Multi-side cooperative control method and system taking cloud service as center

Abstract

The invention discloses a cloud service-centered multilateral cooperative control method and a cloud service-centered multilateral cooperative control system. The method comprises the following steps of: obtaining to-be-distributed calculation task data and server information and historical task records of a plurality of to-be-controlled edge servers; predicting a cloud service requirement parameter corresponding to each edge server based on a prediction algorithm according to the historical task record; determining a control requirement function according to a cloud service requirement parameter corresponding to each edge server and a calculation requirement corresponding to the calculation task data; and performing iterative calculation based on a dynamic programming algorithm according to the control requirement function and the server information to obtain distribution calculation task data and working parameters of each edge server. Therefore, according to the method, the cloud service requirements of the edge server can be fully combined to coordinate and distribute the multilateral calculation, so that the cooperative control effect is improved, and the service quality of the cloud service is ensured.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a multilateral collaborative control method and system centered on cloud services. Background Art

[0002] With the development of Internet technology and data servers, the data processing technology of "Internet +" has begun to be combined with the data processing needs of various industries to improve the intelligence of various industries. Among them, cloud service is a data center located in the remote cloud and provides dynamic, scalable and often virtualized resources through the Internet in an on-demand processing mode. The massive computing task data and storage resources enable the cloud to provide strong background support for the complex calculations involved in smart energy use. Edge computing is a new computing model after cloud computing. It localizes computing by providing IT and cloud computing capabilities on the wireless access network side, aiming to reduce latency, improve network operation efficiency, improve business distribution capabilities, and optimize terminal experience quality. However, in the prior art, when it comes to the collaborative control of multiple edge servers, generally only the resource requirements of the current task and the computing power of each edge server are considered to allocate computing tasks, and the service quality and service efficiency of the core cloud services corresponding to different edge servers are not fully considered to correct the control strategy. Therefore, its collaborative control effect is not balanced enough, and it is easy to affect the service quality and service efficiency of some core cloud services. It can be seen that the prior art has defects and needs to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a multilateral collaborative control method and system centered on cloud services, which can fully combine the cloud service requirements of edge servers to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a multilateral collaborative control method centered on cloud services, the method comprising: Obtain computing task data to be assigned and server information and historical task records of multiple edge servers to be controlled; According to the historical task records, based on the prediction algorithm, predict the cloud service requirement parameters corresponding to each edge server; Determining a control requirement function according to the cloud service requirement parameters corresponding to each of the edge servers and the computing requirements corresponding to the computing task data; According to the control requirement function and the server information, an iterative calculation is performed based on a dynamic programming algorithm to obtain the distribution calculation task data and working parameters of each edge server.

[0005] As an optional implementation, in the first aspect of the present invention, the server information includes hardware information, software information, communication performance information and computing performance information.

[0006] As an optional implementation, in the first aspect of the present invention, the historical task record includes the execution computing task record and communication record of the edge server in multiple historical time periods.

[0007] As an optional implementation, in the first aspect of the present invention, predicting the cloud service requirement parameters corresponding to each edge server based on the historical task record and a prediction algorithm includes: For each edge server, input the historical task record corresponding to the edge server into the trained cloud service prediction neural network to obtain the cloud service node corresponding to the edge server; Obtaining preset cloud service requirements of the cloud service node; the preset cloud service requirements include preset computing efficiency requirements and preset communication accuracy; Calculate the historical average completion requirement corresponding to the edge server according to all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server; Calculate the average of the preset cloud service requirement and the historical average completion requirement, and calculate the cloud service requirement parameters corresponding to the edge server; the cloud service requirement parameters include cloud service type, cloud service computing efficiency requirement, and cloud service communication accuracy requirement.

[0008] As an optional implementation, in the first aspect of the present invention, the cloud service prediction neural network is a CNN network, which is trained by a training data set including multiple training server task records and corresponding cloud service node annotations.

[0009] As an optional implementation, in the first aspect of the present invention, the calculating the historical average completion requirement corresponding to the edge server according to all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server includes: Filter out all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server to obtain multiple historical associated task records; Calculate the average value of the computing efficiency in all the historical associated task records to obtain the historical average computing efficiency requirement corresponding to the edge server; Calculate the average of the communication accuracy rates in all the historical associated task records to obtain the historical average communication accuracy rate requirement corresponding to the edge server; The historical average computing efficiency requirement and the historical average communication accuracy requirement are determined as the historical average completion requirement corresponding to the edge server.

[0010] As an optional implementation, in the first aspect of the present invention, determining the control requirement function according to the cloud service requirement parameter corresponding to each edge server and the computing requirement corresponding to the computing task data includes: Determine the objective function to minimize the computing task data assigned to each edge server in the collaborative control scheme; Determining the constraint conditions includes that the predicted individual computing effect corresponding to each edge server in the collaborative control scheme satisfies the corresponding cloud service requirement parameters and the predicted parallel computing effects corresponding to the computing task data assigned to all edge servers in the collaborative control scheme meet the computing requirements corresponding to the computing task data; the collaborative control scheme includes the assigned computing task data and working parameters corresponding to each edge server; The objective function and the constraint condition are determined as a control requirement function.

[0011] As an optional implementation, in the first aspect of the present invention, the iterative calculation based on the control requirement function and the server information is performed based on a dynamic programming algorithm to obtain the distribution computing task data and working parameters of each edge server, including: For each of the edge servers, calculating information similarity between historical server information in historical application records of multiple candidate prediction neural networks and server information of the edge server; Determine the candidate prediction neural network with the highest information similarity as the computing effect prediction neural network corresponding to the edge server; the computing effect prediction neural network is trained by a training data set including a plurality of training allocation computing data and corresponding working parameter annotations and computing effect annotations; According to the preset dynamic programming algorithm, the collaborative control schemes corresponding to all the edge servers are iteratively generated, and the distributed computing task data and working parameters in the collaborative control scheme are predicted by the computing effect prediction neural network corresponding to each edge server to obtain the corresponding predicted individual computing effect and predicted parallel computing effect. The collaborative control schemes whose corresponding predicted individual computing effects and predicted parallel computing effects meet the constraints are retained, and iterative calculations are performed until the retained collaborative control schemes meet the objective function, so as to obtain the optimal collaborative control scheme and the distributed computing task data and working parameters of each edge server.

[0012] A second aspect of an embodiment of the present invention discloses a multilateral collaborative control system centered on cloud services, the system comprising: An acquisition module, used to acquire computing task data to be assigned and server information and historical task records of multiple edge servers to be controlled; A prediction module, configured to predict the cloud service requirement parameters corresponding to each edge server based on the historical task records and a prediction algorithm; A determination module, configured to determine a control requirement function according to a cloud service requirement parameter corresponding to each of the edge servers and a computing requirement corresponding to the computing task data; The calculation module is used to perform iterative calculation based on the dynamic programming algorithm according to the control requirement function and the server information to obtain the distribution calculation task data and working parameters of each edge server.

[0013] As an optional implementation, in the second aspect of the present invention, the server information includes hardware information, software information, communication performance information and computing performance information.

[0014] As an optional implementation, in the second aspect of the present invention, the historical task records include execution computing task records and communication records of the edge server in multiple historical time periods.

[0015] As an optional implementation, in the second aspect of the present invention, the prediction module predicts the cloud service requirement parameters corresponding to each edge server based on the historical task record and the prediction algorithm, including: For each edge server, input the historical task record corresponding to the edge server into the trained cloud service prediction neural network to obtain the cloud service node corresponding to the edge server; Obtaining preset cloud service requirements of the cloud service node; the preset cloud service requirements include preset computing efficiency requirements and preset communication accuracy; Calculate the historical average completion requirement corresponding to the edge server according to all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server; Calculate the average of the preset cloud service requirement and the historical average completion requirement, and calculate the cloud service requirement parameters corresponding to the edge server; the cloud service requirement parameters include cloud service type, cloud service computing efficiency requirement, and cloud service communication accuracy requirement.

[0016] As an optional implementation, in the second aspect of the present invention, the cloud service prediction neural network is a CNN network, which is trained by a training data set including multiple training server task records and corresponding cloud service node annotations.

[0017] As an optional implementation, in the second aspect of the present invention, the prediction module calculates the specific manner of the historical average completion requirement corresponding to the edge server according to all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server, including: Filter out all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server to obtain multiple historical associated task records; Calculate the average value of the computing efficiency in all the historical associated task records to obtain the historical average computing efficiency requirement corresponding to the edge server; Calculate the average of the communication accuracy rates in all the historical associated task records to obtain the historical average communication accuracy rate requirement corresponding to the edge server; The historical average computing efficiency requirement and the historical average communication accuracy requirement are determined as the historical average completion requirement corresponding to the edge server.

[0018] As an optional implementation, in the second aspect of the present invention, the determination module determines the specific manner of controlling the requirement function according to the cloud service requirement parameters corresponding to each of the edge servers and the computing requirements corresponding to the computing task data, including: Determine the objective function to minimize the computing task data assigned to each edge server in the collaborative control scheme; Determining the constraint conditions includes that the predicted individual computing effect corresponding to each edge server in the collaborative control scheme satisfies the corresponding cloud service requirement parameters and the predicted parallel computing effects corresponding to the computing task data assigned to all edge servers in the collaborative control scheme meet the computing requirements corresponding to the computing task data; the collaborative control scheme includes the assigned computing task data and working parameters corresponding to each edge server; The objective function and the constraint condition are determined as a control requirement function.

[0019] As an optional implementation, in the second aspect of the present invention, the calculation module performs iterative calculation based on the dynamic programming algorithm according to the control requirement function and the server information to obtain the specific method of allocating computing task data and working parameters of each edge server, including: For each of the edge servers, calculating information similarity between historical server information in historical application records of multiple candidate prediction neural networks and server information of the edge server; Determine the candidate prediction neural network with the highest information similarity as the computing effect prediction neural network corresponding to the edge server; the computing effect prediction neural network is trained by a training data set including a plurality of training allocation computing data and corresponding working parameter annotations and computing effect annotations; According to the preset dynamic programming algorithm, the collaborative control schemes corresponding to all the edge servers are iteratively generated, and the distributed computing task data and working parameters in the collaborative control scheme are predicted by the computing effect prediction neural network corresponding to each edge server to obtain the corresponding predicted individual computing effect and predicted parallel computing effect. The collaborative control schemes whose corresponding predicted individual computing effects and predicted parallel computing effects meet the constraints are retained, and iterative calculations are performed until the retained collaborative control schemes meet the objective function, so as to obtain the optimal collaborative control scheme and the distributed computing task data and working parameters of each edge server.

[0020] A third aspect of the present invention discloses another multilateral collaborative control system centered on cloud services, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the cloud service-centric multilateral collaborative control method disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the cloud service-centric multilateral collaborative control method disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can predict the cloud service requirement parameters corresponding to each edge server based on the historical task records of the edge server, and then determine the control requirement function according to the cloud service requirement parameters and the computing requirements corresponding to the computing task data, so as to obtain the distributed computing task data and working parameters of each edge server through iterative calculation based on the dynamic programming algorithm, so as to fully combine the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a multilateral collaborative control method centered on cloud services disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural diagram of a multilateral collaborative control system centered on cloud services disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural diagram of another multilateral collaborative control system centered on cloud services disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a multilateral collaborative control method and system centered on cloud services, which can predict the cloud service requirement parameters corresponding to each edge server based on the historical task records of the edge server, and then determine the control requirement function according to the cloud service requirement parameters and the computing requirements corresponding to the computing task data, so as to obtain the distributed computing task data and working parameters of each edge server through iterative calculation based on the dynamic programming algorithm, so as to fully combine the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 : is a flowchart of a multilateral collaborative control method centered on cloud services disclosed in an embodiment of the present invention. Figure 1 The described multilateral collaborative control method centered on cloud services can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the cloud service-centric multilateral collaborative control method may include the following operations: 101. Obtain computing task data to be assigned and server information and historical task records of multiple edge servers to be controlled.

[0032] 102. According to the historical task records and based on the prediction algorithm, predict the cloud service requirement parameters corresponding to each edge server. 103. Determine a control requirement function according to the cloud service requirement parameters corresponding to each edge server and the computing requirements corresponding to the computing task data. 104. According to the control requirement function and the server information, an iterative calculation is performed based on a dynamic programming algorithm to obtain the distribution computing task data and working parameters of each edge server.

[0033] It can be seen that the above-mentioned embodiments of the invention can predict the cloud service requirement parameters corresponding to each edge server based on the historical task records of the edge server, and then determine the control requirement function according to the cloud service requirement parameters and the computing requirements corresponding to the computing task data, so as to obtain the allocated computing task data and working parameters of each edge server by iterative calculation based on the dynamic programming algorithm, so as to fully combine the cloud service requirements of the edge server to coordinate the allocation of multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0034] As an optional embodiment, in the above steps, the server information includes hardware information, software information, communication performance information and computing performance information.

[0035] It can be seen that through the above optional embodiments, the content of the server information is limited to comprehensively characterize the characteristics of the server, assisting in fully combining the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0036] As an optional embodiment, in the above steps, the historical task records include the execution computing task records and communication records of the edge server in multiple historical time periods.

[0037] It can be seen that through the above optional embodiments, the content of historical task records is limited to comprehensively characterize the computing records and characteristics of the server in historical time, to accurately predict its cloud service requirement parameters, and to assist in fully combining the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0038] As an optional embodiment, in the above steps, based on the historical task records and the prediction algorithm, predicting the cloud service requirement parameters corresponding to each edge server includes: For each edge server, the historical task records corresponding to the edge server are input into the trained cloud service prediction neural network to obtain the cloud service node corresponding to the edge server; Obtaining preset cloud service requirements of the cloud service node; optionally, the preset cloud service requirements include preset computing efficiency requirements and preset communication accuracy; Calculate the historical average completion requirement corresponding to the edge server based on all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server; Calculate the average of the preset cloud service requirements and the historical average completion requirements, and calculate the cloud service requirement parameters corresponding to the edge server; the cloud service requirement parameters include cloud service type, cloud service computing efficiency requirements, and cloud service communication accuracy requirements.

[0039] It can be seen that through the above optional embodiments, the cloud service node corresponding to the edge server can be predicted through the cloud service prediction neural network, and the cloud service requirement parameters corresponding to the edge server can be accurately determined based on the preset cloud service requirements of the cloud service node and the historical average completion requirements corresponding to the edge server, thereby assisting in fully combining the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0040] As an optional embodiment, in the above steps, the cloud service prediction neural network is a CNN network, which is trained by a training data set including multiple training server task records and corresponding cloud service node annotations.

[0041] It can be seen that through the above optional embodiments, the network details and training details of the cloud service prediction neural network are clarified, which can be used to accurately determine the cloud service node corresponding to the edge server, so as to facilitate subsequent calculations and control, and assist in fully combining the cloud service requirements of the edge server to coordinate the allocation of multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0042] As an optional embodiment, in the above steps, calculating the historical average completion requirement corresponding to the edge server according to all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server includes: Filter out all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server to obtain multiple historical related task records; Calculate the average value of the computing efficiency in all historical associated task records to obtain the historical average computing efficiency requirement corresponding to the edge server; Calculate the average of the communication accuracy rates in all historical associated task records to obtain the historical average communication accuracy rate requirement corresponding to the edge server; The historical average computing efficiency requirement and the historical average communication accuracy requirement are determined as the historical average completion requirement corresponding to the edge server.

[0043] It can be seen that through the above optional embodiments, historical task records related to the computing tasks of the cloud service nodes can be screened out, and the cloud service computing efficiency requirements and cloud service communication accuracy requirements corresponding to the edge server can be determined through average calculation, which helps to fully combine the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0044] As an optional embodiment, in the above steps, determining the control requirement function according to the cloud service requirement parameters corresponding to each edge server and the computing requirements corresponding to the computing task data includes: Determine the objective function to minimize the computing task data assigned to each edge server in the collaborative control scheme; Determining the constraint condition includes that the predicted individual computing effect corresponding to each edge server in the collaborative control scheme satisfies the corresponding cloud service requirement parameter and the predicted parallel computing effect corresponding to the computing task data assigned to all edge servers in the collaborative control scheme satisfies the computing requirement corresponding to the computing task data; optionally, the collaborative control scheme includes the assigned computing task data and working parameters corresponding to each edge server; The objective function and constraints are determined as the control requirement function.

[0045] It can be seen that through the above-mentioned optional embodiments, reasonable objective functions and constraints can be accurately determined, so that subsequent calculations can obtain more reasonable collaborative control solutions, and assist in fully combining the cloud service requirements of the edge server to coordinate the allocation of multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0046] As an optional embodiment, in the above steps, according to the control requirement function and the server information, an iterative calculation is performed based on a dynamic programming algorithm to obtain the distribution computing task data and working parameters of each edge server, including: For each edge server, calculating information similarity between historical server information in historical application records of multiple candidate prediction neural networks and server information of the edge server; Determine the candidate prediction neural network with the highest information similarity as the computing effect prediction neural network corresponding to the edge server; optionally, the computing effect prediction neural network is trained by a training data set including a plurality of training allocation computing data and corresponding working parameter annotations and computing effect annotations; According to the preset dynamic programming algorithm, the collaborative control schemes corresponding to all edge servers are iteratively generated, and the distributed computing task data and working parameters in the collaborative control scheme are predicted through the computing effect prediction neural network corresponding to each edge server to obtain the corresponding predicted individual computing effect and predicted parallel computing effect. The collaborative control schemes whose corresponding predicted individual computing effects and predicted parallel computing effects meet the constraints are retained, and the iterative calculation is performed until the retained collaborative control scheme meets the objective function, so as to obtain the optimal collaborative control scheme and the distributed computing task data and working parameters of each edge server.

[0047] It can be seen that through the above optional embodiments, the computing effect prediction neural network corresponding to the edge server can be screened out according to the server information, and the iterative scheme can be generated and verified in combination with the dynamic programming algorithm until the optimal collaborative control scheme is obtained, so as to fully combine the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0048] Embodiment 2 See also Figure 2 , Figure 2 This is a schematic diagram of a multi-party collaborative control system centered on cloud services disclosed in an embodiment of the present invention. Figure 2 The described multilateral collaborative control system centered on cloud services can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the multilateral collaborative control system centered on cloud services may include: The acquisition module 201 is used to acquire computing task data to be assigned and server information and historical task records of multiple edge servers to be controlled.

[0049] The prediction module 202 is used to predict the cloud service requirement parameters corresponding to each edge server based on the historical task records and the prediction algorithm. The determination module 203 is used to determine the control requirement function according to the cloud service requirement parameters corresponding to each edge server and the computing requirements corresponding to the computing task data. The calculation module 204 is used to perform iterative calculation based on the dynamic programming algorithm according to the control requirement function and the server information to obtain the distribution calculation task data and working parameters of each edge server.

[0050] It can be seen that the above-mentioned embodiments of the invention can predict the cloud service requirement parameters corresponding to each edge server based on the historical task records of the edge server, and then determine the control requirement function according to the cloud service requirement parameters and the computing requirements corresponding to the computing task data, so as to obtain the allocated computing task data and working parameters of each edge server by iterative calculation based on the dynamic programming algorithm, so as to fully combine the cloud service requirements of the edge server to coordinate the allocation of multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0051] As an optional embodiment, the server information includes hardware information, software information, communication performance information and computing performance information.

[0052] It can be seen that through the above optional embodiments, the content of the server information is limited to comprehensively characterize the characteristics of the server, assisting in fully combining the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0053] As an optional embodiment, the historical task records include the execution computing task records and communication records of the edge server in multiple historical time periods.

[0054] It can be seen that through the above optional embodiments, the content of historical task records is limited to comprehensively characterize the computing records and characteristics of the server in historical time, to accurately predict its cloud service requirement parameters, and to assist in fully combining the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0055] As an optional embodiment, the prediction module predicts the specific manner of the cloud service requirement parameters corresponding to each edge server based on the historical task records and the prediction algorithm, including: For each edge server, the historical task records corresponding to the edge server are input into the trained cloud service prediction neural network to obtain the cloud service node corresponding to the edge server; Obtaining preset cloud service requirements of the cloud service node; optionally, the preset cloud service requirements include preset computing efficiency requirements and preset communication accuracy; Calculate the historical average completion requirement corresponding to the edge server based on all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server; Calculate the average of the preset cloud service requirements and the historical average completion requirements, and calculate the cloud service requirement parameters corresponding to the edge server; the cloud service requirement parameters include cloud service type, cloud service computing efficiency requirements, and cloud service communication accuracy requirements.

[0056] It can be seen that through the above optional embodiments, the cloud service node corresponding to the edge server can be predicted through the cloud service prediction neural network, and the cloud service requirement parameters corresponding to the edge server can be accurately determined based on the preset cloud service requirements of the cloud service node and the historical average completion requirements corresponding to the edge server, thereby assisting in fully combining the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0057] As an optional embodiment, the cloud service prediction neural network is a CNN network, which is trained by a training data set including multiple training server task records and corresponding cloud service node annotations.

[0058] It can be seen that through the above optional embodiments, the network details and training details of the cloud service prediction neural network are clarified, which can be used to accurately determine the cloud service node corresponding to the edge server, so as to facilitate subsequent calculations and control, and assist in fully combining the cloud service requirements of the edge server to coordinate the allocation of multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0059] As an optional embodiment, the prediction module calculates the specific manner of the historical average completion requirement corresponding to the edge server according to all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server, including: Filter out all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server to obtain multiple historical related task records; Calculate the average value of the computing efficiency in all historical associated task records to obtain the historical average computing efficiency requirement corresponding to the edge server; Calculate the average of the communication accuracy rates in all historical associated task records to obtain the historical average communication accuracy rate requirement corresponding to the edge server; The historical average computing efficiency requirement and the historical average communication accuracy requirement are determined as the historical average completion requirement corresponding to the edge server.

[0060] It can be seen that through the above optional embodiments, historical task records related to the computing tasks of the cloud service nodes can be screened out, and the cloud service computing efficiency requirements and cloud service communication accuracy requirements corresponding to the edge server can be determined through average calculation, which helps to fully combine the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0061] As an optional embodiment, the determination module determines a specific method of controlling the requirement function according to the cloud service requirement parameters corresponding to each edge server and the computing requirements corresponding to the computing task data, including: Determine the objective function to minimize the computing task data assigned to each edge server in the collaborative control scheme; Determining the constraint condition includes that the predicted individual computing effect corresponding to each edge server in the collaborative control scheme satisfies the corresponding cloud service requirement parameter and the predicted parallel computing effect corresponding to the computing task data assigned to all edge servers in the collaborative control scheme satisfies the computing requirement corresponding to the computing task data; optionally, the collaborative control scheme includes the assigned computing task data and working parameters corresponding to each edge server; The objective function and constraints are determined as the control requirement function.

[0062] It can be seen that through the above-mentioned optional embodiments, reasonable objective functions and constraints can be accurately determined, so that subsequent calculations can obtain more reasonable collaborative control solutions, and assist in fully combining the cloud service requirements of the edge server to coordinate the allocation of multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0063] As an optional embodiment, the calculation module performs iterative calculation based on the dynamic programming algorithm according to the control requirement function and the server information to obtain the specific method of allocating computing task data and working parameters of each edge server, including: For each edge server, calculating information similarity between historical server information in historical application records of multiple candidate prediction neural networks and server information of the edge server; Determine the candidate prediction neural network with the highest information similarity as the computing effect prediction neural network corresponding to the edge server; optionally, the computing effect prediction neural network is trained by a training data set including a plurality of training allocation computing data and corresponding working parameter annotations and computing effect annotations; According to the preset dynamic programming algorithm, the collaborative control schemes corresponding to all edge servers are iteratively generated, and the distributed computing task data and working parameters in the collaborative control scheme are predicted through the computing effect prediction neural network corresponding to each edge server to obtain the corresponding predicted individual computing effect and predicted parallel computing effect. The collaborative control schemes whose corresponding predicted individual computing effects and predicted parallel computing effects meet the constraints are retained, and the iterative calculation is performed until the retained collaborative control scheme meets the objective function, so as to obtain the optimal collaborative control scheme and the distributed computing task data and working parameters of each edge server.

[0064] It can be seen that through the above optional embodiments, the computing effect prediction neural network corresponding to the edge server can be screened out according to the server information, and the iterative scheme can be generated and verified in combination with the dynamic programming algorithm until the optimal collaborative control scheme is obtained, so as to fully combine the cloud service requirements of the edge server to coordinate and allocate multilateral computing, so as to improve the effect of collaborative control and ensure the service quality of cloud services.

[0065] Embodiment 3 See also Figure 3 , Figure 3 It is another multilateral collaborative control system centered on cloud services disclosed in an embodiment of the present invention. Figure 3 The described multilateral collaborative control system centered on cloud services is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the multilateral collaborative control system centered on cloud services may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the cloud service-centric multilateral collaborative control method described in the first embodiment.

[0066] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the cloud service-centric multilateral collaborative control method described in the first embodiment.

[0067] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the cloud service-centric multilateral collaborative control method described in Example 1.

[0068] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0070] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0077] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0079] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0080] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0081] Finally, it should be noted that the cloud service-centric multilateral collaborative control method and system disclosed in the embodiments of the present invention only discloses the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multilateral collaborative control method centered on cloud services, characterized in that: The method comprises: Obtain computing task data to be assigned and server information and historical task records of multiple edge servers to be controlled; According to the historical task records, based on the prediction algorithm, predict the cloud service requirement parameters corresponding to each edge server; Determining a control requirement function according to the cloud service requirement parameters corresponding to each of the edge servers and the computing requirements corresponding to the computing task data; According to the control requirement function and the server information, an iterative calculation is performed based on a dynamic programming algorithm to obtain the distribution calculation task data and working parameters of each edge server.

2. The cloud-based service-centric multilateral collaborative control method according to claim 1, characterized in that: The server information includes hardware information, software information, communication performance information and computing performance information.

3. The cloud-based service-centric multilateral collaborative control method according to claim 1, characterized in that: The historical task records include the execution computing task records and communication records of the edge server in multiple historical time periods.

4. The cloud service-centric multilateral collaborative control method according to claim 3 is characterized in that: The predicting, based on the historical task records and a prediction algorithm, of cloud service requirement parameters corresponding to each edge server includes: For each edge server, input the historical task record corresponding to the edge server into the trained cloud service prediction neural network to obtain the cloud service node corresponding to the edge server; Obtaining preset cloud service requirements of the cloud service node; the preset cloud service requirements include preset computing efficiency requirements and preset communication accuracy; Calculate the historical average completion requirement corresponding to the edge server according to all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server; Calculate the average of the preset cloud service requirement and the historical average completion requirement, and calculate the cloud service requirement parameters corresponding to the edge server; the cloud service requirement parameters include cloud service type, cloud service computing efficiency requirement, and cloud service communication accuracy requirement.

5. The cloud-based service-centric multilateral collaborative control method according to claim 4 is characterized in that: The cloud service prediction neural network is a CNN network, which is trained by a training data set including multiple training server task records and corresponding cloud service node annotations.

6. The cloud service-centric multilateral collaborative control method according to claim 4, characterized in that: The calculating the historical average completion requirement corresponding to the edge server according to all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server includes: Filter out all records of completing the computing tasks corresponding to the cloud service node in the historical task records corresponding to the edge server to obtain multiple historical associated task records; Calculate the average value of the computing efficiency in all the historical associated task records to obtain the historical average computing efficiency requirement corresponding to the edge server; Calculate the average of the communication accuracy rates in all the historical associated task records to obtain the historical average communication accuracy rate requirement corresponding to the edge server; The historical average computing efficiency requirement and the historical average communication accuracy requirement are determined as the historical average completion requirement corresponding to the edge server.

7. The cloud-based service-centric multilateral collaborative control method according to claim 1, characterized in that: The determining of the control requirement function according to the cloud service requirement parameters corresponding to each edge server and the computing requirements corresponding to the computing task data includes: Determine the objective function to minimize the computing task data assigned to each edge server in the collaborative control scheme; Determining the constraint conditions includes that the predicted individual computing effect corresponding to each edge server in the collaborative control scheme satisfies the corresponding cloud service requirement parameters and the predicted parallel computing effects corresponding to the computing task data assigned to all edge servers in the collaborative control scheme meet the computing requirements corresponding to the computing task data; the collaborative control scheme includes the assigned computing task data and working parameters corresponding to each edge server; The objective function and the constraint condition are determined as a control requirement function.

8. The cloud-based service-centric multilateral collaborative control method according to claim 7, characterized in that: The step of performing iterative calculation based on the control requirement function and the server information and based on a dynamic programming algorithm to obtain the distribution computing task data and working parameters of each edge server includes: For each of the edge servers, calculating information similarity between historical server information in historical application records of multiple candidate prediction neural networks and server information of the edge server; Determine the candidate prediction neural network with the highest information similarity as the computing effect prediction neural network corresponding to the edge server; the computing effect prediction neural network is trained by a training data set including a plurality of training allocation computing data and corresponding working parameter annotations and computing effect annotations; According to the preset dynamic programming algorithm, the collaborative control schemes corresponding to all the edge servers are iteratively generated, and the distributed computing task data and working parameters in the collaborative control scheme are predicted by the computing effect prediction neural network corresponding to each edge server to obtain the corresponding predicted individual computing effect and predicted parallel computing effect. The collaborative control schemes whose corresponding predicted individual computing effects and predicted parallel computing effects meet the constraints are retained, and iterative calculations are performed until the retained collaborative control schemes meet the objective function, so as to obtain the optimal collaborative control scheme and the distributed computing task data and working parameters of each edge server.

9. A multilateral collaborative control system centered on cloud services, characterized in that: The system comprises: An acquisition module, used to acquire computing task data to be assigned and server information and historical task records of multiple edge servers to be controlled; A prediction module, configured to predict the cloud service requirement parameters corresponding to each edge server based on the historical task records and a prediction algorithm; A determination module, configured to determine a control requirement function according to a cloud service requirement parameter corresponding to each of the edge servers and a computing requirement corresponding to the computing task data; The calculation module is used to perform iterative calculation based on the dynamic programming algorithm according to the control requirement function and the server information to obtain the distribution calculation task data and working parameters of each edge server.

10. A multilateral collaborative control system centered on cloud services, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the cloud service-centric multilateral collaborative control method as described in any one of claims 1-8.

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