, and obtain the external interaction rate of multiple cores, including: Obtain processing logs from multiple server cores within a recent preset time range. Each processing log contains the data source for the tasks processed within the recent preset time range. Based on external data sources within multiple processing logs, multiple core external interaction rates are obtained.

5. The multi-core server control method based on data security according to claim 4, characterized in that, Based on external data sources within multiple processing logs, several core external interaction rates are obtained, including: Calculate the proportion of internal and external data sources in multiple processing logs as multiple core external interaction rates, where the data source is either external or internal.

6. The multi-core server control method based on data security according to claim 1, characterized in that, Based on multiple task characteristics, the basic first cache attack coefficient is modified to obtain a first cache attack security value, including: The basic first cache attack security value is calculated based on the basic first cache attack coefficient. Calculate the repetition of the multiple task features as the task concentration. Obtain the average task concentration of task processing within the historical events of a multi-core server; The first cache attack security value is obtained by correcting the basic first cache attack security value based on the ratio of the task concentration to the average task concentration.

7. The multi-core server control method based on data security according to claim 1, characterized in that, According to the optimal task allocation control scheme, multiple server cores are controlled to process multiple tasks, including: According to the optimal task allocation control scheme, multiple tasks are allocated to multiple server cores for task processing.

8. A multi-core server control system based on data security, characterized in that, The method for implementing the data security-based multi-core server control method according to any one of claims 1-7 includes: The information acquisition module is used to acquire multiple tasks currently pending processing on the multi-core server, collect the characteristics of multiple tasks, perform data security requirement analysis, and obtain multiple security requirement parameters. The interaction analysis module is used to obtain the processing logs of multiple server cores within the most recent preset time range, perform interaction analysis, and obtain the external interaction rate of multiple cores. The scheme optimization module is used to optimize task allocation control based on multiple security requirement parameters and multiple core external interaction rates to obtain the optimal task allocation control scheme. In this process, the data leakage security value and cache attack security value of the task allocation control scheme are analyzed and optimized. The optimal task allocation control scheme includes the optimal allocation relationship between multiple tasks and multiple server cores. The task processing module is used to control multiple server cores to process multiple tasks according to the optimal task allocation control scheme.

Images