A cross-vendor computing power routing protocol interoperability interface specification method
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU SUNNYSITE TECH CO LTD
- Filing Date
- 2026-03-13
- Publication Date
- 2026-06-16
Smart Images
Figure CN122226873A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of computing network convergence technology, and in particular to a method for specifying an interoperability interface for cross-vendor computing power routing protocols. Background Technology
[0002] In the process of converged development of computing and the network, interoperability of cross-vendor computing power routing is a core prerequisite for achieving efficient scheduling of computing resources. In existing technologies, there are significant differences in the multi-vendor computing power routing protocol stacks. For example, Vendor A uses a traditional TCP / IP-based protocol architecture, while Vendor B uses a lightweight QUIC-based protocol architecture. The lack of unified standards in syntax rules and semantic definitions leads to frequent issues such as field mismatches and semantic ambiguities during protocol parsing, resulting in an interoperability failure rate exceeding 25%. Furthermore, in cross-city scheduling scenarios, network parameters such as link bandwidth and latency change dynamically. Existing methods only use fixed scheduling parameters to calculate routing decisions, failing to adjust adaptation strategies based on real-time load. When the load difference exceeds 40%, route establishment time increases by more than three times, severely impacting scheduling efficiency. In addition, existing solutions lack effective closed-loop feedback mechanisms, relying solely on initial parameters for calculation without dynamically optimizing parameters based on actual interoperability results. After long-term operation, due to component aging, changes in the network environment, and parameter rigidity, the interoperability accuracy continuously declines, with the deviation rate gradually increasing from the initial 5% to 15%, failing to meet the high reliability and adaptability requirements of converged computing and the network.
[0003] Based on the above problems, there is an urgent need for a cross-vendor computing power routing protocol interoperability interface specification method that can solve the problems of multi-vendor protocol incompatibility, poor dynamic adaptation of cross-city scheduling, and rigid parameters. Summary of the Invention
[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and proposes a cross-vendor computing power routing protocol interoperability interface specification method. The specific technical solution is as follows:
[0005] Preferably, a method for cross-vendor computing power routing protocol interoperability interface specification includes collecting computing power demand parameters and network status parameters, and preprocessing the collected parameters;
[0006] By loading multi-vendor protocol parsing templates through a protocol parsing compatibility layer, syntax and semantic adaptation of different vendors' computing power routing protocol stacks is achieved. Basic interoperability coefficients are calculated based on the computing power requirement parameters and network state parameters. Load correction coefficients are calculated based on dynamic load parameters, and the basic interoperability coefficients are corrected using these load correction coefficients to obtain intermediate scheduling coefficients. Deviation correction coefficients are calculated based on feedback deviation parameters output by a closed-loop feedback mechanism, and the intermediate scheduling coefficients are corrected using these deviation correction coefficients to obtain the final routing decision value. The actual interoperability result corresponding to the final routing decision value is compared with a preset threshold using the closed-loop feedback mechanism, and the parameters in the calculation process of the basic interoperability coefficients and intermediate scheduling coefficients are adjusted according to the comparison results to achieve dynamic optimization of interoperability accuracy.
[0007] Preferably, the protocol parsing compatibility layer includes a template management unit, a dual-stack parallel parsing unit, and a semantic mapping unit. The template management unit stores protocol parsing templates from at least two vendors. The dual-stack parallel parsing unit simultaneously loads protocol parsing templates from both the target vendor and the local vendor, and performs parallel syntax parsing on the input protocol data. The semantic mapping unit establishes a semantic correspondence between the parsing results of the two protocols, mapping the computing power description field and routing control field in the target vendor's protocol to the unified format of the local vendor's protocol.
[0008] Preferably, the computing power requirement parameters include computing power type, computing power scale and service level requirements; the network status parameters include cross-city link bandwidth, link latency and link packet loss rate; the dynamic load parameters include local computing power node load rate, target computing power node load rate and link load rate; and the feedback deviation parameters include protocol parsing deviation rate, routing response deviation rate and data transmission deviation rate.
[0009] Preferably, the closed-loop feedback mechanism includes a result acquisition unit, a deviation calculation unit, and a parameter adjustment unit. The result acquisition unit acquires the actual interoperability results corresponding to the final routing decision value in real time, including protocol handshake success rate, route establishment time, and data transmission accuracy. The deviation calculation unit calculates the difference between the actual interoperability results and a preset threshold to obtain the feedback deviation parameter. The parameter adjustment unit determines the adjustment direction and magnitude of the parameters during the calculation of the basic interoperability coefficient and the intermediate scheduling coefficient based on the value of the feedback deviation parameter.
[0010] Preferably, the basic interoperability coefficients are calculated to satisfy the following formula:
[0011] ;
[0012] in, These are the basic interoperability coefficients, which are dimensionless and range from 0 to 1. The larger the value, the stronger the basic interoperability compatibility. This refers to computing power, measured in floating-point operations per second (FLOPS). The bandwidth of the intercity link is measured in gigabits per second (Gbps). Link latency, in milliseconds (ms); The link packet loss rate is dimensionless and ranges from 0 to 1. The protocol type influence coefficient is dimensionless and ranges from 0.3 to 0.7. It is used to quantify the impact of differences in protocol stack types from different vendors on interoperability. The syntax complexity of the target vendor's protocol is expressed in terms of the number of syntax rules, and is obtained by counting the number of rules defined in the protocol frame structure and fields. This represents the syntactic complexity of the standard protocol, expressed in terms of the number of syntactic rules, and its value is the preset number of syntactic rules for industry-standard protocols. The semantic difference impact coefficient is dimensionless and ranges from 0.2 to 0.5. It is used to quantify the impact of protocol semantic mapping deviations on interoperability. The semantic mapping deviation rate is dimensionless and ranges from 0 to 1. It is calculated by comparing the information loss rate and ambiguity rate of the fields before and after semantic mapping.
[0013] Preferably, the intermediate scheduling coefficient is calculated according to the following formula:
[0014] ;
[0015] in, This is the intermediate scheduling coefficient, which is dimensionless and ranges from 0 to 1. The larger the value, the stronger the dynamic scheduling adaptability. The load baseline impact coefficient is dimensionless and ranges from 0.1 to 0.4. It is used to quantify the basic impact of the overall load level on scheduling. This is the local computing node load rate, a dimensionless value ranging from 0 to 1, obtained by the ratio of the local node's used computing power to the total computing power. The target computing power node load rate is dimensionless and ranges from 0 to 1. It is obtained by the ratio of the target node's used computing power to the total computing power. The link load rate is dimensionless and ranges from 0 to 1. It is obtained by comparing the used bandwidth of the link with the total bandwidth. The load difference impact coefficient is dimensionless and ranges from 0.15 to 0.55. It is used to quantify the additional impact of the load difference between the local and target nodes on scheduling. The compensation value is calculated for the load; it is dimensionless and takes a value of 0.01, used to avoid... A calculation error occurs when the value is 0.
[0016] Preferably, the final routing decision value is calculated to satisfy the following formula:
[0017] ;
[0018] in, The final routing decision value is dimensionless and ranges from 0 to 1. The larger the value, the stronger the reliability and adaptability of the routing decision. This is the deviation benchmark correction coefficient, which is dimensionless and ranges from 0.2 to 0.6. It is used to quantify the degree of correction of the feedback deviation on the final decision. The standardized value of the protocol resolution deviation rate is dimensionless and ranges from 0 to 1. It is obtained by the ratio of the actual protocol resolution deviation rate to the maximum permissible deviation rate. The standardized value of the routing response deviation rate is dimensionless and ranges from 0 to 1. It is obtained by comparing the actual routing response deviation rate with the maximum allowable response deviation rate. The standardized value of the data transmission deviation rate is dimensionless and ranges from 0 to 1. It is obtained by the ratio of the actual data transmission deviation rate to the maximum allowable transmission deviation rate. The total deviation impact coefficient is dimensionless and ranges from 0.25 to 0.65. It is used to quantify the additional corrective impact on the decision-making process when the total deviation exceeds the threshold. The total deviation value of the feedback deviation parameter is dimensionless and ranges from 0 to 1. This is achieved through... , , The result is obtained by taking the arithmetic mean. The total feedback deviation threshold is dimensionless and has a value of 0.1, which is the preset upper limit of acceptable interoperability deviation.
[0019] Preferably, when preprocessing the collected computing power requirement parameters and network status parameters, an adaptive Kalman filter algorithm is used to remove random noise from the parameters. The output data update frequency of the preprocessing module is consistent with the parameter acquisition frequency, both being updated once every 2 seconds. When the rate of change of the link delay exceeds a preset value, the filter gain of the adaptive Kalman filter algorithm is automatically increased by 40%, the parameter acquisition frequency is automatically increased to once every 1 second, and the rate of change of the link delay is the ratio of the difference in link delay between two adjacent acquisition cycles to the acquisition cycle.
[0020] Preferably, when the bandwidth fluctuation of the intercity link exceeds 20% in extreme scenarios, the protocol parsing compatibility layer automatically enables the lightweight protocol parsing mode, disables unnecessary semantic verification fields, and reduces protocol parsing time; the adjustment range of the parameter adjustment unit automatically increases by 50%, prioritizing the adjustment of the basic interoperability coefficients. With the intermediate scheduling coefficients This ensures continuity of interoperability in extreme scenarios.
[0021] Preferably, the closed-loop feedback mechanism further includes a storage unit, which records the parameter values after each adjustment, the corresponding feedback deviation parameters, and the actual interoperability results to form a parameter optimization database. When a new scenario with a similarity of more than 85% to historical scenarios in the database is encountered, the parameter adjustment unit directly calls the corresponding optimized parameters in the database as initial values, and then makes fine adjustments based on the real-time data of the new scenario, thereby shortening the parameter adjustment time.
[0022] Technical effects:
[0023] This invention addresses the incompatibility of syntax and semantics between multiple vendors' protocol stacks by using multi-template loading and dual-stack parallel parsing in the protocol parsing compatibility layer; it solves the problem of poor cross-city scheduling adaptability by progressively calculating basic interoperability coefficients, intermediate scheduling coefficients, and final routing decision values, combined with the coupled correction of dynamic load and feedback deviation; and it solves the problem of parameter rigidity by using closed-loop feedback and a parameter optimization database, significantly improving the interoperability of computing power routing and scheduling stability, and meeting the open requirements of the computing network convergence ecosystem. Attached Figure Description
[0024] Figure 1 This application presents a flowchart of a cross-vendor computing power routing protocol interoperability interface specification method (5IU). Detailed Implementation
[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0026] The existing technology has the following technical problems: cross-vendor computing power routing has a high interoperability failure rate due to differences in protocol stacks; it cannot dynamically match load and network parameters when scheduling across cities; the lack of feedback leads to parameter rigidity; and it cannot meet the needs of computing network integration.
[0027] Based on this, this embodiment provides a method for specifying an interoperability interface for cross-vendor computing power routing protocols, including:
[0028] S1: Collect computing power demand parameters and network status parameters, and preprocess the collected parameters;
[0029] S2: Load multi-vendor protocol parsing templates through the protocol parsing compatibility layer to achieve syntax and semantic adaptation of different vendors' computing power routing protocol stacks;
[0030] S3: Calculate the basic interoperability coefficients based on the computing power requirement parameters and the network state parameters;
[0031] S4: Calculate the load correction coefficient based on the dynamic load parameters, and combine the load correction coefficient to correct the basic interoperability coefficient to obtain the intermediate scheduling coefficient;
[0032] S5: Calculate the deviation correction coefficient based on the feedback deviation parameter output by the closed-loop feedback mechanism, and correct the intermediate scheduling coefficient in combination with the deviation correction coefficient to obtain the final routing decision value;
[0033] S6: The actual interoperability result corresponding to the final routing decision value is compared with a preset threshold through the closed-loop feedback mechanism. Based on the comparison result, the parameters in the calculation process of the basic interoperability coefficient and the intermediate scheduling coefficient are adjusted to achieve dynamic optimization of interoperability accuracy.
[0034] The core of this technical solution is the construction of a three-layer architecture encompassing protocol adaptation, dynamic computation, and closed-loop optimization. The protocol parsing and compatibility layer breaks down vendor protocol barriers through multi-template management and dual-stack parallel parsing. For example, it can simultaneously load vendor A's TCP / IP protocol template and vendor B's QUIC protocol template, parse the protocol data in parallel, and achieve field unification through semantic mapping. The basic interoperability coefficient calculation integrates computing power requirements and network status, quantifying the impact of protocol differences and semantic deviations. The intermediate scheduling coefficient introduces dynamic load correction to adapt to load fluctuations in cross-city scenarios. The final routing decision value, combined with feedback deviations, ensures decision reliability. The closed-loop feedback mechanism avoids parameter stagnation during long-term operation through result comparison and parameter adjustment. During implementation, parameters such as computing power scale and link bandwidth are first collected through sensors and protocol interfaces. After preprocessing, these parameters are input into the protocol parsing and compatibility layer, sequentially completing adaptation, coefficient calculation, and correction, ultimately outputting the routing decision value. Simultaneously, the feedback mechanism continuously optimizes the parameters.
[0035] This solution addresses the issues of incompatibility between multiple vendor protocols, poor cross-city scheduling adaptability, and rigid parameters, thereby improving interoperability success rate and scheduling stability, and meeting the open and collaborative requirements of computing-network convergence.
[0036] The existing technology has the following technical problems: the protocol parsing compatibility layer lacks a specific module architecture design, and when parsing protocols from multiple vendors, problems such as chaotic template management, low parsing efficiency, and inaccurate semantic mapping are prone to occur, affecting the basic interoperability adaptation effect.
[0037] Based on this, the protocol parsing compatibility layer includes a template management unit, a dual-stack parallel parsing unit, and a semantic mapping unit. The template management unit stores protocol parsing templates from at least two vendors. The dual-stack parallel parsing unit simultaneously loads protocol parsing templates from both the target vendor and the local vendor, and performs parallel syntax parsing on the input protocol data. The semantic mapping unit establishes a semantic correspondence between the parsing results of the two protocols, mapping the computing power description field and routing control field in the target vendor's protocol to the unified format of the local vendor's protocol.
[0038] This technical solution clearly defines the functional division of the protocol parsing compatibility layer through modular design. The template management unit adopts a distributed storage architecture. Each vendor's protocol template contains information such as frame structure definition, field types, and verification rules. For example, it stores the parsing rules for the TCP / IP protocol frame header fields of vendor A and the QUIC protocol frame header fields of vendor B, supporting dynamic updates and version management of templates. The dual-stack parallel parsing unit uses a multi-core processor to achieve parallel computing. For example, it loads the target and local protocol templates through two CPU cores respectively, and simultaneously performs syntax parsing on the input protocol data packets to extract key fields such as computing power description and routing control, with parsing time controlled within 10ms. The semantic mapping unit pre-establishes a field mapping table. For example, it maps the computing power service level field in vendor B's protocol to the service quality level field of the local protocol. The information entropy algorithm is used to verify the integrity of the information before and after mapping, ensuring that the mapping deviation rate is less than 5%. During implementation, when the target vendor's protocol data is input, the template management unit calls the corresponding template, the dual-stack parallel parsing unit completes parallel parsing, and the semantic mapping unit outputs parsing results in a unified format, providing standardized data for subsequent coefficient calculations. This solution addresses the issues of chaotic and inefficient protocol parsing compatibility layer modules, as well as inaccurate mapping, laying a reliable foundation for interoperability and improving the efficiency and accuracy of protocol adaptation.
[0039] The existing technology has the following technical problems: In the interoperability of computing power routing, the parameter definition is vague and the classification is unclear, which leads to the lack of specificity of the collected data, and cannot provide comprehensive and accurate input for subsequent coefficient calculation, thus affecting the reliability of the calculation results.
[0040] Based on this, the computing power requirement parameters include computing power type, computing power scale and service level requirements; the network status parameters include cross-city link bandwidth, link latency and link packet loss rate; the dynamic load parameters include local computing power node load rate, target computing power node load rate and link load rate; and the feedback deviation parameters include protocol parsing deviation rate, routing response deviation rate and data transmission deviation rate.
[0041] This technical solution ensures the targeted and comprehensive nature of data collection by clearly defining parameter categories and specific content. Among the computing power requirement parameters, computing power type is categorized into CPU computing power, GPU computing power, AI computing power, etc., extracted from the computing power request commands submitted by the user; computing power scale is quantified by the number of floating-point operations per second, for example, obtaining a requirement of 500 TFLOPS from the request; service level requirements include latency-sensitive, throughput-sensitive, etc., determined by the QoS field in the request.
[0042] In network status parameters, intercity link bandwidth is collected in real time using a link tester, in Gbps; link latency is obtained using the ping command or a dedicated latency testing tool, in milliseconds; and link packet loss rate is obtained by statistically analyzing the ratio of lost data packets to total data packets within a certain time period, and is dimensionless. In dynamic load parameters, local computing node load rate is calculated as the ratio of local node's used computing power to total computing power; target computing node load rate is calculated based on the computing power usage data reported by the target node; and link load rate is calculated as the ratio of used link bandwidth to total bandwidth. In feedback deviation parameters, protocol parsing deviation rate is obtained as the ratio of the number of fields parsed incorrectly to the total number of fields; routing response deviation rate is obtained as the percentage difference between the actual response time and the preset response time; and data transmission deviation rate is obtained as the ratio of the amount of data transmitted incorrectly to the total amount of data. During implementation, dedicated data acquisition tools are used to collect the above parameters according to their categories, ensuring that each parameter is clearly defined and the data is accurate, providing reliable input for subsequent coefficient calculations.
[0043] This solution addresses the issues of ambiguous parameter definitions and unclear classifications, ensuring the relevance and comprehensiveness of the collected data and laying a data foundation for subsequent accurate calculations.
[0044] The existing technology has the following technical problems: the closed-loop feedback mechanism lacks specific unit composition and functional definition, the actual interoperability results are not fully collected, the deviation calculation is inaccurate, and the parameter adjustment has no clear basis, resulting in poor feedback optimization effect and inability to achieve dynamic improvement of interoperability accuracy.
[0045] Based on this, the closed-loop feedback mechanism includes a result acquisition unit, a deviation calculation unit, and a parameter adjustment unit. The result acquisition unit acquires the actual interoperability results corresponding to the final routing decision value in real time, including protocol handshake success rate, route establishment time, and data transmission accuracy. The deviation calculation unit calculates the difference between the actual interoperability results and a preset threshold to obtain the feedback deviation parameter. The parameter adjustment unit determines the adjustment direction and magnitude of the parameters during the calculation of the basic interoperability coefficient and the intermediate scheduling coefficient based on the value of the feedback deviation parameter.
[0046] This technical solution achieves precise operation of closed-loop feedback through modular design. The result acquisition unit uses real-time monitoring tools. The protocol handshake success rate is obtained by statistically analyzing the ratio of successful handshakes to the total number of attempts within a certain time period; for example, 95 successful attempts out of 100 result in a success rate of 95%. Route establishment time is recorded in milliseconds (ms) from initiating a route request to completing route establishment. Data transmission accuracy is obtained by statistically analyzing the ratio of correctly transmitted data to the total transmitted data. The deviation calculation unit presets thresholds for each result, such as a 90% threshold for protocol handshake success rate, a 500ms threshold for route establishment time, and a 98% threshold for data transmission accuracy. The actual results are compared with these thresholds; for example, if the actual handshake success rate is 92%, the absolute value of the protocol parsing deviation rate is calculated as the ratio of 90% to the absolute value (here, a negative deviation is calculated by taking the absolute value and then standardizing it); the route response deviation rate is calculated as the ratio of 500ms; and the data transmission deviation rate is calculated as the ratio of 98%. These results ultimately yield the feedback deviation parameters. The parameter adjustment unit establishes the correspondence between deviation values and adjustment strategies; for example, when the protocol parsing deviation rate exceeds 5%, the basic interoperability coefficient is adjusted. The impact coefficient of protocol type increases if the deviation rate is positive. To enhance the correction of protocol discrepancies; if the deviation rate is negative, then reduce... The adjustment range is determined based on the deviation rate. The larger the deviation rate, the larger the adjustment range. For example, the adjustment range is 5% when the deviation rate is 5% and 10% when the deviation rate is 10%.
[0047] During implementation, the result acquisition unit acquires interoperability results in real time, the deviation calculation unit accurately calculates deviation parameters, and the parameter adjustment unit formulates adjustment strategies based on the deviation, forming a complete feedback loop. This solution solves the problems of unclear and ambiguous functions in the closed-loop feedback mechanism, achieving comprehensive result acquisition, accurate deviation calculation, and evidence-based parameter adjustment, thus improving the dynamic optimization effect of interoperability accuracy.
[0048] The existing technology has the following technical problems: the calculation of basic interoperability coefficients does not quantify the impact of protocol differences and semantic deviations, and only relies on the simple ratio of computing power and network parameters, which cannot accurately reflect the comprehensive impact of multi-vendor protocols and network status on interoperability, resulting in large deviations in basic compatibility assessment.
[0049] Based on this, the basic interoperability coefficients are calculated to satisfy the following formula:
[0050]
[0051] in, These are the basic interoperability coefficients, which are dimensionless and range from 0 to 1. The larger the value, the stronger the basic interoperability compatibility. This refers to computing power, measured in floating-point operations per second (FLOPS). The bandwidth of the intercity link is measured in gigabits per second (Gbps). Link latency, in milliseconds (ms); The link packet loss rate is dimensionless and ranges from 0 to 1. The protocol type influence coefficient is dimensionless and ranges from 0.3 to 0.7. It is used to quantify the impact of differences in protocol stack types from different vendors on interoperability. The syntax complexity of the target vendor's protocol is expressed in terms of the number of syntax rules, and is obtained by counting the number of rules defined in the protocol frame structure and fields. This represents the syntactic complexity of the standard protocol, expressed in terms of the number of syntactic rules, and its value is the preset number of syntactic rules for industry-standard protocols. The semantic difference impact coefficient is dimensionless and ranges from 0.2 to 0.5. It is used to quantify the impact of protocol semantic mapping deviations on interoperability. The semantic mapping deviation rate is dimensionless and ranges from 0 to 1. It is calculated by comparing the information loss rate and ambiguity rate of the fields before and after semantic mapping.
[0052] This technical solution uses a multi-factor coupled formula design to accurately quantify basic interoperability compatibility. The formula consists of three parts: Part One This reflects the degree of matching between computing power requirements and network conditions. The larger the computing power scale, the higher the bandwidth, the lower the latency, and the lower the packet loss rate, the larger this value, and the stronger the basic adaptability. The second part... The impact of differences in quantifiable protocol types: if the target vendor's protocol syntax complexity is higher than the standard protocol, then... If positive, the exponent term decreases. reduce, The larger the value, the more significant the impact of protocol differences; Part Three The impact of quantified semantic mapping bias is smaller when the semantic mapping bias rate is higher. reduce, The larger the value, the more significant the impact of semantic bias. In implementation, for example... TFLOPS Gbps, ms, , , strip, strip, , The first part is The second part is The third part is ,final Due to numerical scaling, the values here need to be normalized to the 0-1 range during actual calculation to accurately reflect the basic interoperability compatibility. This scheme solves the problem of ignoring protocol and semantic differences in the calculation of basic interoperability coefficients, enabling accurate evaluation of multi-factor coupling and providing a reliable foundation for subsequent scheduling corrections.
[0053] The existing technology has the following technical problems: the calculation of intermediate scheduling coefficients does not take into account the impact of dynamic load, especially the additional impact of the load difference between the local node and the target node on scheduling, resulting in poor scheduling adaptability when the load fluctuates in cross-city scenarios and failing to match the actual load status.
[0054] Based on this, the intermediate scheduling coefficients are calculated according to the following formula:
[0055]
[0056] in, This is the intermediate scheduling coefficient, which is dimensionless and ranges from 0 to 1. The larger the value, the stronger the dynamic scheduling adaptability. The load baseline impact coefficient is dimensionless and ranges from 0.1 to 0.4. It is used to quantify the basic impact of the overall load level on scheduling. This is the local computing node load rate, a dimensionless value ranging from 0 to 1, obtained by the ratio of the local node's used computing power to the total computing power. The target computing power node load rate is dimensionless and ranges from 0 to 1. It is obtained by the ratio of the target node's used computing power to the total computing power. The link load rate is dimensionless and ranges from 0 to 1. It is obtained by comparing the used bandwidth of the link with the total bandwidth. The load difference impact coefficient is dimensionless and ranges from 0.15 to 0.55. It is used to quantify the additional impact of the load difference between the local and target nodes on scheduling. The compensation value is calculated for the load; it is dimensionless and takes a value of 0.01, used to avoid... A calculation anomaly occurs when the load value is 0. This technical solution achieves dynamic scheduling adaptation through the coupling correction of the load benchmark and the load difference.
[0057] In the formula, Extract the maximum load rate in the local, target nodes and links to reflect the overall load level. The higher the maximum load rate, the greater the reduction in scheduling adaptability is required. To quantify the impact of load difference, if the target node's load rate is higher than the local node's, then... If the exponent is positive, the exponent increases, further reducing scheduling adaptability and avoiding scheduling to high-load nodes; if the target node's load rate is lower than the local load rate, the exponent decreases, the reduction in scheduling adaptability is smaller, and it is more conducive to scheduling to low-load nodes. The basic correction level for controlling the overall load. Additional correction force to control load difference; This avoids the problem of a denominator of 0 when the local load rate is 0. In implementation, for example... , , , , , , ,but The value is 0.8. The exponent term is The correction item is ,final This reflects a decrease in adaptability when scheduling to high-load target nodes; if ,but The exponent term is The correction item is , This reflects the improved adaptability when scheduling to low-load nodes.
[0058] This solution addresses the issue of intermediate scheduling coefficients ignoring dynamic load and load difference, enabling precise adaptation to load fluctuations in cross-city scenarios and improving the dynamic rationality of scheduling.
[0059] The existing technology has the following technical problems: the calculation of the final routing decision value does not take into account the comprehensive impact of feedback deviation, and it is impossible to correct the decision based on the actual interoperability results, resulting in low decision reliability and easy routing adaptation errors.
[0060] Based on this, the final routing decision value is calculated to satisfy the following formula:
[0061] ;
[0062] in, The final routing decision value is dimensionless and ranges from 0 to 1. The larger the value, the stronger the reliability and adaptability of the routing decision. This is the deviation benchmark correction coefficient, which is dimensionless and ranges from 0.2 to 0.6. It is used to quantify the degree of correction of the feedback deviation on the final decision. The standardized value of the protocol resolution deviation rate is dimensionless and ranges from 0 to 1. It is obtained by the ratio of the actual protocol resolution deviation rate to the maximum permissible deviation rate. The standardized value of the routing response deviation rate is dimensionless and ranges from 0 to 1. It is obtained by comparing the actual routing response deviation rate with the maximum allowable response deviation rate. The standardized value of the data transmission deviation rate is dimensionless and ranges from 0 to 1. It is obtained by the ratio of the actual data transmission deviation rate to the maximum allowable transmission deviation rate. The total deviation impact coefficient is dimensionless and ranges from 0.25 to 0.65. It is used to quantify the additional corrective impact on the decision-making process when the total deviation exceeds the threshold. The total deviation value of the feedback deviation parameter is dimensionless and ranges from 0 to 1. This is achieved through... , , The result is obtained by taking the arithmetic mean. The total feedback deviation threshold is dimensionless and has a value of 0.1, which is the preset upper limit of acceptable interoperability deviation.
[0063] This technical solution ensures the reliability of the final routing decision by coupling and correcting multi-dimensional feedback bias. In the formula, The root mean square value of the deviation comprehensively reflects the deviation level of protocol parsing, routing response, and data transmission. The larger the root mean square value, the stronger the basic correction. The additional impact of quantifying the total deviation exceeding a threshold is considered; if the total deviation exceeds the threshold, then... If the total deviation is positive, the exponential term increases, further strengthening the correction and lowering the decision value to avoid unreliable routes; if the total deviation is below the threshold, the exponential term decreases, and the correction weakens. The basic correction range for controlling deviation Control the additional correction magnitude for the portion exceeding the threshold.
[0064] During implementation, for example , , , , , The root mean square value is , , The exponent term is 1, and the correction term is... , ;like ,but The exponent term is The correction item is , This reflects the additional correction to the decision based on the threshold bias.
[0065] This solution addresses the issue of neglecting feedback bias in the final routing decision, achieving comprehensive correction of multi-dimensional biases and improving the reliability and adaptability of the decision.
[0066] The existing technology has the following technical problems: the noise removal effect during parameter preprocessing is poor, and the preprocessing strategy and acquisition frequency are not dynamically adjusted according to parameter changes, resulting in low parameter accuracy of input coefficient calculation. Especially when there are sudden changes in link delay, the data is not updated in time, which affects the real-time performance of the calculation results.
[0067] Based on this, when preprocessing the collected computing power requirement parameters and network status parameters, an adaptive Kalman filter algorithm is used to remove random noise from the parameters. The output data update frequency of the preprocessing module is consistent with the parameter acquisition frequency, which is once every 2 seconds. When the rate of change of the link delay exceeds a preset value, the filter gain of the adaptive Kalman filter algorithm is automatically increased by 40%, the parameter acquisition frequency is automatically increased to once every 1 second, and the rate of change of the link delay is the ratio of the difference in link delay between two adjacent acquisition cycles to the acquisition cycle.
[0068] This technical solution improves the accuracy and real-time performance of parameter preprocessing through adaptive filtering and dynamic frequency adjustment. The adaptive Kalman filter algorithm dynamically adjusts the filter gain by estimating the noise covariance matrix in real time. For example, when random noise in the link delay data increases, the filter gain is automatically adjusted to enhance noise suppression and ensure smooth output data. Under normal conditions, the parameter acquisition and preprocessing update frequency is 2 seconds / time, balancing accuracy and resource consumption. When the link delay change rate exceeds the preset value, for example, if the previous cycle delay is 50ms and the current cycle delay is 55ms, with an acquisition cycle of 2 seconds, the change rate is (55-50) / 2=2.5ms / second, far exceeding the preset value. At this time, the filter gain increases by 40%, enhancing the ability to track sudden changes in data and avoiding filtering lag. At the same time, the acquisition frequency is increased to 1 second / time, accelerating the data update speed and ensuring timely capture of delay changes, providing real-time and accurate parameters for coefficient calculation. During implementation, the preprocessing module integrates an adaptive Kalman filter algorithm to monitor the link delay change rate in real time. Based on the change rate, it triggers adjustments to the filter gain and acquisition frequency. For example, when a sudden congestion occurs in an intercity link, it can quickly respond to sudden delay changes and output accurate delay data.
[0069] This solution addresses the issues of poor noise removal and lack of dynamic adjustment in parameter preprocessing, improving parameter accuracy and real-time performance, and providing high-quality input for coefficient calculation.
[0070] The existing technology has the following technical problems: In extreme scenarios, the protocol parsing and parameter adjustment strategies lack targeted optimization, resulting in increased protocol parsing time, untimely parameter adjustment, easy interruption of interoperability continuity, and inability to cope with extreme network environments.
[0071] Based on this, when the bandwidth fluctuation of the intercity link exceeds 20% in extreme scenarios, the protocol parsing compatibility layer automatically enables the lightweight protocol parsing mode, disables unnecessary semantic verification fields, and reduces protocol parsing time; the adjustment range of the parameter adjustment unit automatically increases by 50%, prioritizing the adjustment of the basic interoperability coefficients. With the intermediate scheduling coefficients This ensures interoperability continuity under extreme scenarios. The technical solution guarantees interoperability continuity through targeted optimizations for extreme scenarios.
[0072] The criterion for bandwidth fluctuation exceeding 20% is: the absolute value of the difference between the current link bandwidth and the bandwidth of the previous cycle exceeds 20% of the bandwidth of the previous cycle. For example, if the bandwidth of the previous cycle was 10Gbps and the bandwidth of the current cycle is 7Gbps, the fluctuation is 30%, triggering extreme scenario handling. The lightweight mode of the protocol parsing compatibility layer disables unnecessary semantic verification fields, such as redundant check bits and historical data fields in vendor protocols, retaining only the core computing power description and routing control fields. Parsing time is reduced from the usual 10ms to less than 5ms, improving parsing efficiency. The adjustment range of the parameter adjustment unit is increased from the usual 5%-10% to 7.5%-15%, accelerating parameter adaptation and prioritizing adjustments. The protocol type impact coefficient is adjusted to enhance the flexibility of protocol adaptation. The load baseline impact factor is adjusted to accommodate load changes caused by bandwidth fluctuations, for example, by increasing... To address protocol differences arising from changes in protocol parsing modes, and to increase... To reduce scheduling adaptability under high load fluctuations and avoid resource overload, the link monitoring module calculates bandwidth fluctuations in real time. When the fluctuation exceeds a threshold, it triggers lightweight parsing and adjustment. For example, when a sudden drop in bandwidth occurs on an intercity link due to a surge in traffic, the parsing and parameters are quickly optimized to ensure uninterrupted interoperability.
[0073] This solution addresses the issues of slow protocol parsing and delayed parameter adjustment in extreme scenarios, ensuring the continuity and stability of interoperability in extreme network environments.
[0074] The existing technology has the following technical problems: the closed-loop feedback mechanism does not utilize historical data, and each new scenario requires adjustment from the initial parameters. Parameter optimization is time-consuming and cannot quickly adapt to similar historical scenarios, affecting interoperability efficiency.
[0075] Based on this, the closed-loop feedback mechanism also includes a storage unit, which records the parameter values after each adjustment, the corresponding feedback deviation parameters, and the actual interoperation results to form a parameter optimization database. When a new scenario with a similarity of more than 85% to the historical scenario in the database is encountered, the parameter adjustment unit directly calls the corresponding optimization parameters in the database as the initial values, and then makes fine adjustments based on the real-time data of the new scenario to shorten the parameter adjustment time.
[0076] This technical solution improves parameter optimization efficiency by reusing historical data and matching it to specific scenarios. The storage unit uses a relational database, and each record contains the adjusted parameters (such as...). , , etc.), feedback deviation parameters (such as , The similarity is calculated using the cosine similarity algorithm, comparing the feature vectors of the new scenario with those of historical scenarios. For example, a new scenario might have a computing power of 1000 TFLOPS, bandwidth of 10Gbps, and a load rate of 0.5, while a historical scenario might have a computing power of 950 TFLOPS, bandwidth of 9.5Gbps, and a load rate of 0.48. If the similarity exceeds 85%, the scenario is considered similar. The parameter adjustment unit uses optimized parameters from similar historical scenarios as initial values, such as the optimized parameters from the historical scenario. , For new scenarios, this value is used directly, and then fine-tuned based on real-time feedback deviations (e.g., adjusted to...). , The adjustment time has been reduced from the usual 10 collection cycles (20 seconds) to 3 collection cycles (6 seconds). During implementation, the storage unit updates the database periodically. When a new scenario appears, a similarity calculation is triggered. If a match is successful, historical parameters are called. For example, when encountering a cross-city computing power scheduling scenario similar to the previous month, historical optimization parameters can be quickly reused to reduce adjustment time.
[0077] This solution addresses the issues of closed-loop feedback not utilizing historical data and time-consuming parameter optimization, enabling rapid adaptation to similar historical scenarios and improving interoperability efficiency and parameter optimization speed.
[0078] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.
Claims
1. A method for specifying an interoperability interface for cross-vendor computing power routing protocols, characterized in that, include: Collect computing power demand parameters and network status parameters, and preprocess the collected parameters; By loading multi-vendor protocol parsing templates through the protocol parsing compatibility layer, the syntax and semantics of different vendors' computing power routing protocol stacks are adapted. Calculate the basic interoperability coefficients based on the computing power requirement parameters and the network state parameters; The load correction coefficient is calculated based on the dynamic load parameters, and the intermediate scheduling coefficient is obtained by correcting the basic interoperability coefficient based on the load correction coefficient. The deviation correction coefficient is calculated based on the feedback deviation parameter output by the closed-loop feedback mechanism, and the intermediate scheduling coefficient is corrected by the deviation correction coefficient to obtain the final routing decision value. The closed-loop feedback mechanism compares the actual interoperability result corresponding to the final routing decision value with a preset threshold, and adjusts the parameters in the calculation process of the basic interoperability coefficient and the intermediate scheduling coefficient based on the comparison result, thereby achieving dynamic optimization of interoperability accuracy.
2. The method for cross-vendor computing power routing protocol interoperability interface specification according to claim 1, characterized in that, The protocol parsing compatibility layer includes a template management unit, a dual-stack parallel parsing unit, and a semantic mapping unit. The template management unit stores protocol parsing templates from at least two vendors. The dual-stack parallel parsing unit simultaneously loads protocol parsing templates from both the target vendor and the local vendor, and performs parallel syntax parsing on the input protocol data. The semantic mapping unit establishes a semantic correspondence between the parsing results of the two protocols, mapping the computing power description field and routing control field in the target vendor's protocol to the unified format of the local vendor's protocol.
3. The method for cross-vendor computing power routing protocol interoperability interface specification according to claim 1, characterized in that, The computing power requirement parameters include computing power type, computing power scale and service level requirements; the network status parameters include cross-city link bandwidth, link latency and link packet loss rate; the dynamic load parameters include local computing power node load rate, target computing power node load rate and link load rate; and the feedback deviation parameters include protocol parsing deviation rate, routing response deviation rate and data transmission deviation rate.
4. The method for cross-vendor computing power routing protocol interoperability interface specification according to claim 1, characterized in that, The closed-loop feedback mechanism includes a result acquisition unit, a deviation calculation unit, and a parameter adjustment unit. The result acquisition unit acquires the actual interoperability results corresponding to the final routing decision value in real time, including protocol handshake success rate, route establishment time, and data transmission accuracy. The deviation calculation unit calculates the difference between the actual interoperability result and the preset threshold to obtain the feedback deviation parameter; the parameter adjustment unit determines the adjustment direction and adjustment range of the parameters during the calculation of the basic interoperability coefficient and the intermediate scheduling coefficient based on the magnitude of the feedback deviation parameter.
5. The method for cross-vendor computing power routing protocol interoperability interface specification according to claim 1, characterized in that, The basic interoperability coefficients are calculated according to the following formula: ； in, These are the basic interoperability coefficients, which are dimensionless and range from 0 to 1. The larger the value, the stronger the basic interoperability compatibility. This refers to computing power, measured in floating-point operations per second (FLOPS). The bandwidth of the intercity link is measured in gigabits per second (Gbps). Link latency, in milliseconds (ms); The link packet loss rate is dimensionless and ranges from 0 to 1. The protocol type influence coefficient is dimensionless and ranges from 0.3 to 0.
7. It is used to quantify the impact of differences in protocol stack types from different vendors on interoperability. The syntax complexity of the target vendor's protocol is expressed in terms of the number of syntax rules, and is obtained by counting the number of rules defined in the protocol frame structure and fields. This represents the syntactic complexity of the standard protocol, expressed in terms of the number of syntactic rules, and its value is the preset number of syntactic rules for industry-standard protocols. The semantic difference impact coefficient is dimensionless and ranges from 0.2 to 0.
5. It is used to quantify the impact of protocol semantic mapping deviations on interoperability. The semantic mapping deviation rate is dimensionless and ranges from 0 to 1. It is calculated by comparing the information loss rate and ambiguity rate of the fields before and after semantic mapping.
6. The method for cross-vendor computing power routing protocol interoperability interface specification according to claim 1, characterized in that, The intermediate scheduling coefficients are calculated according to the following formula: ； in, This is the intermediate scheduling coefficient, which is dimensionless and ranges from 0 to 1. The larger the value, the stronger the dynamic scheduling adaptability. The load baseline impact coefficient is dimensionless and ranges from 0.1 to 0.
4. It is used to quantify the basic impact of the overall load level on scheduling. This is the local computing node load rate, a dimensionless value ranging from 0 to 1, obtained by the ratio of the local node's used computing power to the total computing power. The target computing power node load rate is dimensionless and ranges from 0 to 1. It is obtained by the ratio of the target node's used computing power to the total computing power. The link load rate is dimensionless and ranges from 0 to 1. It is obtained by comparing the used bandwidth of the link with the total bandwidth. The load difference impact coefficient is dimensionless and ranges from 0.15 to 0.
55. It is used to quantify the additional impact of the load difference between the local and target nodes on scheduling. The compensation value is calculated for the load; it is dimensionless and takes a value of 0.01, used to avoid... A calculation error occurs when the value is 0.
7. The method for cross-vendor computing power routing protocol interoperability interface specification according to claim 1, characterized in that, The final routing decision value is calculated to satisfy the following formula: ； in, The final routing decision value is dimensionless and ranges from 0 to 1. The larger the value, the stronger the reliability and adaptability of the routing decision. This is the deviation benchmark correction coefficient, which is dimensionless and ranges from 0.2 to 0.
6. It is used to quantify the degree of correction of the feedback deviation on the final decision. The standardized value of the protocol resolution deviation rate is dimensionless and ranges from 0 to 1. It is obtained by the ratio of the actual protocol resolution deviation rate to the maximum permissible deviation rate. The standardized value of the routing response deviation rate is dimensionless and ranges from 0 to 1. It is obtained by comparing the actual routing response deviation rate with the maximum allowable response deviation rate. The standardized value of the data transmission deviation rate is dimensionless and ranges from 0 to 1. It is obtained by the ratio of the actual data transmission deviation rate to the maximum allowable transmission deviation rate. The total deviation impact coefficient is dimensionless and ranges from 0.25 to 0.
65. It is used to quantify the additional corrective impact on the decision-making process when the total deviation exceeds the threshold. The total deviation value of the feedback deviation parameter is dimensionless and ranges from 0 to 1. This is achieved through... , , The result is obtained by taking the arithmetic mean. The total feedback deviation threshold is dimensionless and has a value of 0.1, which is the preset upper limit of acceptable interoperability deviation.
8. The method for cross-vendor computing power routing protocol interoperability interface specification according to claim 1, characterized in that, When preprocessing the collected computing power requirement parameters and network status parameters, an adaptive Kalman filter algorithm is used to remove random noise from the parameters. The output data update frequency of the preprocessing module is consistent with the parameter acquisition frequency, which is once every 2 seconds. When the rate of change of the link delay exceeds a preset value, the filter gain of the adaptive Kalman filter algorithm is automatically increased by 40%, the parameter acquisition frequency is automatically increased to once every 1 second, and the rate of change of the link delay is the ratio of the difference in link delay between two adjacent acquisition cycles to the acquisition cycle.
9. The method for specifying an interoperable interface for a cross-vendor computing power routing protocol according to claim 1, characterized in that, When cross-city links experience extreme scenarios with bandwidth fluctuations exceeding 20%, the protocol parsing compatibility layer automatically activates a lightweight protocol parsing mode, disabling unnecessary semantic verification fields to reduce protocol parsing time; the adjustment range of the parameter adjustment unit automatically increases by 50%, prioritizing adjustments to the basic interoperability coefficients. With the intermediate scheduling coefficients This ensures continuity of interoperability in extreme scenarios.
10. A method for specifying an interoperable interface for a cross-vendor computing power routing protocol according to claim 4, characterized in that, The closed-loop feedback mechanism also includes a storage unit, which records the parameter values after each adjustment, the corresponding feedback deviation parameters, and the actual interoperation results to form a parameter optimization database. When a new scenario with a similarity of more than 85% to the historical scenario in the database is encountered, the parameter adjustment unit directly calls the corresponding optimization parameters in the database as the initial values, and then makes fine adjustments based on the real-time data of the new scenario to shorten the parameter adjustment time.