Node configuration method and device of distributed system and electronic equipment

Abstract

The invention discloses a node configuration method and device of a distributed system and electronic equipment, and relates to the technical field of data operation and maintaining.The node configuration method comprises the steps that the reliability requirement of a target user is received; analyzing all nodes of the distributed system by adopting the target reliability model to obtain a reliability value of the distributed system; based on the target attenuation model, adjusting the target reliability model to obtain an adjusted target reliability model; determining the number of target nodes of the distributed system based on a preset target cost function, a preset constraint condition and the adjusted target reliability model; and performing node configuration on the distributed system based on the initial node number and the target node number of the distributed system. According to the method and the device, the technical problem of resource waste caused by incapability of dynamic balance between system reliability and cost effectiveness in related technologies is solved.

Description

Technical Field

[0001] This invention relates to the field of data operation and maintenance technology, and more specifically, to a node configuration method, apparatus, and electronic device for a distributed system. Background Technology

[0002] With the development of information technology and big data applications, the data processing and storage capabilities of systems are facing challenges, and the demand for system availability and data consistency continues to rise. To ensure service stability and continuity, the voting system in a distributed system architecture has become a critical component. Voting systems typically employ an odd-numbered node deployment model (at least three nodes), utilizing inter-node collaborative voting mechanisms and consensus protocols to synchronize states across nodes. This ensures that the system can continue to provide services even if a single node fails, thereby guaranteeing service continuity and controlling data consistency.

[0003] However, voting systems primarily employ static node configuration, meaning the number of nodes is fixed at the initial deployment stage. Increasing the number of nodes improves system fault tolerance and availability. While this method simplifies operation and maintenance, it fails to consider the impact of dynamic load changes on the effectiveness of resource allocation and cost control. As the number of nodes increases, system complexity also rises, leading to increased network load and higher message transmission latency. This not only increases operational costs, but over- or under-configuration of resources can also result in low system performance and weaken overall system reliability.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a node configuration method, apparatus, and electronic device for a distributed system, to at least solve the technical problem of resource waste caused by the inability to dynamically balance system reliability and cost-effectiveness in related technologies.

[0006] According to one aspect of the present invention, a node configuration method for a distributed system is provided. The distributed system includes at least multiple nodes, including: receiving reliability requirements from a target user, wherein the reliability requirements include at least a numerical value of a reliability index that the distributed system needs to achieve during operation within a preset time period; analyzing all nodes of the distributed system using a target reliability model to obtain a reliability value of the distributed system, wherein the reliability value is the probability value of the distributed system operating normally within the preset time period; adjusting the target reliability model based on a target decay model to obtain an adjusted target reliability model, wherein the target decay model is obtained by screening multiple preset decay models; determining the target number of nodes in the distributed system based on a preset target cost function, preset constraints, and the adjusted target reliability model, wherein the reliability value of the distributed system is recalculated using the adjusted target reliability model to obtain the adjusted reliability value, and the preset constraints are determined based on the index value and the adjusted reliability value; configuring nodes in the distributed system based on the initial number of nodes and the target number of nodes, wherein the initial number of nodes is the number of nodes in the distributed system before node configuration.

[0007] Furthermore, before analyzing all nodes of the distributed system using the target reliability model to obtain the reliability value of the distributed system, the process includes: determining whether each node in the distributed system can operate independently; if each node can operate independently, determining the structure type of the distributed system, wherein the structure type includes at least one of the following: serial system, parallel system, and voting system, each structure type corresponding to a different preset reliability model; counting the number of all nodes in the distributed system to determine the initial number of nodes; and determining the target reliability model based on the structure type and the preset reliability model corresponding to the structure type.

[0008] Furthermore, after determining whether each node in the distributed system can run independently, the process also includes: analyzing the dependencies between each node if each node cannot run independently; decoupling the nodes indicated by the dependencies to obtain the nodes that can run independently.

[0009] Furthermore, the step of determining the target reliability model based on the structure type and the corresponding preset reliability model includes: when the structure type is a series system, determining the first preset reliability model corresponding to the series system as the target reliability model; or, when the structure type is a parallel system, determining the second preset reliability model corresponding to the parallel system as the target reliability model; or, when the structure type is a voting system, determining the third preset reliability model corresponding to the voting system as the target reliability model.

[0010] Furthermore, before adjusting the target reliability model based on the target decay model to obtain the adjusted target reliability model, the process includes: for each preset decay model, determining the node variable parameters of the preset decay model and determining the decay coefficient of the preset decay model; determining the initial reliability value of each node in the distributed system; constructing a preset decay model based on the initial reliability value, node variable parameters, and decay coefficient, wherein the preset decay model includes at least: a first preset decay model, a second preset decay model, and a third preset decay model; and screening the first preset decay model, the second preset decay model, and the third preset decay model to determine the target decay model.

[0011] Furthermore, the steps for determining the target number of nodes in the distributed system based on the preset target cost function, preset constraints, and the adjusted target reliability model include: obtaining installation cost data for each node; determining a first preset cost based on the installation cost data; obtaining operation and maintenance data for each node; determining a second preset cost based on the operation and maintenance data; determining a preset target cost function based on node variable parameters, the first preset cost, and the second preset cost; iterating the preset target cost function using a preset optimization algorithm to obtain the current number of nodes; for each iteration, calculating an adjusted reliability value based on the adjusted target reliability model and the current number of nodes; and determining the target number of nodes based on the adjusted reliability value and the indicator value.

[0012] Furthermore, the step of determining the target number of nodes based on the adjusted reliability value and the index value includes: defining the condition that the adjusted reliability value is greater than or equal to the index value as a preset constraint; for each iteration, recording the current number of nodes if the preset constraint is met; if the preset constraint is not met, continuing to iterate on the preset target cost function until a preset termination iteration condition is met, wherein the preset termination iteration condition is used to control the number of iterations of the preset target cost function; comparing the size of all current node numbers, and determining the smallest current node number as the target number of nodes.

[0013] According to another aspect of the present invention, a node configuration apparatus for a distributed system is also provided, comprising: a receiving unit for receiving reliability requirements from a target user, wherein the reliability requirements include at least: a value of a reliability index that the distributed system needs to achieve during operation within a preset time period; an analysis unit for analyzing all nodes of the distributed system using a target reliability model to obtain a reliability value of the distributed system, wherein the reliability value is a probability value that the distributed system operates normally within the preset time period; an adjustment unit for adjusting the target reliability model based on a target decay model to obtain an adjusted target reliability model, wherein the target decay model is obtained by screening multiple preset decay models; a determination unit for determining the target number of nodes in the distributed system based on a preset target cost function, preset constraints, and the adjusted target reliability model, wherein the reliability value of the distributed system is recalculated using the adjusted target reliability model to obtain the adjusted reliability value, and the preset constraints are determined based on the index value and the adjusted reliability value; and a configuration unit for configuring nodes in the distributed system based on the initial number of nodes and the target number of nodes, wherein the initial number of nodes is the number of nodes in the distributed system before node configuration.

[0014] Furthermore, the node configuration device includes: a first judgment module, used to determine whether each node in the distributed system can operate independently before analyzing all nodes of the distributed system using a target reliability model to obtain the reliability value of the distributed system; and, if each node can operate independently, determining the structure type of the distributed system, wherein the structure type includes at least one of the following: a series system, a parallel system, and a voting system, each structure type corresponding to a different preset reliability model; a first determination module, used to count the number of all nodes in the distributed system and determine the initial number of nodes; and a second determination module, used to determine the target reliability model based on the structure type and the preset reliability model corresponding to the structure type.

[0015] Furthermore, the node configuration device also includes: a first analysis module, used to analyze the dependency relationship between each node after determining whether each node in the distributed system can run independently, and in the case that each node cannot run independently; and a first decoupling module, used to decouple the nodes indicated by the dependency relationship to obtain independently running nodes.

[0016] Furthermore, the second determining module includes: a first determining submodule, used to determine the first preset reliability model corresponding to the series system as the target reliability model when the structure type is a series system; or, a second determining submodule, used to determine the second preset reliability model corresponding to the parallel system as the target reliability model when the structure type is a parallel system; or, a third determining submodule, used to determine the third preset reliability model corresponding to the voting system as the target reliability model when the structure type is a voting system.

[0017] Furthermore, the node configuration device also includes: a third determining module, used to determine the node variable parameters of the preset attenuation model and the attenuation coefficient of the preset attenuation model for each preset attenuation model before adjusting the target reliability model based on the target attenuation model to obtain the adjusted target reliability model; a fourth determining module, used to determine the initial reliability value of each node in the distributed system; a first constructing module, used to construct a preset attenuation model based on the initial reliability value, node variable parameters and attenuation coefficient, wherein the preset attenuation model includes at least: a first preset attenuation model, a second preset attenuation model and a third preset attenuation model; and a first filtering module, used to filter the first preset attenuation model, the second preset attenuation model and the third preset attenuation model to determine the target attenuation model.

[0018] Further, the determining unit includes: a fifth determining module, used to acquire installation cost data for each node and determine a first preset cost based on the installation cost data; a first acquiring module, used to acquire operation and maintenance data for each node and determine a second preset cost based on the operation and maintenance data; a sixth determining module, used to determine a preset target cost function based on node variable parameters, the first preset cost, and the second preset cost; a first iteration module, used to iterate the preset target cost function using a preset optimization algorithm to obtain the current number of nodes; a first calculation module, used to calculate an adjusted reliability value for each iteration based on the adjusted target reliability model and the current number of nodes; and a seventh determining module, used to determine the target number of nodes based on the adjusted reliability value and the indicator value.

[0019] Furthermore, the seventh determining module includes: a fourth determining submodule, used to determine the condition that the adjusted reliability value is greater than or equal to the index value as a preset constraint condition; a first recording submodule, used to record the current number of nodes for each iteration, provided that the preset constraint condition is met; a first iteration submodule, used to continue iterating on the preset target cost function if the preset constraint condition is not met, until a preset termination iteration condition is met, wherein the preset termination iteration condition is used to control the number of iterations on the preset target cost function; and a first comparison submodule, used to compare the size of all current node numbers and determine the smallest current node number as the target node number.

[0020] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the node configuration method of any of the above-described distributed systems.

[0021] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any of the above-described node configuration methods for a distributed system.

[0022] In this invention, the reliability requirements of the target user are received; a target reliability model is used to analyze all nodes of the distributed system to obtain the reliability value of the distributed system; the target reliability model is adjusted based on the target decay model to obtain the adjusted target reliability model; the target number of nodes in the distributed system is determined based on the preset target cost function, preset constraints, and the adjusted target reliability model; and the nodes of the distributed system are configured based on the initial number of nodes and the target number of nodes, thereby solving the technical problem of resource waste caused by the inability to dynamically balance system reliability and cost-effectiveness in related technologies.

[0023] In this invention, specific reliability requirements from target users are received. These requirements include reliability index values ​​for the distributed system within a specific time period. Based on a target reliability model, all nodes in the system are analyzed to assess the system's reliability under the current configuration. Then, the target reliability model is adjusted using a target decay model to obtain an adjusted target reliability model. This target decay model is selected from multiple pre-defined decay models to more accurately reflect the impact of increasing the number of nodes on system reliability. Based on a preset target cost function, preset constraints, and the adjusted target reliability model, an optimization algorithm determines the target number of nodes, ensuring that the cost is minimized while meeting reliability indicators. Subsequently, based on the initial and target number of nodes in the distributed system, node configuration optimization is performed, including adding or removing nodes, to ensure that the actual configuration meets the dual objectives of low cost-effectiveness and high reliability. By constructing and adjusting the reliability model, quantifying cost analysis, and applying optimization algorithms, accurate adjustment of the distributed system's node configuration is achieved, balancing system reliability and cost-effectiveness, thereby reducing resource waste. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0025] Figure 1 This is a flowchart of an optional node configuration method for a distributed system according to an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of an optional system reliability assessment and cost optimization framework based on a decay model according to an embodiment of the present invention;

[0027] Figure 3 This is a flowchart illustrating an optional reliability analysis module performing reliability analysis according to an embodiment of the present invention.

[0028] Figure 4 This is a flowchart of an optional optimization algorithm for finding the optimal solution according to an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of an optional node configuration device for a distributed system according to an embodiment of the present invention;

[0030] Figure 6 This is a hardware structure block diagram of an electronic device (or mobile device) for a node configuration method in a distributed system according to an embodiment of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] It should be noted that all related information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, and displayed data) collected and involved in this invention are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data comply with the relevant laws, regulations, and standards of the relevant regions, necessary confidentiality measures have been taken, and it does not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse. For example, this system has an interface with relevant users or organizations. Before obtaining relevant information, a request to obtain the information needs to be sent to the aforementioned user or organization through the interface, and the relevant information is obtained only after receiving consent from the aforementioned user or organization.

[0034] In this invention, an atomized analysis model is used to decouple the computation of complex system architectures, evaluate the reliability of the system under different node configurations, and, by using a decay model that reflects the different interaction characteristics between multiple nodes, more accurately analyze the reliability changes of the system when the number of nodes increases, thus improving the accuracy of system reliability analysis. Simultaneously, considering the cost model and under the constraint of meeting reliability requirements, an optimization model is designed to obtain a node deployment strategy that minimizes costs. By coupling the reliability decay model with the cost model, under the constraint of target reliability, an optimization algorithm is used to dynamically solve for the optimal solution, thereby achieving optimal resource allocation and cost minimization, improving resource utilization efficiency, and avoiding resource waste.

[0035] The present invention will now be described in detail with reference to various embodiments.

[0036] Example 1

[0037] According to an embodiment of the present invention, an embodiment of a node configuration method for a distributed system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0038] Figure 1 This is a flowchart of an optional node configuration method for a distributed system according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0039] Step S101: Receive the reliability requirements of the target user, wherein the reliability requirements include at least the indicator values ​​of the reliability indicators that the distributed system needs to achieve during operation within a preset time period.

[0040] In this embodiment, the target user has a clear reliability requirement for the distributed system (the numerical value of the reliability index that the distributed system needs to achieve when running within a preset time period). The reliability requirement is received from the target user (the business party, system owner, etc. who have specific requirements for system performance). This reliability requirement is usually given in numerical form, such as "the system downtime does not exceed 0.1% in one year", that is, the system can run normally for 99.9% of the time.

[0041] Step S102: Analyze all nodes of the distributed system using the target reliability model to obtain the reliability value of the distributed system, where the reliability value is the probability value that the distributed system will work normally within a preset time period.

[0042] Alternatively, when performing reliability analysis on a distributed system, the distributed system can be abstracted into: a serial system, a parallel system, and a voting system.

[0043] In a series system, all nodes must function properly to ensure the overall system operates correctly. The overall reliability R of the series system is... series The computational model can be represented as R series =R1×R2×…×R n Where R1 represents the reliability of the first node, R2 represents the reliability of the second node, and R... n This represents the reliability of the nth node.

[0044] In a parallel system, as long as any one node in the system is working properly, the entire system can operate normally. The overall reliability R of the parallel system is... parallel The computational model can be represented as Among them, R i Let i represent the reliability of the i-th node, where i = 1, ..., n, and n represents the n-th node.

[0045] In a voting system, the system can operate normally as long as any k out of n nodes can function properly. The reliability R of the voting system is... k-out-of-n The computational model can be represented as Among them, R i Let i represent the reliability of the i-th node, where i = k, ..., n.

[0046] In this embodiment, a target reliability model needs to be selected based on the actual configuration of the system and node data. Based on the target reliability model, the probability (i.e., reliability value) that the distributed system will work normally within a preset time period (a given time period that can be set by the user) can be calculated, and the health status and potential failure rate of the system can be measured.

[0047] Step S103: Based on the target attenuation model, the target reliability model is adjusted to obtain the adjusted target reliability model. The target attenuation model is obtained by screening multiple preset attenuation models.

[0048] Optionally, in a multi-node voting system, the interaction between nodes and the propagation of faults can cause the system reliability to decline as the number of nodes increases. To accurately assess this decline trend, three models can be used for analysis: exponential decline, logarithmic decline, and power-law decline.

[0049] In this embodiment, the target decay model is the model that best matches the current system environment and specific requirements selected from multiple preset decay models (such as exponential decay model, logarithmic decay model and power law decay model). It is used to quantify the negative impact of the increase in nodes on system reliability, especially the enhanced interaction between nodes.

[0050] Optionally, the exponential decay model describes the situation where system reliability decreases exponentially with time or scale. In a multi-node voting system, as the number of nodes increases, the interaction between nodes and fault propagation lead to an exponential decay in system reliability. The formula for the exponential decay model can be expressed as follows: Among them, R exp (n) represents the node reliability adjusted based on the exponential decay model, R0 is the initial reliability of a single node (factory set, each node has the same reliability), n is the number of nodes in the system, and λ exp It is the decay coefficient of the exponential model, representing the strength of the interaction between nodes.

[0051] Optionally, the logarithmic decay model is used to describe the situation where the system reliability decreases at a logarithmic rate as the number of nodes increases. This model is suitable for systems with weak interactions between nodes, but whose reliability gradually decreases as the system size increases. The formula for the logarithmic decay model can be expressed as follows: Among them, R log (n) represents the node reliability adjusted based on the logarithmic decay model, R0 is the initial reliability of a single node, n is the number of nodes in the system, and λ log It is the logarithmic model decay coefficient, representing the strength of the interaction between nodes.

[0052] Optionally, the power-law decay model is used to describe the situation where the system reliability decreases at a power-law rate as the number of nodes increases. This model is suitable for systems with complex interactions between nodes and whose influence changes significantly with scale. The formula for the power-law decay model can be expressed as follows: Among them, R pow (n) represents the node reliability adjusted based on the power-law decay model, R0 is the initial reliability of a single node, n is the number of nodes in the system, and λ pow It is the attenuation coefficient of the power-law model, representing the strength of the interaction between nodes.

[0053] In this embodiment, the target reliability model can be modified based on the target attenuation model to obtain an adjusted target reliability model that better reflects the actual system operation and the characteristics of interactions between nodes.

[0054] For example, if the target decay model is an exponential decay model The target reliability model is the model corresponding to the voting system. When the number of nodes n equals 1, then R in the target reliability model can be... i Replace with R0. When n is not equal to 1, then R can be... i Replace with

[0055] Step S104: Based on the preset target cost function, preset constraints, and the adjusted target reliability model, determine the target number of nodes in the distributed system. The reliability value of the distributed system is recalculated using the adjusted target reliability model to obtain the adjusted reliability value. The preset constraints are determined based on the index values ​​and the adjusted reliability value.

[0056] Optionally, a cost analysis model can be defined to assess the total costs associated with system deployment and maintenance (including initial deployment costs, operation and maintenance costs, and incremental costs due to system scaling).

[0057] The cost analysis model can be divided into initial investment cost and periodic operating cost. The initial investment cost is directly related to the number of machines and includes the cost of hardware, software, and installation configuration. The initial investment cost of a single node can be expressed as C. unit =C hardware +C software +C setup , where C hardware C represents the hardware cost of each node. softtware C represents the software cost per node. setup This represents the installation and configuration cost of each node. If the number of nodes is n, then the initial investment for all nodes is C. init =n×C unit .

[0058] Periodic operating costs include fixed maintenance costs and facility costs. The periodic operating cost of a single node can be expressed as C. oper =C maintenance +C facilities , where C maintenance C represents the cost of manpower, software maintenance, and other technical support for a single node. facilities This represents the infrastructure costs (such as power, water, and electricity) invested in a single node of a data center. If the number of nodes is n, then the periodic operating cost of all nodes is n×C. oper .

[0059] In this embodiment, a preset target cost function (i.e., Min C) is used. total =C init +C oper =n×(C unit +C operThis is used to calculate the total system cost (including initial investment cost and periodic operating cost), based on a preset target cost function and preset constraints (i.e., R(n) ≥ R). target The adjusted target reliability model can be used with a genetic algorithm as an optimization tool to determine the target number of nodes in the distributed system (i.e., the minimum number of nodes required to achieve the desired reliability index under given cost constraints), where R... target R(n) represents the reliability requirements of the target user, and is the adjusted reliability value obtained by recalculating using the adjusted target reliability model.

[0060] Step S105: Configure the nodes of the distributed system based on the initial number of nodes and the target number of nodes. The initial number of nodes is the number of nodes in the distributed system before node configuration.

[0061] In this embodiment, the initial number of nodes is the number of nodes in the distributed system before node configuration. Based on the initial number of nodes and the target number of nodes in the distributed system, node configuration can be performed. For example, if the target number of nodes is larger than the initial number of nodes, the node configuration needs to be reduced; if the target number of nodes is smaller than the initial number of nodes, the node configuration needs to be increased.

[0062] Optionally, after obtaining the target number of nodes, manual adjustments and re-evaluations can be made based on the actual situation to ensure the feasibility and efficiency of the solution in actual deployment.

[0063] Figure 2 This is a schematic diagram of an optional system reliability assessment and cost optimization framework based on a decay model according to an embodiment of the present invention, such as... Figure 2 As shown, users can input system reliability requirements. The reliability analysis module can perform reliability analysis on the system, assessing the system reliability under different structural types. Simultaneously, it can request a decay model to analyze how system reliability changes with the number of nodes (e.g., rapid decline, gradual decline, and significant decline). The decay model returns a decay impact assessment. At the same time, the reliability analysis module requests a cost analysis model to perform cost analysis, assessing the total cost of system deployment and maintenance, and requests an optimization algorithm to find the cost-reliability balance point (i.e., integrating the results of reliability analysis, decay model, and cost analysis to solve for the node deployment strategy that minimizes cost while achieving the system reliability target). The cost analysis model returns a cost analysis report, and the optimization algorithm can provide optimization solutions. Thus, the reliability analysis module can output the final deployment plan to the user.

[0064] In summary, this approach first identifies and clarifies the target user's specific requirements for system reliability. Then, it analyzes the current system state using a target reliability model, adjusts the system reliability using exponential, logarithmic, or power-law decay models, and, based on a preset target cost function, preset constraints, and the adjusted target reliability model, ensures the system meets or exceeds the target user's reliability indicators while dynamically determining the optimal number of nodes using a genetic optimization algorithm to minimize costs. Subsequently, based on the initial number of nodes and the optimized target number of nodes, the system's node configuration is precisely adjusted. By integrating reliability analysis, decay models, cost-benefit assessment, and genetic optimization algorithms into a comprehensive framework, this approach ensures the system meets high reliability requirements while achieving optimal economic efficiency, improving system resource utilization. This solves the technical problem of resource waste caused by the inability to dynamically balance system reliability and cost-effectiveness in related technologies.

[0065] To accurately determine the target reliability model, in the node configuration method of the distributed system provided in Embodiment 1 of this application, it is determined whether each node in the distributed system can run independently. If each node can run independently, the structure type of the distributed system is determined. The structure type includes at least one of the following: serial system, parallel system, and voting system. Each structure type corresponds to a different preset reliability model. The number of all nodes in the distributed system is counted to determine the initial number of nodes. Based on the structure type and the preset reliability model corresponding to the structure type, the target reliability model is determined.

[0066] In this embodiment, the independent operational capability of each node in the system can be tested and evaluated to check whether it can autonomously complete its functions without being affected by the state of other nodes. Assuming each node can operate independently, the structure type of the distributed system can be identified (i.e., whether it is a serial system, a parallel system, or a voting system). Different system structure types correspond to different preset reliability models. Serial systems emphasize the sequential execution of each node, requiring all nodes to be fault-free; parallel systems focus on redundancy, ensuring that at least one component is functioning correctly; while voting systems require the decision-making of a majority of nodes to maintain system state and data consistency. Clearly defining the system structure type helps in selecting an appropriate preset reliability model for subsequent calculations and analysis.

[0067] First, the number of all nodes in the distributed system is counted to determine the initial number of nodes. This initial count is used to calculate the difference between the target number of nodes and the target number, enabling effective and economical system configuration optimization. Then, based on the system structure and its corresponding preset reliability model (i.e., the three reliability calculation models mentioned above for series, parallel, and voting systems), the target reliability model can be determined. For example, for a voting system, the preset reliability model corresponding to the voting system can be used for reliability analysis.

[0068] For example, if the structure type is a series system, the target reliability model is R. series =R1×R2×...×R n If the structure type is a parallel system, the target reliability model is: If the structure type is a voting system, the target reliability model is:

[0069] In order to accurately obtain independently running nodes, in the node configuration method of the distributed system provided in Embodiment 1 of this application, when each node cannot run independently, the dependency relationship between each node is analyzed; the nodes indicated by the dependency relationship are decoupled to obtain independently running nodes.

[0070] In this embodiment, there may be close data interactions or control dependencies between nodes, causing the operating state of a single node to be affected by other nodes. When each node cannot operate independently, each node in the system is analyzed to identify the data flow and control dependencies between nodes (such as communication modes, data exchange frequencies, and recovery mechanisms in case of failure). The nodes indicated by the dependencies are decoupled to obtain independently operating nodes. Decoupling can be carried out in various ways, including but not limited to data localization, service independence, adding redundant storage, and using message queues to reduce direct dependencies between nodes, so that nodes can operate independently to a certain extent. By reducing direct dependencies, even if some nodes fail, the system can maintain basic service continuity and data consistency, thereby improving overall reliability.

[0071] To improve the accuracy of determining the target reliability model, in the node configuration method of the distributed system provided in Embodiment 1 of this application, when the structure type is a serial system, the first preset reliability model corresponding to the serial system is determined as the target reliability model; or, when the structure type is a parallel system, the second preset reliability model corresponding to the parallel system is determined as the target reliability model; or, when the structure type is a voting system, the third preset reliability model corresponding to the voting system is determined as the target reliability model.

[0072] In this embodiment, when the structure type is a series system, the first preset reliability model (i.e., R) corresponding to the series system can be used. series =R1×R2×...×R n If the target reliability model is determined, and the structural type is a parallel system, then the second preset reliability model corresponding to the parallel system (i.e., If the target reliability model is determined, and the structural type is a voting system, then the third preset reliability model corresponding to the voting system (i.e., This was determined as the target reliability model.

[0073] Figure 3 This is a flowchart illustrating an optional reliability analysis module performing reliability analysis according to an embodiment of the present invention, such as... Figure 3 As shown, the process first determines whether a node is independent. If a node is not independent, the dependencies between nodes are analyzed, and the nodes indicated by the dependencies are decoupled to obtain independently operating nodes (i.e., the system is decoupled into an independent structural system). If a node is independent, the system structure type is determined. If the system structure type is a series system, the first preset reliability model corresponding to the series system is determined as the target reliability model, and the reliability of the series system is calculated. If the structure type is a parallel system, the second preset reliability model corresponding to the parallel system is determined as the target reliability model, and the reliability of the parallel system is calculated. If the structure type is a voting system, the third preset reliability model corresponding to the voting system is determined as the target reliability model, and the reliability of the voting system is calculated.

[0074] To accurately determine the target attenuation model, in the node configuration method of the distributed system provided in Embodiment 1 of this application, for each preset attenuation model, the node variable parameters of the preset attenuation model are determined, and the attenuation coefficient of the preset attenuation model is determined; the initial reliability value of each node in the distributed system is determined; based on the initial reliability value, node variable parameters, and attenuation coefficient, a preset attenuation model is constructed, wherein the preset attenuation model includes at least: a first preset attenuation model, a second preset attenuation model, and a third preset attenuation model; the first preset attenuation model, the second preset attenuation model, and the third preset attenuation model are screened to determine the target attenuation model.

[0075] In this embodiment, the node variable parameters (i.e., n) of each preset attenuation model are determined, and the attenuation coefficient of each preset attenuation model is determined (e.g., the attenuation coefficient λ of the first preset attenuation model (i.e., the exponential model)). exp The second preset attenuation model (i.e., logarithmic model) has an attenuation coefficient λ. log The third preset attenuation model (i.e., power-law model) has an attenuation coefficient λ. powDetermine the initial reliability value (e.g., R0) for each node in the distributed system. Based on the initial reliability value, node variable parameters, and attenuation coefficient, construct a preset attenuation model (including a first preset attenuation model). Second preset attenuation model and the third preset attenuation model Based on the system requirements and the interaction characteristics between nodes, the first preset attenuation model, the second preset attenuation model, and the third preset attenuation model are screened to determine the target attenuation model.

[0076] To accurately determine the target number of nodes, the node configuration method for a distributed system provided in Embodiment 1 of this application obtains the installation cost data for each node and determines a first preset cost based on the installation cost data; obtains the operation and maintenance data for each node and determines a second preset cost based on the operation and maintenance data; determines a preset target cost function based on node variable parameters, the first preset cost, and the second preset cost; iterates the preset target cost function using a preset optimization algorithm to obtain the current number of nodes; for each iteration, calculates an adjusted reliability value based on the adjusted target reliability model and the current number of nodes; and determines the target number of nodes based on the adjusted reliability value and the indicator value.

[0077] In this embodiment, installation cost data (such as hardware cost C) is collected for each node. hardware Software purchase cost C softtware And installation and configuration costs C setup Based on installation cost data, the initial investment cost (i.e., the first preset cost C) is determined. unit C unit =C hardware +C software +C setup Collect operational data for each node (such as manpower, software maintenance, and other technical support costs). maintenance And the cost of infrastructure such as wind, thermal, and hydropower (C) facilities Based on operational data, determine the periodic operating costs (i.e., the second pre-set cost C). oper C oper =C maintenance +C facilities Based on the node variable parameters (i.e., n), the first preset cost, and the second preset cost, determine the preset target cost function (i.e., Min C). total =n×(C unit +C oper )).

[0078] In this embodiment, a preset optimization algorithm (such as genetic algorithm, gradient descent or other optimization algorithms) can be used to iterate the preset target cost function. The goal is to find the number of nodes with the lowest cost while meeting the system reliability requirements. In each iteration, the algorithm will try different numbers of nodes (i.e., the current number of nodes) and calculate the adjusted reliability value and the new cost value based on the adjusted target reliability model and the current number of nodes, until it converges to the optimal solution (i.e., obtain the target number of nodes). Through the iteration of the optimization algorithm, the best balance point between cost and reliability can be found.

[0079] Figure 4 This is a flowchart of an optional optimization algorithm for finding the optimal solution according to an embodiment of the present invention, such as... Figure 4 As shown, firstly, the optimization problem is defined. Then, the objective function (i.e., the preset objective cost function) and constraints (i.e., the preset constraints) are defined according to the needs of the optimization problem. At the same time, a decay model (i.e., the objective decay model) is introduced, and an optimization algorithm (such as a genetic algorithm) is selected. The algorithm process includes: initializing parameters, defining the fitness function, defining the genetic operation, and defining the termination condition. After that, the algorithm is implemented according to the optimization algorithm process, and the optimal solution is evaluated and selected.

[0080] To improve the accuracy of the target node count, in the node configuration method of the distributed system provided in Embodiment 1 of this application, the condition that the adjusted reliability value is greater than or equal to the index value is determined as a preset constraint condition; for each iteration, if the preset constraint condition is met, the current node count is recorded; if the preset constraint condition is not met, the preset target cost function is iterated until the preset termination iteration condition is met, wherein the preset termination iteration condition is used to control the number of iterations of the preset target cost function; the size of all current node counts is compared, and the smallest current node count is determined as the target node count.

[0081] In this embodiment, before the iterative optimization begins, a preset constraint condition needs to be defined (i.e., the adjusted reliability value is greater than or equal to a preset index value). This preset constraint condition is the minimum reliability requirement of the system, ensuring that the system reliability is not lower than the preset standard under any circumstances. For each iteration, if the preset constraint condition is met, the current number of nodes is recorded. If the preset constraint condition is not met, the preset target cost function continues to be iterated, and the number of nodes and related parameters are adjusted. During the iteration process, in order to avoid falling into an infinite loop, a preset termination iteration condition needs to be set (such as reaching the set maximum number of iterations or the change range of the target function gradually stabilizing). The iteration stops when the preset termination iteration condition is met, and the size of all current node numbers is compared. The smallest current node number is determined as the target node number. That is, under the premise of satisfying system reliability, this number of node configuration scheme can achieve the lowest total cost.

[0082] In this embodiment of the invention, the dependencies between nodes are first analyzed, and the nodes with dependencies are decoupled to obtain independently operating nodes. Then, the most suitable reliability model is determined according to the system structure type. By selecting a target attenuation model and combining node installation and maintenance cost data, a cost prediction model is constructed. An iterative optimization algorithm is used to minimize costs while meeting the system reliability constraints, thereby obtaining the optimal node deployment scheme. This dynamically balances system reliability and cost-effectiveness, effectively controls maintenance costs while ensuring service quality, and improves the utilization rate of system resources.

[0083] The following is a detailed description with reference to another embodiment.

[0084] Example 2

[0085] The node configuration device for a distributed system provided in this embodiment includes multiple implementation units, each of which corresponds to a specific implementation step in Embodiment 1 above.

[0086] Figure 5 This is a schematic diagram of an optional node configuration device for a distributed system according to an embodiment of the present invention, such as... Figure 5 As shown, the node configuration device of the distributed system may include: a receiving unit 50, an analysis unit 51, an adjustment unit 52, a determination unit 53, and a configuration unit 54.

[0087] The receiving unit 50 is used to receive the reliability requirements of the target user. The reliability requirements include at least the indicator values ​​of the reliability indicators that the distributed system needs to achieve during operation within a preset time period.

[0088] Analysis unit 51 is used to analyze all nodes of the distributed system using the target reliability model to obtain the reliability value of the distributed system, wherein the reliability value is the probability value of the distributed system working normally within a preset time period.

[0089] The adjustment unit 52 is used to adjust the target reliability model based on the target attenuation model to obtain the adjusted target reliability model, wherein the target attenuation model is obtained by screening multiple preset attenuation models.

[0090] The determining unit 53 is used to determine the target number of nodes in the distributed system based on the preset target cost function, preset constraints, and the adjusted target reliability model. The reliability value of the distributed system is recalculated through the adjusted target reliability model to obtain the adjusted reliability value. The preset constraints are determined based on the index values ​​and the adjusted reliability value.

[0091] Configuration unit 54 is used to configure nodes in the distributed system based on the initial number of nodes and the target number of nodes in the distributed system. The initial number of nodes is the number of nodes in the distributed system before node configuration.

[0092] The node configuration device of the aforementioned distributed system can receive the reliability requirements of the target user through the receiving unit 50, analyze all nodes of the distributed system using the target reliability model through the analysis unit 51 to obtain the reliability value of the distributed system, adjust the target reliability model based on the target decay model through the adjustment unit 52 to obtain the adjusted target reliability model, determine the target number of nodes of the distributed system through the determination unit 53 based on the preset target cost function, preset constraints and the adjusted target reliability model, and configure the nodes of the distributed system through the configuration unit 54 based on the initial number of nodes and the target number of nodes of the distributed system.

[0093] Optionally, the node configuration device includes: a first judgment module for determining whether each node in the distributed system can operate independently before analyzing all nodes of the distributed system using a target reliability model to obtain the reliability value of the distributed system; and, if each node can operate independently, determining the structure type of the distributed system, wherein the structure type includes at least one of the following: a series system, a parallel system, and a voting system, each structure type corresponding to a different preset reliability model; a first determination module for counting the number of all nodes in the distributed system and determining the initial number of nodes; and a second determination module for determining the target reliability model based on the structure type and the preset reliability model corresponding to the structure type.

[0094] Optionally, the node configuration device further includes: a first analysis module, used to analyze the dependency relationship between each node after determining whether each node in the distributed system can run independently, and in the case that each node cannot run independently; and a first decoupling module, used to decouple the nodes indicated by the dependency relationship to obtain independently running nodes.

[0095] Optionally, the second determining module includes: a first determining submodule, used to determine the first preset reliability model corresponding to the series system as the target reliability model when the structure type is a series system; or, a second determining submodule, used to determine the second preset reliability model corresponding to the parallel system as the target reliability model when the structure type is a parallel system; or, a third determining submodule, used to determine the third preset reliability model corresponding to the voting system as the target reliability model when the structure type is a voting system.

[0096] Optionally, the node configuration device further includes: a third determining module, used to determine the node variable parameters of the preset attenuation model and the attenuation coefficient of the preset attenuation model for each preset attenuation model before adjusting the target reliability model based on the target attenuation model to obtain the adjusted target reliability model; a fourth determining module, used to determine the initial reliability value of each node in the distributed system; a first constructing module, used to construct a preset attenuation model based on the initial reliability value, node variable parameters and attenuation coefficient, wherein the preset attenuation model includes at least: a first preset attenuation model, a second preset attenuation model and a third preset attenuation model; and a first filtering module, used to filter the first preset attenuation model, the second preset attenuation model and the third preset attenuation model to determine the target attenuation model.

[0097] Optionally, the determining unit 53 includes: a fifth determining module, used to acquire installation cost data for each node and determine a first preset cost based on the installation cost data; a first acquiring module, used to acquire operation and maintenance data for each node and determine a second preset cost based on the operation and maintenance data; a sixth determining module, used to determine a preset target cost function based on node variable parameters, the first preset cost, and the second preset cost; a first iteration module, used to iterate the preset target cost function using a preset optimization algorithm to obtain the current number of nodes; a first calculation module, used to calculate an adjusted reliability value for each iteration based on the adjusted target reliability model and the current number of nodes; and a seventh determining module, used to determine the target number of nodes based on the adjusted reliability value and the indicator value.

[0098] Optionally, the seventh determining module includes: a fourth determining submodule, used to determine the condition that the adjusted reliability value is greater than or equal to the index value as a preset constraint condition; a first recording submodule, used to record the current number of nodes for each iteration, provided that the preset constraint condition is met; a first iteration submodule, used to continue iterating on the preset target cost function if the preset constraint condition is not met, until a preset termination iteration condition is met, wherein the preset termination iteration condition is used to control the number of iterations of the preset target cost function; and a first comparison submodule, used to compare the size of all current node numbers and determine the smallest current node number as the target node number.

[0099] The node configuration device of the aforementioned distributed system may also include a processor and a memory. The receiving unit 50, the analysis unit 51, the adjustment unit 52, the determination unit 53, the configuration unit 54, etc., are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0100] The aforementioned processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, the distributed system's nodes can be configured based on the initial and target number of nodes.

[0101] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0102] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the node configuration method of any of the above-described distributed systems.

[0103] When a computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following steps: receiving the reliability requirements of the target user; analyzing all nodes of the distributed system using a target reliability model to obtain the reliability value of the distributed system; adjusting the target reliability model based on the target decay model to obtain an adjusted target reliability model; determining the target number of nodes in the distributed system based on a preset target cost function, preset constraints, and the adjusted target reliability model; and configuring the nodes of the distributed system based on the initial number of nodes and the target number of nodes. By integrating reliability analysis, decay models, cost modeling, and optimization algorithms, the overall availability of the system can be quantitatively evaluated and planned, resulting in an optimal node deployment strategy that balances reliability assurance and cost-effectiveness, providing quantitative decision-making for deployment schemes in real production environments.

[0104] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the node configuration method of the distributed system described above.

[0105] Figure 6 This is a hardware structure block diagram of an electronic device (or mobile device) for a node configuration method in a distributed system according to an embodiment of the present invention. Figure 6 As shown, an electronic device may include one or more processors (e.g., Figure 6 The processors 602a, 602b, ..., 602n, etc., may include, but are not limited to, processing devices such as microprocessors (MCUs) or programmable logic devices (FPGAs), and a memory 604 for storing data. In addition, it may include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 6 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device may also include components that are more... Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown.

[0106] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0107] The embodiments or examples disclosed herein are not exhaustive, but merely illustrative of some embodiments or examples, and are not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment or example can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment or example can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment or example can be arbitrarily interchanged. Furthermore, optional methods or examples in a particular embodiment or example can be arbitrarily combined; moreover, embodiments or examples can be arbitrarily combined. For example, some or all steps of different embodiments or examples can be arbitrarily combined, and a particular embodiment or example can be arbitrarily combined with optional methods or examples of other embodiments or examples.

[0108] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0109] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces; the indirect coupling or communication connection of units or modules can be electrical or other forms.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0112] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0113] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A node configuration method for a distributed system, characterized in that, The distributed system includes at least: multiple nodes, including: Receive the reliability requirements of the target user, wherein the reliability requirements include at least the indicator values ​​of the reliability indicators that the distributed system needs to achieve during operation within a preset time period; The target reliability model is used to analyze all the nodes of the distributed system to obtain the reliability value of the distributed system, wherein the reliability value is the probability value of the distributed system working normally within the preset time period; Based on the target attenuation model, the target reliability model is adjusted to obtain the adjusted target reliability model, wherein the target attenuation model is obtained by screening multiple preset attenuation models; Based on the preset target cost function, preset constraints, and the adjusted target reliability model, the target number of nodes in the distributed system is determined. The reliability value of the distributed system is recalculated using the adjusted target reliability model to obtain the adjusted reliability value. The preset constraints are determined based on the index values ​​and the adjusted reliability value. Based on the initial number of nodes and the target number of nodes in the distributed system, the distributed system is configured with nodes, wherein the initial number of nodes is the number of nodes in the distributed system before node configuration.

2. The node configuration method for a distributed system according to claim 1, characterized in that, Before analyzing all nodes of the distributed system using a target reliability model to obtain the reliability value of the distributed system, the process also includes: Determine whether each node in the distributed system can run independently. If each node can run independently, determine the structure type of the distributed system. The structure type includes at least one of the following: serial system, parallel system, and voting system. Each structure type corresponds to a different preset reliability model. Count the number of all nodes in the distributed system to determine the initial number of nodes; The target reliability model is determined based on the structure type and the preset reliability model corresponding to the structure type.

3. The node configuration method for a distributed system according to claim 2, characterized in that, After determining whether each node in the distributed system can operate independently, the method further includes: Analyze the dependencies between each node when each node cannot operate independently; The nodes indicated by the dependency relationship are decoupled to obtain the nodes that run independently.

4. The node configuration method for a distributed system according to claim 2, characterized in that, The step of determining the target reliability model based on the structure type and the corresponding preset reliability model includes: When the structure type is the series system, the first preset reliability model corresponding to the series system is determined as the target reliability model; or, When the structure type is the parallel system, the second preset reliability model corresponding to the parallel system is determined as the target reliability model; or, When the structure type is the voting system, the third preset reliability model corresponding to the voting system is determined as the target reliability model.

5. The node configuration method for a distributed system according to claim 1, characterized in that, Before adjusting the target reliability model based on the target attenuation model to obtain the adjusted target reliability model, the process further includes: For each of the preset attenuation models, determine the node variable parameters of the preset attenuation model and determine the attenuation coefficient of the preset attenuation model; Determine the initial reliability value for each node in the distributed system; Based on the initial reliability value, the node variable parameters, and the attenuation coefficient, the preset attenuation model is constructed, wherein the preset attenuation model includes at least: a first preset attenuation model, a second preset attenuation model, and a third preset attenuation model; The first preset attenuation model, the second preset attenuation model, and the third preset attenuation model are filtered to determine the target attenuation model.

6. The node configuration method for a distributed system according to claim 1, characterized in that, The steps for determining the target number of nodes in the distributed system based on a preset target cost function, preset constraints, and the adjusted target reliability model include: Obtain installation cost data for each node, and determine a first preset cost based on the installation cost data; Obtain the operation and maintenance data of each node, and determine the second preset cost based on the operation and maintenance data; The preset target cost function is determined based on the node variable parameters, the first preset cost, and the second preset cost; The preset target cost function is iterated using a preset optimization algorithm to obtain the current number of nodes; For each iteration, the adjusted reliability value is calculated based on the adjusted target reliability model and the current number of nodes; The target number of nodes is determined based on the adjusted reliability value and the indicator value.

7. The node configuration method for a distributed system according to claim 6, characterized in that, The step of determining the target number of nodes based on the adjusted reliability value and the indicator value includes: The condition that the adjusted reliability value is greater than or equal to the index value is determined as the preset constraint condition; For each iteration, under the premise that the preset constraints are met, record the current number of nodes; If the preset constraint is not met, the preset target cost function continues to be iterated until the preset termination iteration condition is met, wherein the preset termination iteration condition is used to control the number of iterations of the preset target cost function; Compare the size of all the current node counts, and determine the smallest current node count as the target node count.

8. A node configuration device for a distributed system, characterized in that, The distributed system includes at least: multiple nodes, including: A receiving unit is used to receive the reliability requirements of a target user, wherein the reliability requirements include at least the index values ​​of the reliability indicators that the distributed system needs to achieve during operation within a preset time period. The analysis unit is used to analyze all the nodes of the distributed system using a target reliability model to obtain the reliability value of the distributed system, wherein the reliability value is the probability value of the distributed system working normally within the preset time period. An adjustment unit is used to adjust the target reliability model based on the target attenuation model to obtain an adjusted target reliability model, wherein the target attenuation model is obtained by screening multiple preset attenuation models; The determining unit is used to determine the target number of nodes in the distributed system based on a preset target cost function, preset constraints, and the adjusted target reliability model. The reliability value of the distributed system is recalculated using the adjusted target reliability model to obtain the adjusted reliability value. The preset constraints are determined based on the index value and the adjusted reliability value. A configuration unit is used to configure nodes in the distributed system based on the initial number of nodes and the target number of nodes, wherein the initial number of nodes is the number of nodes in the distributed system before node configuration.

9. A computer program product, characterized in that, The system includes a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the node configuration method of the distributed system according to any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the node configuration method of the distributed system according to any one of claims 1 to 7.

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