Computing resource allocation method, system and program based on privacy computing

Abstract

The present disclosure relates to a computing resource allocation method, system and program based on privacy computing, which performs performance scoring on each computing node resource in a weighted average manner, analyzes the resource score range that meets application requirements by artificial intelligence methods, and hides the real resource performance of the resource supply end by generating a commitment and proving the commitment, thereby improving the privacy of computing resource allocation, and being able to verify whether the resource performance score is within a specified resource demand range without leaking the resource performance score, thereby ensuring the privacy protection and efficiency of the verification process, and by allocating the computing node resources to the user end according to the resource request, the computing node resources corresponding to the requirements can be allocated with the user end without leaking the specific performance parameters of the computing node resources, thereby improving the privacy and data security during computing node resource allocation.

Description

Technical Field

[0001] The present disclosure relates to the technical field of computing resource allocation, and in particular to a computing resource allocation method, system and program based on privacy computing. Background Art

[0002] Computing resource allocation refers to the process of rationally allocating and managing available resources such as CPU, memory, storage, and network bandwidth in a computing system to optimize task execution efficiency. The goal of computing resource allocation is to maximize resource utilization, minimize task execution time, and ensure system stability and availability. With the popularization of technologies such as artificial intelligence, the market demand for computing resources is growing exponentially. Under this circumstance, computing resource allocation technology is gaining increasing attention.

[0003] Traditional computing resource allocation technology is a resource allocation method based on performance indicator labeling, that is, each computing resource supplier publishes the performance indicators of the computing resources they own, and users screen according to the published performance indicators. After the screening, the computing resources of the screened suppliers are allocated. In actual applications, suppliers may worry that disclosing performance indicators will reveal their technological advantages and are unwilling to disclose this information. The resource allocation method based on performance indicator labeling may result in lower privacy when allocating computing resources. Summary of the invention

[0004] The present disclosure provides a computing resource allocation method, system and program based on privacy computing, which performs performance scoring on each computing node resource in a weighted average manner, uses artificial intelligence methods to analyze the resource score range that meets application requirements, and hides the true resource performance of the resource supply end by generating commitments and proving the commitments, thereby improving the privacy of computing resource allocation.

[0005] In the first aspect, the present disclosure provides a computing resource allocation method based on privacy computing, which is applied to the user side, including: extracting application features from the service application to be allocated, and using a pre-trained application resource model to analyze the resource demand interval corresponding to the application features; sending the resource demand interval to the resource supply side, and receiving the resource commitment feedback from the resource supply side for the resource demand interval; initializing a challenge value according to the resource commitment, sending the challenge value to the resource supply side, and receiving the commitment proof feedback from the resource supply side for the challenge value; verifying the resource commitment according to the challenge value and the commitment proof to obtain a verification result; generating a resource request according to the verification result, sending the resource request to the resource supply side, and receiving the computing node resources feedback from the resource supply side for the resource request.

[0006] In some embodiments, the extracting application characteristics from the service application to be allocated includes: performing load monitoring on the service application to be allocated to obtain application load characteristics; performing demand analysis on the service application to obtain application demand characteristics; performing algorithm performance analysis on the service application to obtain application algorithm characteristics; generating application characteristics based on the application load characteristics, the application demand characteristics and the application algorithm characteristics.

[0007] In some embodiments, the resource commitment is verified based on the challenge value and the commitment proof to obtain a verification result, including: extracting polynomial coefficients and a product polynomial from the commitment proof; extracting a commitment value and a commitment range from the resource commitment; verifying the product polynomial using the challenge value and the polynomial coefficients to obtain a polynomial verification result; verifying the commitment range based on the polynomial verification result and the commitment value to obtain a range verification result; and generating a verification result based on the polynomial verification result and the range verification result.

[0008] The present disclosure provides a computing resource allocation method based on privacy computing, which is applied to the resource supply side, including: benchmarking and weighted scoring computing node resources to obtain resource performance scores; receiving a resource demand interval sent by a user side, and initializing the commitment of the resource performance score according to the resource demand interval to obtain a resource commitment; sending the resource commitment to the user side, and receiving a challenge value fed back by the user side for the resource commitment; proving the challenge value according to the resource commitment to obtain a commitment certificate, and sending the commitment certificate to the user side; receiving a resource request fed back by the user side for the commitment certificate, and allocating the computing node resources to the user side according to the resource request.

[0009] In some embodiments, the benchmarking and weighted scoring of computing node resources to obtain resource performance scores includes: performing performance benchmarking on computing node resources to obtain a performance parameter set; performing performance scoring on the performance parameter set to obtain a primary performance score set; performing data standardization operations on the primary performance score set to obtain a standard performance score set; and performing weighted averaging on the standard performance score set to obtain a resource performance score.

[0010] In some embodiments, the resource performance score is initialized according to the resource demand interval to obtain a resource commitment, including: determining whether the resource performance score is within the resource demand interval; if not, returning to the step of receiving the resource demand interval sent by the user end; if so, initializing a commitment random number, generating a commitment value according to the commitment random number and the resource performance score; generating a commitment range according to the commitment value, and generating a resource commitment according to the commitment value and the commitment range.

[0011] In some embodiments, the proving the challenge value according to the resource commitment to obtain a commitment proof includes: generating a commitment left polynomial and a commitment right polynomial according to the resource commitment; taking the inner product of the commitment left polynomial and the commitment right polynomial as a product polynomial; substituting the challenge value into the product polynomial to obtain a proof challenge polynomial; extracting a base point element from the resource commitment and extracting a polynomial coefficient from the proof challenge polynomial; multiplying the polynomial coefficient by the base point element to obtain a base point coefficient; and generating a commitment proof according to the product polynomial, the polynomial coefficient and the base point coefficient.

[0012] In the second aspect, the present disclosure provides a computing resource allocation system based on privacy computing, which is applied to the user side and includes: a demand analysis module, which is used to extract application features from the service applications to be allocated, and use a pre-trained application resource model to analyze the resource demand interval corresponding to the application features; a demand publishing module, which is used to send the resource demand interval to the resource supply side, and receive the resource commitment fed back by the resource supply side for the resource demand interval; a commitment challenge module, which is used to initialize a challenge value according to the resource commitment, send the challenge value to the resource supply side, and receive the commitment proof fed back by the resource supply side for the challenge value; a commitment verification module, which is used to verify the resource commitment according to the challenge value and the commitment proof to obtain a verification result; a resource request module, which is used to generate a resource request according to the verification result, send the resource request to the resource supply side, and receive the computing node resources fed back by the resource supply side for the resource request.

[0013] The present disclosure provides a computing resource allocation system based on privacy computing, which is applied to the resource supply side, including: a resource scoring module, which is used to benchmark and weightedly score computing node resources to obtain resource performance scores; a commitment creation module, which is used to receive a resource demand interval sent by a user side, and initialize the commitment of the resource performance score according to the resource demand interval to obtain a resource commitment; a commitment sending module, which is used to send the resource commitment to the user side, and receive a challenge value fed back by the user side for the resource commitment; a commitment proof module, which is used to prove the challenge value according to the resource commitment, obtain a commitment proof, and send the commitment proof to the user side; a resource allocation module, which is used to receive a resource request fed back by the user side for the commitment proof, and allocate the computing node resources to the user side according to the resource request.

[0014] In a third aspect, the present disclosure provides a computer device comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a computing resource allocation method based on privacy computing described in the above aspect.

[0015] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a computing resource allocation method based on privacy computing as described in the above aspect.

[0016] In a fifth aspect, the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of a computing resource allocation method based on privacy computing described in the above aspects.

[0017] The present invention provides a computing resource allocation method, system and program based on privacy computing. By extracting application characteristics and analyzing resource demand ranges, it can make rapid judgments on resource requirements based on the characteristics of service applications, thereby improving the efficiency of resource allocation. By proving and verifying the resource commitment based on the challenge value and the commitment proof, it can verify the correctness of the commitment proof and resource commitment provided by the resource supplier, thereby ensuring that the performance score of the computing node resources of the resource supplier is within the resource demand range.

[0018] By receiving the computing node resources fed back by the resource supplier in response to the resource request, computing node resources that meet the needs can be allocated to the service application, while ensuring the performance privacy of the computing nodes on the resource supplier side, thereby improving data security. By performing benchmark tests and weighted scoring, performance testing and numerical representation of computing node resources can be achieved, thereby facilitating meeting user needs and completing docking with users, thereby improving the efficiency of computing resource allocation.

[0019] By performing commitment initialization, the commitment value can be submitted to the user end without leaking the resource performance score, thereby protecting the privacy and security of the computing node resources on the resource supply end. By proving the challenge value according to the resource commitment to obtain a commitment certificate, it is possible to verify whether the resource performance score is within the specified resource demand range without leaking the resource performance score, thereby ensuring the efficiency of the privacy protection and verification process. By allocating the computing node resources to the user end according to the resource request, the computing node resources corresponding to the needs can be allocated with the user end without leaking the specific performance parameters of the computing node resources, thereby improving the privacy and data security when allocating computing node resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0021] Figure 1 A flow chart of the computing resource allocation method based on privacy computing in the first embodiment of the present disclosure when applied to a user terminal is shown.

[0022] Figure 2 A flow chart of the computing resource allocation method based on privacy computing in the first embodiment of the present disclosure when applied to the resource supply side is shown.

[0023] Figure 3 A functional module diagram of the computing resource allocation system based on privacy computing of the second embodiment of the present disclosure when applied to the user end is shown.

[0024] Figure 4 A functional module diagram of the computing resource allocation system based on privacy computing of the second embodiment of the present disclosure when applied to the resource supply end is shown.

[0025] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, and to fully understand and implement how the present disclosure applies technical means to solve technical problems and achieve the corresponding technical effects, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. The embodiments of the present disclosure and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the scope of protection of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0028] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] Embodiment 1

[0030] Figure 1 The following is a flowchart of a computing resource allocation method based on privacy computing provided in an embodiment of the present disclosure. Figure 1 As shown, a computing resource allocation method based on privacy computing is applied to a user end, including:

[0031] S1. Extract application features from the service application to be allocated, and use a pre-trained application resource model to analyze the resource demand interval corresponding to the application features.

[0032] In detail, the user end refers to a user terminal that needs to operate a service application, and the service application refers to a service application program deployed on different computing nodes, which completes various tasks or applications by utilizing computing node resources of each computing node.

[0033] Specifically, the extracting application characteristics from the service application to be allocated includes: performing load monitoring on the service application to be allocated to obtain application load characteristics; performing demand analysis on the service application to obtain application demand characteristics; performing algorithm performance analysis on the service application to obtain application algorithm characteristics; generating application characteristics based on the application load characteristics, the application demand characteristics and the application algorithm characteristics.

[0034] In detail, load detection can be performed through network monitoring tools such as NetFlow or Wireshark, application logs, or monitoring platforms of cloud service providers such as AWS CloudWatch or Azure Monitor. The application load characteristics refer to characteristics related to the load that the service application needs to bear, such as the number of user requests, data transmission volume, and number of concurrent users.

[0035] Specifically, system detection tools such as top, htop or Perf may be used to perform demand analysis, wherein the demand analysis refers to analyzing the demand type of the service application, and the application demand characteristics may be, for example, computationally intensive, storage intensive, and transmission intensive.

[0036] In detail, static code analysis tools or performance analysis tools such as Gprof or Valgrind can be used to perform algorithm performance analysis. The application algorithm characteristics include cyclic complexity, recursive complexity, concurrency complexity and data structure complexity. The application characteristics can be generated according to the application load characteristics, the application demand characteristics and the application algorithm characteristics through a fully connected fusion method.

[0037] Specifically, the pre-trained application resource model is a model trained by a large number of data sets whose input is application features and whose output is resource demand intervals. The application resource model can be a linear regression, polynomial regression, decision tree regression, random forest regression, support vector regression or artificial neural network model.

[0038] In the embodiment of the present invention, by extracting application features and analyzing resource demand intervals, it is possible to quickly determine resource demands based on the features of service applications, thereby improving the efficiency of resource allocation.

[0039] S2. Send the resource demand interval to a resource supply end, and receive a resource commitment fed back by the resource supply end for the resource demand interval.

[0040] In detail, the resource supply end refers to the supply end that provides computing node resources with different performances, and the resource commitment refers to the commitment of the resource supply end that the resource performance scores of the computing node resources it owns meet the resource demand range.

[0041] In an embodiment of the present invention, the resource demand interval can be sent to the resource supply end by using a blockchain, a centralized database, a distributed file system or a message queue system.

[0042] S3. Initialize a challenge value according to the resource commitment, send the challenge value to the resource supplier, and receive a commitment certificate fed back by the resource supplier for the challenge value.

[0043] In an embodiment of the present invention, the challenge value is a unique random number, which can be initialized using a pseudo-random number generator, a true random number generator or a hash function, or can be obtained by performing a hash operation based on relevant data in the resource commitment. The commitment proof is used to prove that the resource performance score meets the resource demand range.

[0044] S4. Verify the resource commitment according to the challenge value and the commitment certificate to obtain a verification result.

[0045] In detail, the verification result includes verification success and verification failure, wherein the verification success means that the performance score of the computing node resources of the resource supply end is within the resource demand range, and the verification failure means that the performance score of the computing node resources of the resource supply end is not within the resource demand range.

[0046] In an embodiment of the present invention, the resource commitment is verified based on the challenge value and the commitment proof to obtain a verification result, including: extracting polynomial coefficients and a product polynomial from the commitment proof; extracting a commitment value and a commitment range from the resource commitment; verifying the product polynomial using the challenge value and the polynomial coefficients to obtain a polynomial verification result; verifying the commitment range based on the polynomial verification result and the commitment value to obtain a range verification result; and generating a verification result based on the polynomial verification result and the range verification result.

[0047] Specifically, the verifying the product polynomial using the challenge value and the polynomial coefficients refers to generating a challenge value verification result by using the challenge value and the polynomial coefficients, and comparing the challenge value verification result with the result of the product polynomial. If the results are consistent, the polynomial verification result is successful.

[0048] In detail, verifying the commitment range according to the polynomial verification result and the commitment value refers to extracting a base point element from the commitment value, and verifying the commitment range according to the correlation between the base point element and the polynomial verification result; if the base point element corresponds to the polynomial verification result, the polynomial verification result is successful.

[0049] Specifically, generating the verification result according to the polynomial verification result and the range verification result means that when both the polynomial verification result and the range verification result are successful, determining that the verification result is a successful verification, otherwise, determining that the verification result is a failed verification.

[0050] In an embodiment of the present invention, by verifying the resource commitment based on the challenge value and the commitment proof, the correctness of the commitment proof and the resource commitment provided by the resource supplier can be verified, thereby ensuring that the performance score of the computing node resources of the resource supplier is within the resource demand range.

[0051] S5. Generate a resource request according to the verification result, send the resource request to the resource supplier, and receive computing node resources fed back by the resource supplier in response to the resource request.

[0052] In detail, generating a resource request according to the verification result refers to generating a resource request when the verification result is successful verification, and the computing node resources are node resources used to configure the service application.

[0053] In an embodiment of the present invention, by receiving the computing node resources fed back by the resource supplier in response to the resource request, computing node resources that meet the needs can be allocated to the service application, while ensuring the performance privacy of the computing nodes on the resource supplier side, thereby improving data security.

[0054] like Figure 2 As shown, a computing resource allocation method based on privacy computing is applied to the resource supply side, including:

[0055] S21. Benchmark and weighted score the computing node resources to obtain resource performance scores.

[0056] In an embodiment of the present invention, the benchmarking and weighted scoring of computing node resources to obtain resource performance scores includes: performing performance benchmarking on computing node resources to obtain a performance parameter set; performing performance scoring on the performance parameter set to obtain a primary performance score set; performing data standardization operations on the primary performance score set to obtain a standard performance score set; and performing weighted averaging on the standard performance score set to obtain a resource performance score.

[0057] In detail, the performance benchmark test can be a system resource occupancy test, an application performance index test, and a database performance test, and the performance parameter set includes parameters such as CPU utilization, memory utilization, network bandwidth utilization, application response time, request rate, error rate, resource usage, database query response time, database query execution plan, database lock and deadlock, number of database connections, and database cache hit rate.

[0058] Specifically, the performance scoring refers to determining a primary performance score corresponding to each performance parameter in the performance parameter set according to a pre-set scoring index.

[0059] In detail, data standardization can be performed using methods such as normalization, extreme value standardization or Z-score standardization, and weighted averaging can be performed using a weighted averaging method, where the sum of the weights of each standard performance score in the standard performance score set is 1.

[0060] In the embodiment of the present invention, by performing benchmark tests and weighted scoring, performance testing and numerical representation of computing node resources can be achieved, thereby conveniently meeting user needs and completing docking with users, thereby improving the efficiency of computing resource allocation.

[0061] S22: Receive a resource requirement interval sent by the user terminal, and initialize the resource performance score according to the resource requirement interval to obtain a resource commitment.

[0062] Specifically, the resource demand interval is a resource performance score interval of computing node resources that can meet the application requirements of the user end, and the resource commitment refers to the commitment of the resource supplier that the resource performance score of the computing node resources it owns meets the resource demand interval.

[0063] In an embodiment of the present invention, the resource performance score is initialized according to the resource demand interval to obtain a resource commitment, including: determining whether the resource performance score is within the resource demand interval; if not, returning to the step of receiving the resource demand interval sent by the user end; if so, initializing a commitment random number, generating a commitment value according to the commitment random number and the resource performance score; generating a commitment range according to the commitment value, and generating a resource commitment according to the commitment value and the commitment range.

[0064] In detail, the commitment random number may be initialized using a pseudo-random number generator, a true random number generator, or a hash function, and the commitment random number is not 0.

[0065] Specifically, generating the commitment value according to the commitment random number and the resource performance score refers to selecting two base point elements in a preset elliptic curve, multiplying the commitment random number and the resource performance score by the two base point elements respectively, and taking the sum of the products as the commitment value.

[0066] In detail, the commitment range can be generated according to the commitment value by using a polynomial proof method, that is, a numerical polynomial is constructed according to the commitment value, and the inner product of the numerical polynomial is used as the commitment range. The resource commitment includes the commitment value and the commitment range.

[0067] In the embodiment of the present invention, by performing commitment initialization, the commitment value can be submitted to the user end without leaking the resource performance score, thereby protecting the privacy and security of the computing node resources of the resource supply end.

[0068] S23: Send the resource commitment to the user terminal, and receive a challenge value fed back by the user terminal in response to the resource commitment.

[0069] S24. Prove the challenge value according to the resource commitment to obtain a commitment certificate, and send the commitment certificate to the user terminal.

[0070] In detail, the commitment certificate is a certificate used to prove that the resource performance score meets the resource requirement range.

[0071] In an embodiment of the present invention, the challenge value is proved according to the resource commitment to obtain a commitment proof, including: generating a commitment left polynomial and a commitment right polynomial according to the resource commitment; taking the inner product of the commitment left polynomial and the commitment right polynomial as a product polynomial; substituting the challenge value into the product polynomial to obtain a proof challenge polynomial; extracting a base point element from the resource commitment and extracting a polynomial coefficient from the proof challenge polynomial; multiplying the polynomial coefficient by the base point element to obtain a base point coefficient; and generating a commitment proof according to the product polynomial, the polynomial coefficient and the base point coefficient.

[0072] Specifically, generating a commitment left polynomial and a commitment right polynomial according to the resource commitment refers to extracting a resource performance score from the resource commitment, converting the resource performance score into binary to obtain score data, and generating a commitment left polynomial and a commitment right polynomial according to the score data.

[0073] In detail, the polynomial coefficients refer to the coefficients of the proof challenge polynomial, and generating the commitment proof based on the product polynomial, the polynomial coefficients and the base point coefficients refers to combining the product polynomial, the polynomial coefficients and the base point coefficients into a commitment proof.

[0074] In an embodiment of the present invention, by proving the challenge value according to the resource commitment to obtain a commitment certificate, it is possible to verify whether the resource performance score is within the specified resource demand range without leaking the resource performance score, thereby ensuring privacy protection and the efficiency of the verification process.

[0075] S25. Receive a resource request fed back by the user terminal in response to the commitment certificate, and allocate the computing node resources to the user terminal according to the resource request.

[0076] In an embodiment of the present invention, allocating the computing node resources to the user terminal according to the resource request means extracting the address port information of the user terminal from the resource request after receiving the resource request, connecting with the user terminal according to the address port information, and thereby allocating the computing node resources to the user terminal.

[0077] In an embodiment of the present invention, by allocating the computing node resources to the user terminal according to the resource request, the computing node resources corresponding to the needs of the user terminal can be allocated without leaking the specific performance parameters of the computing node resources, thereby improving the privacy and data security during the allocation of computing node resources.

[0078] Embodiment 2

[0079] Based on the above embodiments, Figure 3 This is a functional module diagram of a computing resource allocation system based on privacy computing provided by an embodiment of the present disclosure. Figure 3 As shown, a computing resource allocation system based on privacy computing includes:

[0080] The computing resource allocation system 300 based on privacy computing described in this embodiment can be installed in an electronic device. According to the functions implemented, the computing resource allocation system 300 based on privacy computing can include a demand analysis module 301, a demand release module 302, a commitment challenge module 303, a commitment verification module 304 and a resource request module 305. The module described in the present disclosure can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0081] In this embodiment, the functions of each module / unit are as follows:

[0082] The demand analysis module 301 is used to extract application features from the service applications to be allocated, and analyze the resource demand interval corresponding to the application features using a pre-trained application resource model;

[0083] The demand publishing module 302 is used to send the resource demand interval to the resource supply end, and receive the resource commitment fed back by the resource supply end for the resource demand interval;

[0084] The commitment challenge module 303 is used to initialize a challenge value according to the resource commitment, send the challenge value to the resource supplier, and receive a commitment certificate fed back by the resource supplier for the challenge value;

[0085] The commitment verification module 304 is used to verify the resource commitment according to the challenge value and the commitment proof to obtain a verification result;

[0086] The resource request module 305 is used to generate a resource request according to the verification result, send the resource request to the resource supplier, and receive the computing node resources fed back by the resource supplier in response to the resource request.

[0087] In detail, each module described in the computing resource allocation system 300 based on privacy computing described in the embodiment of the present disclosure adopts the same technical means as the computing resource allocation method based on privacy computing described in Example 1 when used, and can produce the same technical effects, which will not be repeated here.

[0088] Based on the above embodiments, Figure 4 This is a functional module diagram of a computing resource allocation system based on privacy computing provided by an embodiment of the present disclosure. Figure 4 As shown, a computing resource allocation system based on privacy computing includes:

[0089] The computing resource allocation system 400 based on privacy computing described in this embodiment can be installed in an electronic device. According to the functions implemented, the computing resource allocation system 400 based on privacy computing can include a demand analysis module 401, a demand release module 402, a commitment challenge module 403, a commitment verification module 404 and a resource request module 405. The module described in the present disclosure can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0090] In this embodiment, the functions of each module / unit are as follows:

[0091] The resource scoring module 401 is used to perform benchmark testing and weighted scoring on computing node resources to obtain resource performance scores;

[0092] The commitment creation module 402 is used to receive the resource requirement interval sent by the user terminal, and initialize the resource performance score according to the resource requirement interval to obtain a resource commitment;

[0093] The commitment sending module 403 is used to send the resource commitment to the user terminal and receive a challenge value fed back by the user terminal in response to the resource commitment;

[0094] The commitment proof module 404 is used to prove the challenge value according to the resource commitment, obtain a commitment proof, and send the commitment proof to the user terminal;

[0095] The resource allocation module 405 is used to receive the resource request fed back by the user terminal in response to the commitment certificate, and allocate the computing node resources to the user terminal according to the resource request.

[0096] In detail, each module described in the computing resource allocation system 400 based on privacy computing described in the embodiment of the present disclosure adopts the same technical means as the computing resource allocation method based on privacy computing described in Example 1 when used, and can produce the same technical effects, which will not be repeated here.

[0097] Embodiment 3

[0098] On the basis of the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0099] In some implementations of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0100] In some implementations of this embodiment, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0101] The processor may include, but is not limited to, one or more processors or microprocessors, etc. Each processor may be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components to execute the methods in the above embodiments.

[0102] The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, and the computer-readable storage medium may include but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD ROM, DVD ROM, Blu-ray disc, etc.).

[0103] The computer-readable storage medium may also store at least one computer executable program, such as a computer-readable instruction. The computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The computer-readable storage medium may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions stored on the computer-readable storage medium, the various methods described above may be performed.

[0104] In addition, the computer device may also include (but not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.), etc.

[0105] The processor may communicate with external devices via an I / O bus via a wired or wireless network.

[0106] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product / computer program product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0107] In the embodiments provided in the present disclosure, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the above-mentioned module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0108] It should be noted that in the present disclosure, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element limited by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0109] Although the embodiments disclosed in the present disclosure are as above, the above contents are only embodiments adopted for facilitating the understanding of the present disclosure and are not intended to limit the present disclosure. Any technician in the technical field to which the present disclosure belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present disclosure, but the scope of patent protection of the present disclosure shall still be subject to the scope defined in the attached claims.

Claims

1. A computing resource allocation method based on privacy computing, characterized in that: Applied to the user side, including: Extracting application features from the service applications to be allocated, and analyzing the resource demand interval corresponding to the application features using a pre-trained application resource model; Sending the resource demand interval to a resource supplier, and receiving a resource commitment fed back by the resource supplier for the resource demand interval; Initializing a challenge value according to the resource commitment, sending the challenge value to the resource supplier, and receiving a commitment certificate fed back by the resource supplier for the challenge value; Verify the resource commitment according to the challenge value and the commitment certificate to obtain a verification result; A resource request is generated according to the verification result, the resource request is sent to the resource supplier, and computing node resources fed back by the resource supplier in response to the resource request are received.

2. The computing resource allocation method based on privacy computing according to claim 1, characterized in that: The step of extracting application features from the service application to be allocated includes: Perform load monitoring on the service application to be allocated to obtain application load characteristics; Performing demand analysis on the service application to obtain application demand characteristics; Performing algorithm performance analysis on the service application to obtain application algorithm features; An application feature is generated according to the application load feature, the application demand feature, and the application algorithm feature.

3. The computing resource allocation method based on privacy computing according to claim 1, characterized in that: The verifying the resource commitment according to the challenge value and the commitment proof to obtain a verification result includes: extracting polynomial coefficients and a product polynomial from the commitment proof; extracting a commitment value and a commitment range from the resource commitment; Verifying the product polynomial using the challenge value and the polynomial coefficients to obtain a polynomial verification result; Verify the commitment range according to the polynomial verification result and the commitment value to obtain a range verification result; A verification result is generated according to the polynomial verification result and the range verification result.

4. A computing resource allocation method based on privacy computing, characterized in that: Applied to resource supply side, including: Benchmark and weighted score the computing node resources to obtain resource performance scores; Receiving a resource requirement interval sent by a user terminal, and initializing a commitment of the resource performance score according to the resource requirement interval to obtain a resource commitment; Sending the resource commitment to the user terminal, and receiving a challenge value fed back by the user terminal in response to the resource commitment; Prove the challenge value according to the resource commitment to obtain a commitment certificate, and send the commitment certificate to the user terminal; Receive a resource request fed back by the user terminal in response to the commitment certificate, and allocate the computing node resources to the user terminal according to the resource request.

5. The computing resource allocation method based on privacy computing according to claim 4 is characterized in that: The benchmarking and weighted scoring of computing node resources to obtain resource performance scores include: Perform performance benchmark tests on computing node resources to obtain a performance parameter set; Performing performance scoring on the performance parameter set to obtain a primary performance score set; Performing a data standardization operation on the primary performance score set to obtain a standard performance score set; The standard performance score set is weighted averaged to obtain a resource performance score.

6. The computing resource allocation method based on privacy computing according to claim 4 is characterized in that: The initializing the resource performance score according to the resource demand interval to obtain a resource commitment includes: Determining whether the resource performance score is within the resource demand range; If not, returning to the step of receiving the resource requirement interval sent by the user terminal; If yes, initialize the commitment random number, and generate the commitment value according to the commitment random number and the resource performance score; A commitment range is generated according to the commitment value, and a resource commitment is generated according to the commitment value and the commitment range.

7. The computing resource allocation method based on privacy computing according to claim 4 is characterized in that: The step of proving the challenge value according to the resource commitment to obtain a commitment certificate includes: Generate a commitment left polynomial and a commitment right polynomial according to the resource commitment; Taking the inner product of the commitment left polynomial and the commitment right polynomial as the product polynomial; Substituting the challenge value into the product polynomial to obtain a proof challenge polynomial; Extracting a base point element from the resource commitment and extracting a polynomial coefficient from the proof challenge polynomial; Multiplying the base point element by the polynomial coefficient to obtain a base point coefficient; A commitment proof is generated based on the product polynomial, the polynomial coefficients, and the base point coefficients.

8. A computing resource allocation system based on privacy computing, characterized in that: Applied to the user side, including: A demand analysis module, used to extract application features from the service applications to be allocated, and analyze the resource demand interval corresponding to the application features using a pre-trained application resource model; A demand publishing module, used to send the resource demand interval to the resource supply end, and receive the resource commitment fed back by the resource supply end for the resource demand interval; A commitment challenge module, configured to initialize a challenge value according to the resource commitment, send the challenge value to the resource supplier, and receive a commitment certificate fed back by the resource supplier for the challenge value; A commitment verification module, used to verify the resource commitment according to the challenge value and the commitment proof, and obtain a verification result; The resource request module is used to generate a resource request according to the verification result, send the resource request to the resource supply end, and receive the computing node resources fed back by the resource supply end in response to the resource request.

9. A computing resource allocation system based on privacy computing, characterized in that: Applied to resource supply side, including: Resource scoring module, used to benchmark and weightedly score computing node resources to obtain resource performance scores; Creating a commitment module, used for receiving the resource requirement interval sent by the user end, initializing the commitment of the resource performance score according to the resource requirement interval, and obtaining a resource commitment; A commitment sending module, used to send the resource commitment to the user terminal, and receive a challenge value fed back by the user terminal for the resource commitment; A commitment proof module, used to prove the challenge value according to the resource commitment, obtain a commitment proof, and send the commitment proof to the user terminal; The resource allocation module is used to receive the resource request fed back by the user terminal in response to the commitment certificate, and allocate the computing node resources to the user terminal according to the resource request.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the computing resource allocation method based on privacy computing described in any one of claims 1 to 7 are implemented.

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