CN117527535A - Cloud edge collaborative fusion deposit management method and system - Google Patents
Cloud edge collaborative fusion deposit management method and system Download PDFInfo
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Abstract
The invention is applicable to the technical field of computers, and provides a cloud edge collaborative fusion deposit calculation management method and a cloud edge collaborative fusion deposit calculation management system, wherein the method comprises the following steps: receiving and processing a task request of a cloud, and analyzing and distributing the task; storing data on the cloud end and the edge equipment, and dynamically distributing storage resources according to the requirements of the cloud end and the edge equipment; executing computing tasks of the cloud end and the edge equipment, and dynamically distributing computing resources according to computing requirements of the cloud end and the edge equipment and an edge computing resource management algorithm; establishing an edge auxiliary decision database to enable the cloud end and the edge equipment to cooperatively work; dynamic resource allocation and scheduling: the system can distribute and schedule tasks according to the priority and the emergency degree of the taking over work. According to the information such as the running state of the fault equipment, the method selects proper standby equipment to take over. And the dynamic allocation and scheduling of resources are performed according to the condition of the take-over work, so that the efficiency and accuracy of the take-over work are improved.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a cloud edge collaborative fusion deposit calculation management method and system.
Background
Centralized storage is a data storage architecture in which all data is stored in a single central location on a network, such as a disk array or network attached storage device. The storage mode can provide an efficient data management and fault tolerance mechanism and can easily realize data sharing and backup. However, it also has some drawbacks such as single point failure, performance bottlenecks, and difficulty in expansion. Accordingly, many businesses now have begun to employ distributed storage systems, making data storage more flexible and scalable.
With the rapid development of technologies such as big data, cloud computing and artificial intelligence, the data volume is explosively increased, and the demand for computing resources is also increasing. Conventional centralized storage and computing modes have failed to meet these needs, and therefore, it has become important to develop an efficient, scalable storage system. However, the existing storage and calculation system often has the problems of low resource utilization rate, high energy consumption, poor expansibility and the like. Therefore, a new type of memory system is urgently needed to solve these problems.
Disclosure of Invention
The invention aims to provide a cloud edge collaborative fusion deposit management method, which aims to solve the technical problems in the prior art determined in the background art.
The invention discloses a cloud edge collaborative fusion deposit calculation management method, which comprises the following steps:
receiving and processing a task request of a cloud, analyzing and distributing the task, receiving data and the task on the edge equipment, and processing the received task;
storing data on the cloud end and the edge equipment, dynamically distributing storage resources according to the requirements of the cloud end and the edge equipment, and managing the storage resources;
executing computing tasks of the cloud end and the edge equipment, dynamically distributing computing resources according to computing requirements of the cloud end and the edge equipment and an edge computing resource management algorithm, and managing the computing resources;
establishing an edge auxiliary decision database to enable the cloud end and the edge equipment to cooperatively work;
when the cloud or edge equipment fails, the automatic calling equipment takes over the work of the failed equipment.
As a further scheme of the present invention, the storing the data on the cloud end and the edge device dynamically allocates the storage resource according to the requirements of the cloud end and the edge device, and manages the storage resource, which specifically includes:
the storage resources in the management system comprise cloud storage resources and edge equipment storage resources, the use condition of the storage resources is monitored, and dynamic allocation and release are carried out according to requirements;
adding access rights to the storage resources, acquiring rights and roles of a visitor when an access application is received, and limiting the content accessible to the visitor according to the access rights;
monitoring stored parameters in real time, analyzing performance and state, setting an abnormal state threshold interval, and sending out abnormal warning information when the parameters are not in the abnormal state threshold.
As a further scheme of the present invention, the executing the computing tasks of the cloud end and the edge device dynamically allocates computing resources according to computing requirements of the cloud end and the edge device and an edge computing resource management algorithm, and manages the computing resources, which specifically includes:
the storage resources in the management system comprise cloud storage resources and edge equipment storage resources, the use condition of the storage resources is monitored, and dynamic allocation and release are carried out according to requirements;
adding access rights to the storage resources, acquiring rights and roles of a visitor when an access application is received, and limiting the content accessible to the visitor according to the access rights;
monitoring stored parameters in real time, analyzing performance and state, setting an abnormal state threshold interval, and sending out abnormal warning information when the parameters are not in the abnormal state threshold.
As a further scheme of the invention, the establishment of the edge auxiliary decision database enables the cloud end and the edge equipment to cooperatively work, and the method specifically comprises the following steps:
establishing an edge auxiliary decision database, and storing historical abnormal information and processing results;
according to the dynamic allocation calculation result, automatically acquiring abnormal information and processing results of similar results in the edge auxiliary decision database, and feeding back to the user;
and maintaining the consistency and real-time performance of data between the cloud and the edge equipment through a data transmission and synchronization mechanism.
Another object of the present invention is to provide a cloud edge collaborative fusion deposit management system, the system includes:
the task processing module is used for receiving and processing the task request of the cloud, analyzing and distributing the task, receiving data and the task on the edge equipment, and processing the received task;
the super fusion storage module is used for storing data on the cloud and the edge equipment, dynamically distributing storage resources according to the requirements of the cloud and the edge equipment and managing the storage resources;
the super fusion computing module is used for executing computing tasks of the cloud end and the edge equipment, dynamically distributing computing resources according to computing requirements of the cloud end and the edge equipment and an edge computing resource management algorithm, and managing the computing resources;
the cloud edge cooperative module is used for establishing an edge auxiliary decision database so as to perform cooperative work between the cloud end and edge equipment;
and the system operation and maintenance module is used for automatically calling equipment to take over the work of the fault equipment when the cloud or the edge equipment fails.
As a further aspect of the present invention, the super fusion storage module includes:
the storage resource management unit is used for managing storage resources in the system, including cloud storage resources and edge equipment storage resources, monitoring the use condition of the storage resources, and dynamically distributing and releasing the storage resources according to requirements;
the data access unit is used for adding access rights to the storage resources, acquiring rights and roles of the accessor when receiving the access application, and limiting the content accessible to the accessor according to the access rights;
the storage monitoring unit is used for monitoring the stored parameters in real time, analyzing the performance and the state, setting an abnormal state threshold interval, and sending out abnormal warning information when the parameters are not in the abnormal state threshold.
As a further aspect of the present invention, the super fusion calculation module includes:
the computing resource management unit is used for managing computing resources in the system, including cloud computing resources and computing resources of edge equipment, monitoring the service condition of the computing resources, and dynamically distributing and releasing the computing resources according to requirements;
and the performance monitoring unit is used for monitoring the performance and the state of the computing system in real time, and comprises a computing node load and response time.
8. The system according to claim 7, wherein said dynamic allocation specifically comprises:
defining a priority A, a weight Q and response time S of a task, and marking a priority sequence number for the task according to the priority;
the dynamic priority Y of the task is calculated, and the calculation formula is as follows:
and sequencing all the tasks according to the numerical value of the dynamic priority, updating the dynamic priority of all the tasks in real time, and sequencing the tasks again.
As a further aspect of the present invention, the cloud edge coordination module includes:
the module establishing unit is used for establishing an edge auxiliary decision database and storing historical abnormal information and processing results;
the auxiliary decision unit is used for automatically acquiring abnormal information and processing results of similar results in the edge auxiliary decision database according to the dynamic allocation calculation result and feeding back the abnormal information and the processing results to the user;
the data synchronization unit is used for maintaining the consistency and instantaneity of data between the cloud and the edge equipment through a data transmission and synchronization mechanism.
The beneficial effects of the invention are as follows:
automatic fault take-over: the system can automatically detect the fault of cloud or edge equipment and automatically call standby equipment to take over the work of the fault equipment. The automatic fault take-over function can greatly reduce the downtime of the system and improve the availability and reliability of the system;
real-time monitoring and management: the system can monitor fault states of the cloud end and the edge equipment in real time, and monitor and manage the operation of the butt joint pipe. Through real-time monitoring and management, faults can be found out in time and processed, and normal operation of the system is ensured;
dynamic resource allocation and scheduling: the system can distribute and schedule tasks according to the priority and the emergency degree of the taking over work. According to the information of the type, the position, the running state and the like of the fault equipment, proper standby equipment is selected for taking over. Meanwhile, the dynamic allocation and scheduling of resources are carried out according to the condition of the take-over work, so that the efficiency and accuracy of the take-over work are improved;
efficient cloud edge cooperative work: the system can perform cooperative work among the modules, and high-efficiency operation of the whole cloud-edge cooperative system is realized.
Drawings
FIG. 1 is a flowchart of a cloud edge collaborative fusion memory management method provided by an embodiment of the present invention;
FIG. 2 is a flowchart of dynamically allocating storage resources according to the requirements of a cloud and an edge device to manage the storage resources according to the data on the cloud and the edge device provided by the embodiment of the invention;
FIG. 3 is a flowchart of a method for dynamically allocating computing resources and managing the computing resources according to computing requirements of cloud and edge devices and an edge computing resource management algorithm, which are provided by an embodiment of the present invention, for executing computing tasks of the cloud and edge devices;
fig. 4 is a flowchart for establishing an edge auxiliary decision database to enable a cloud end and edge devices to cooperate with each other according to an embodiment of the present invention;
FIG. 5 is a block diagram of a cloud-edge collaborative fusion memory management system according to an embodiment of the present invention;
FIG. 6 is a block diagram of a super-fusion memory module according to an embodiment of the present invention;
FIG. 7 is a block diagram of a super fusion computing module according to an embodiment of the present invention;
fig. 8 is a structural block diagram of a cloud edge collaboration module provided by an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
Fig. 1 is a flowchart of a cloud edge collaborative fusion deposit calculation management method provided by an embodiment of the present invention, as shown in fig. 1, and the method includes:
s100, receiving and processing a task request of a cloud, analyzing and distributing the task, receiving data and the task on the edge equipment, and processing the received task;
in this step, the cloud end may send the task request to the cloud edge collaboration module, where the module receives the task request through the network interface.
After receiving the task request, the task is analyzed, and the analysis task request comprises information such as the type, the priority, the resource requirement and the like of the analysis task. By analyzing the task request, detailed information of the task can be obtained, and preparation is made for subsequent task distribution and cooperation.
After the task request is analyzed, the task is distributed to a proper cloud or edge device for processing according to the characteristics and the requirements of the task. Task distribution can make decisions based on factors such as priority of tasks, resource requirements, equipment status, etc., to maximize performance and efficiency of the system.
After distributing the task, the execution condition of the task is monitored, and the task is processed. The task processing comprises the operations of task execution, progress monitoring, result collection and the like. Meanwhile, the execution condition of the task is fed back to the cloud end, so that the cloud end can know the progress of the task in time.
S200, storing data on the cloud end and the edge equipment, dynamically distributing storage resources according to the requirements of the cloud end and the edge equipment, and managing the storage resources;
in the step, by monitoring the idle condition and the utilization rate of the storage resources in real time, the storage resources can be reasonably distributed according to the load condition of the system and the availability of the storage resources so as to meet the storage requirements of tasks; through the management of the access authority of the storage resource, the security and privacy of the storage data can be ensured, and unauthorized access and operation are prevented; stored parameters such as storage speed, delay, etc. are also monitored in real time and stored performance and status are analyzed. And meanwhile, an abnormal state threshold interval is set, and when the stored parameter is not in the abnormal state threshold, abnormal warning information is sent out so as to discover and process the storage fault in time.
S300, executing computing tasks of the cloud end and the edge equipment, dynamically distributing computing resources according to computing requirements of the cloud end and the edge equipment and an edge computing resource management algorithm, and managing the computing resources;
in this step, a task distribution request from S100 is received, and task scheduling is performed according to the priority, weight, and response time of the task. The method marks the priority sequence numbers for the tasks according to the priorities of the tasks, and sorts all the tasks according to the numerical values of the dynamic priorities. By updating the dynamic priorities of tasks in real time, the tasks can be reordered in this step to ensure that high priority tasks can be processed in time.
And the use condition of the computing resources on the cloud and the edge equipment is monitored, and the computing resources are dynamically allocated and released according to the computing requirements of the tasks and the load condition of the system. The method can monitor the idle condition and the utilization rate of the computing resources in real time, and reasonably allocate the computing resources according to the load condition of the system and the availability of the computing resources so as to meet the computing requirements of tasks. By dynamically allocating computing resources, the S300 subunit can improve the computing efficiency and response speed of the system.
And also monitors calculated parameters such as calculation speed, delay, etc. in real time and analyzes the calculated performance and status. An abnormal state threshold interval is set, and when the calculated parameter is not in the abnormal state threshold, abnormal warning information is sent out so as to discover and process the calculation fault in time. By monitoring the performance and state of the computation, the allocation of the computing resources can be adjusted in time to improve the stability and reliability of the system.
S400, establishing an edge auxiliary decision database to enable the cloud end and the edge equipment to cooperatively work;
in this step, an edge-aided decision-making database is created for storing historical anomaly information and processing results. The database may include information of fault type, fault occurrence time, fault handling process, and handling result of the fault device. By establishing an edge auxiliary decision database, the step can provide the inquiry and analysis functions of historical abnormal information and processing results and provide references for the taking over work of the fault equipment.
And according to the result of the dynamic allocation calculation, automatically acquiring the abnormal information and the processing result similar to the current fault equipment in the edge auxiliary decision database. According to the fault type, fault occurrence time, fault processing process and other parameters of the fault equipment, similar abnormal information and processing results are retrieved from the database and fed back to the user. By acquiring the abnormal information and the processing result of the similar result, the reference and the guidance of the fault equipment for taking over the work can be provided, and the efficiency and the accuracy of taking over the work are improved.
And the consistency and instantaneity of data between the cloud and the edge equipment are maintained through a data transmission and synchronization mechanism. The method can monitor the data change on the cloud and the edge equipment and synchronize the data into an edge auxiliary decision database in time. Meanwhile, the delay and the reliability of data transmission can be monitored, and the timely transmission and the integrity of the data are ensured. Accurate data support and decision reference can be provided through a data transmission and synchronization mechanism, and efficient operation of the cloud edge cooperative system is realized.
S500, when the cloud or edge equipment fails, the equipment is automatically called to take over the work of the failed equipment.
In the step, fault states of the cloud end and the edge equipment are continuously monitored, faults are timely found and processed. The cloud and edge equipment state information, including equipment running state, network connection state, hardware faults and the like, can be obtained in real time through network connection, equipment monitoring and other modes. By monitoring the fault states of the cloud and the edge equipment, faults can be found out in time and processed, and normal operation of the system is ensured.
When the cloud or edge equipment fails, the equipment is automatically called to take over the work. According to the information of the type, the position, the running state and the like of the fault equipment, proper standby equipment is selected for taking over. Meanwhile, the task allocation and scheduling can be carried out according to the priority and the emergency degree of the equipment take-over work, so that the timely completion of the take-over work is ensured. And the equipment take-over work can be monitored and managed, so that the smooth proceeding of the take-over work is ensured. The method can monitor the running state of the standby equipment, the progress of taking over the work, the result and other information, and timely feed back the information to the user. Meanwhile, the method can dynamically allocate and schedule resources according to the condition of the take-over work so as to improve the efficiency and accuracy of the take-over work.
Fig. 2 is a flowchart of managing storage resources by dynamically allocating storage resources according to requirements of a cloud end and an edge device according to data on the storage cloud end and the edge device provided in an embodiment of the present invention, as shown in fig. 2, where the storing of data on the cloud end and the edge device dynamically allocates storage resources according to requirements of the cloud end and the edge device, and managing storage resources specifically includes:
s210, managing storage resources in the system, including cloud storage resources and edge equipment storage resources, monitoring the use condition of the storage resources, and dynamically distributing and releasing according to requirements;
s220, adding access rights to the storage resources, acquiring rights and roles of a visitor when an access application is received, and limiting the content accessible to the visitor according to the access rights;
s230, monitoring stored parameters in real time, analyzing performance and states, setting an abnormal state threshold interval, and sending out abnormal warning information when the parameters are not in the abnormal state threshold.
Fig. 3 is a flowchart of dynamically allocating computing resources according to computing requirements of cloud and edge devices and an edge computing resource management algorithm to manage the computing resources, where, as shown in fig. 3, the executing computing tasks of the cloud and edge devices dynamically allocates computing resources according to computing requirements of the cloud and edge devices and the edge computing resource management algorithm to manage the computing resources, and specifically includes:
s310, managing storage resources in the system, including cloud storage resources and edge equipment storage resources, monitoring the use condition of the storage resources, and dynamically distributing and releasing according to requirements;
s320, adding access rights to the storage resources, acquiring rights and roles of a visitor when an access application is received, and limiting accessible contents of the visitor according to the access rights;
s330, monitoring stored parameters in real time, analyzing performance and state, setting an abnormal state threshold interval, and sending out abnormal warning information when the parameters are not in the abnormal state threshold.
Fig. 4 is a flowchart for establishing an edge auxiliary decision database to enable a cloud end and an edge device to perform collaborative work, as shown in fig. 4, where the establishing an edge auxiliary decision database to enable a cloud end and an edge device to perform collaborative work specifically includes:
s410, an edge auxiliary decision database is established, and historical abnormal information and processing results are stored;
s420, according to the dynamic allocation calculation result, automatically acquiring abnormal information and processing results of similar results in the edge auxiliary decision database, and feeding back to a user;
s430, maintaining data consistency and instantaneity between the cloud and the edge equipment through a data transmission and synchronization mechanism.
Fig. 5 is a structural block diagram of a cloud-edge collaborative fusion deposit management system provided by an embodiment of the present invention, as shown in fig. 5, a cloud-edge collaborative fusion deposit management system, where the system includes:
the task processing module 100 is configured to receive and process a task request of the cloud, parse and distribute the task, receive data and the task on the edge device, and process the received task;
in the module, the cloud end can send the task request to the cloud edge cooperative module, and the module receives the task request through the network interface.
After receiving the task request, the task is analyzed, and the analysis task request comprises information such as the type, the priority, the resource requirement and the like of the analysis task. By analyzing the task request, detailed information of the task can be obtained, and preparation is made for subsequent task distribution and cooperation.
After the task request is analyzed, the task is distributed to a proper cloud or edge device for processing according to the characteristics and the requirements of the task. Task distribution can make decisions based on factors such as priority of tasks, resource requirements, equipment status, etc., to maximize performance and efficiency of the system.
After distributing the task, the execution condition of the task is monitored, and the task is processed. The task processing comprises the operations of task execution, progress monitoring, result collection and the like. Meanwhile, the execution condition of the task is fed back to the cloud end, so that the cloud end can know the progress of the task in time.
The super-fusion storage module 200 is used for storing data on the cloud and the edge devices, dynamically distributing storage resources according to the requirements of the cloud and the edge devices, and managing the storage resources, wherein the super-fusion refers to integrating traditional storage and computing devices into a single system which is easy to manage, so that the efficiency and the flexibility of a data center are improved;
in the module, by monitoring the idle condition and the utilization rate of the storage resources in real time, the storage resources can be reasonably distributed according to the load condition of the system and the availability of the storage resources so as to meet the storage requirements of tasks; through the management of the access authority of the storage resource, the security and privacy of the storage data can be ensured, and unauthorized access and operation are prevented; stored parameters such as storage speed, delay, etc. are also monitored in real time and stored performance and status are analyzed. And meanwhile, an abnormal state threshold interval is set, and when the stored parameter is not in the abnormal state threshold, abnormal warning information is sent out so as to discover and process the storage fault in time.
The super-fusion computing module 300 is configured to execute computing tasks of the cloud and the edge devices, dynamically allocate computing resources according to computing requirements of the cloud and the edge devices and an edge computing resource management algorithm, and manage the computing resources, where the super-fusion is to comprehensively fuse computing, storing, network and other resources, and realize high integration and automatic management in a software-defined manner;
in the module, a task distribution request is received, and task scheduling is performed according to the priority, weight and response time of the task. The method marks the priority sequence numbers for the tasks according to the priorities of the tasks, and sorts all the tasks according to the numerical values of the dynamic priorities. By updating the dynamic priorities of tasks in real time, the tasks can be reordered in this step to ensure that high priority tasks can be processed in time.
And the use condition of the computing resources on the cloud and the edge equipment is monitored, and the computing resources are dynamically allocated and released according to the computing requirements of the tasks and the load condition of the system. The method can monitor the idle condition and the utilization rate of the computing resources in real time, and reasonably allocate the computing resources according to the load condition of the system and the availability of the computing resources so as to meet the computing requirements of tasks. By dynamically allocating computing resources, the S300 subunit can improve the computing efficiency and response speed of the system.
And also monitors calculated parameters such as calculation speed, delay, etc. in real time and analyzes the calculated performance and status. An abnormal state threshold interval is set, and when the calculated parameter is not in the abnormal state threshold, abnormal warning information is sent out so as to discover and process the calculation fault in time. By monitoring the performance and state of the computation, the allocation of the computing resources can be adjusted in time to improve the stability and reliability of the system.
The cloud edge cooperative module 400 is configured to establish an edge auxiliary decision database, so that cooperative work is performed between the cloud end and the edge device;
in the module, an edge auxiliary decision database is established for storing historical abnormal information and processing results. The database may include information of fault type, fault occurrence time, fault handling process, and handling result of the fault device. By establishing an edge auxiliary decision database, the step can provide the inquiry and analysis functions of historical abnormal information and processing results and provide references for the taking over work of the fault equipment.
And according to the result of the dynamic allocation calculation, automatically acquiring the abnormal information and the processing result similar to the current fault equipment in the edge auxiliary decision database. According to the fault type, fault occurrence time, fault processing process and other parameters of the fault equipment, similar abnormal information and processing results are retrieved from the database and fed back to the user. By acquiring the abnormal information and the processing result of the similar result, the reference and the guidance of the fault equipment for taking over the work can be provided, and the efficiency and the accuracy of taking over the work are improved.
And the consistency and instantaneity of data between the cloud and the edge equipment are maintained through a data transmission and synchronization mechanism. The method can monitor the data change on the cloud and the edge equipment and synchronize the data into an edge auxiliary decision database in time. Meanwhile, the delay and the reliability of data transmission can be monitored, and the timely transmission and the integrity of the data are ensured. Accurate data support and decision reference can be provided through a data transmission and synchronization mechanism, and efficient operation of the cloud edge cooperative system is realized.
The system operation and maintenance module 500 is configured to automatically invoke a device to take over the operation of the failed device when the cloud or the edge device fails.
In the module, fault states of the cloud end and the edge equipment are continuously monitored, faults are timely found and processed. The cloud and edge equipment state information, including equipment running state, network connection state, hardware faults and the like, can be obtained in real time through network connection, equipment monitoring and other modes. By monitoring the fault states of the cloud and the edge equipment, faults can be found out in time and processed, and normal operation of the system is ensured.
When the cloud or edge equipment fails, the equipment is automatically called to take over the work. According to the information of the type, the position, the running state and the like of the fault equipment, proper standby equipment is selected for taking over. Meanwhile, the task allocation and scheduling can be carried out according to the priority and the emergency degree of the equipment take-over work, so that the timely completion of the take-over work is ensured. And the equipment take-over work can be monitored and managed, so that the smooth proceeding of the take-over work is ensured. The method can monitor the running state of the standby equipment, the progress of taking over the work, the result and other information, and timely feed back the information to the user. Meanwhile, the method can dynamically allocate and schedule resources according to the condition of the take-over work so as to improve the efficiency and accuracy of the take-over work.
Fig. 6 is a block diagram of a super-fusion storage module according to an embodiment of the present invention, as shown in fig. 6, where the super-fusion storage module includes:
the storage resource management unit 210 is configured to manage storage resources in the system, including cloud storage resources and edge device storage resources, monitor usage conditions of the storage resources, and dynamically allocate and release the storage resources according to requirements;
the data access unit 220 is configured to add access rights to a storage resource, obtain rights and roles of a visitor when receiving an access application, and limit content accessible to the visitor according to the access rights;
the storage monitoring unit 230 is configured to monitor the stored parameters in real time, analyze the performance and the state, and set an abnormal state threshold interval, and send out abnormal warning information when the parameters are not in the abnormal state threshold.
Fig. 7 is a block diagram of a super fusion computing module according to an embodiment of the present invention, and as shown in fig. 7, the super fusion computing module includes:
the computing resource management unit 310 is configured to manage computing resources in the system, including cloud computing resources and computing resources of the edge device, monitor usage conditions of the computing resources, and dynamically allocate and release the computing resources according to requirements;
the performance monitoring unit 320 is configured to monitor performance and status of the computing system in real time, including computing node load and response time.
In the embodiment of the present invention, the dynamic allocation specifically includes:
defining a priority A, a weight Q and response time S of a task, and marking a priority sequence number for the task according to the priority;
the dynamic priority Y of the task is calculated, and the calculation formula is as follows:
and sequencing all the tasks according to the numerical value of the dynamic priority, updating the dynamic priority of all the tasks in real time, and sequencing the tasks again.
Fig. 8 is a structural block diagram of a cloud edge collaboration module provided by an embodiment of the present invention, as shown in fig. 8, where the cloud edge collaboration module includes:
a module establishing unit 410, configured to establish an edge auxiliary decision database, and store historical anomaly information and processing results;
the auxiliary decision unit 420 is configured to automatically obtain, according to the dynamic allocation calculation result, abnormal information and a processing result of a similar result in the edge auxiliary decision database, and feed back the abnormal information and the processing result to the user;
the data synchronization unit 430 is configured to maintain consistency and real-time of data between the cloud end and the edge device through a data transmission and synchronization mechanism.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (9)
1. The cloud edge collaborative fusion deposit management method is characterized by comprising the following steps of:
receiving and processing a task request of a cloud, analyzing and distributing the task, receiving data and the task on the edge equipment, and processing the received task;
storing data on the cloud end and the edge equipment, dynamically distributing storage resources according to the requirements of the cloud end and the edge equipment, and managing the storage resources;
executing computing tasks of the cloud end and the edge equipment, dynamically distributing computing resources according to computing requirements of the cloud end and the edge equipment and an edge computing resource management algorithm, and managing the computing resources;
establishing an edge auxiliary decision database to enable the cloud end and the edge equipment to cooperatively work;
when the cloud or edge equipment fails, the automatic calling equipment takes over the work of the failed equipment.
2. The method of claim 1, wherein the storing the data on the cloud and the edge device dynamically allocates the storage resources according to the requirements of the cloud and the edge device, and manages the storage resources, specifically comprising:
the storage resources in the management system comprise cloud storage resources and edge equipment storage resources, the use condition of the storage resources is monitored, and dynamic allocation and release are carried out according to requirements;
adding access rights to the storage resources, acquiring rights and roles of a visitor when an access application is received, and limiting the content accessible to the visitor according to the access rights;
monitoring stored parameters in real time, analyzing performance and state, setting an abnormal state threshold interval, and sending out abnormal warning information when the parameters are not in the abnormal state threshold.
3. The method according to claim 2, wherein the executing the computing tasks of the cloud and the edge devices dynamically allocates computing resources according to computing requirements of the cloud and the edge devices and an edge computing resource management algorithm, and manages the computing resources, and specifically comprises:
the storage resources in the management system comprise cloud storage resources and edge equipment storage resources, the use condition of the storage resources is monitored, and dynamic allocation and release are carried out according to requirements;
adding access rights to the storage resources, acquiring rights and roles of a visitor when an access application is received, and limiting the content accessible to the visitor according to the access rights;
monitoring stored parameters in real time, analyzing performance and state, setting an abnormal state threshold interval, and sending out abnormal warning information when the parameters are not in the abnormal state threshold.
4. A method according to claim 3, wherein the establishing an edge-aided decision-making database enables cooperative work between the cloud and the edge device, specifically comprises:
establishing an edge auxiliary decision database, and storing historical abnormal information and processing results;
according to the dynamic allocation calculation result, automatically acquiring abnormal information and processing results of similar results in the edge auxiliary decision database, and feeding back to the user;
and maintaining the consistency and real-time performance of data between the cloud and the edge equipment through a data transmission and synchronization mechanism.
5. The cloud edge collaborative fusion deposit management system is characterized by comprising:
the task processing module is used for receiving and processing the task request of the cloud, analyzing and distributing the task, receiving data and the task on the edge equipment, and processing the received task;
the super fusion storage module is used for storing data on the cloud and the edge equipment, dynamically distributing storage resources according to the requirements of the cloud and the edge equipment and managing the storage resources;
the super fusion computing module is used for executing computing tasks of the cloud end and the edge equipment, dynamically distributing computing resources according to computing requirements of the cloud end and the edge equipment and an edge computing resource management algorithm, and managing the computing resources;
the cloud edge cooperative module is used for establishing an edge auxiliary decision database so as to perform cooperative work between the cloud end and edge equipment;
and the system operation and maintenance module is used for automatically calling equipment to take over the work of the fault equipment when the cloud or the edge equipment fails.
6. The system of claim 5, wherein the super-fusion storage module comprises:
the storage resource management unit is used for managing storage resources in the system, including cloud storage resources and edge equipment storage resources, monitoring the use condition of the storage resources, and dynamically distributing and releasing the storage resources according to requirements;
the data access unit is used for adding access rights to the storage resources, acquiring rights and roles of the accessor when receiving the access application, and limiting the content accessible to the accessor according to the access rights;
the storage monitoring unit is used for monitoring the stored parameters in real time, analyzing the performance and the state, setting an abnormal state threshold interval, and sending out abnormal warning information when the parameters are not in the abnormal state threshold.
7. The system of claim 6, wherein the super-fusion calculation module comprises:
the computing resource management unit is used for managing computing resources in the system, including cloud computing resources and computing resources of edge equipment, monitoring the service condition of the computing resources, and dynamically distributing and releasing the computing resources according to requirements;
and the performance monitoring unit is used for monitoring the performance and the state of the computing system in real time, and comprises a computing node load and response time.
8. The system according to claim 7, wherein said dynamic allocation specifically comprises:
defining a priority A, a weight Q and response time S of a task, and marking a priority sequence number for the task according to the priority;
the dynamic priority Y of the task is calculated, and the calculation formula is as follows:
and sequencing all the tasks according to the numerical value of the dynamic priority, updating the dynamic priority of all the tasks in real time, and sequencing the tasks again.
9. The system of claim 8, wherein the cloud-edge collaboration module comprises:
the module establishing unit is used for establishing an edge auxiliary decision database and storing historical abnormal information and processing results;
the auxiliary decision unit is used for automatically acquiring abnormal information and processing results of similar results in the edge auxiliary decision database according to the dynamic allocation calculation result and feeding back the abnormal information and the processing results to the user;
the data synchronization unit is used for maintaining the consistency and instantaneity of data between the cloud and the edge equipment through a data transmission and synchronization mechanism.
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