CN115633051A - Edge computing node self-cooperation management method - Google Patents
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Abstract
The invention provides a self-cooperative management method for edge computing nodes, which comprises the steps of initializing after the edge computing nodes N are deployed, connecting to a network and starting a service program S N And a client program C N (ii) a The edge computing node N starts a client program C N Performing subnet scanning, and forming formatted data for storage; the edge computing node M scanned by the edge computing node N updates the dynamic statistical table T of the edge computing node M M Adding the information of the node N to T M In the table, and modifying the table metadata; edge calculation node N and dynamic statistical table T N According to T N The version number of obtains the table data T of the latest version NN (ii) a All in the sub-networkEnabling the edge computing nodes to reach a consistent state, and enabling one edge computing node to become a main node through an election algorithm; the main nodes are used for communicating with the cloud platform, and when the main nodes are in failure and are down, each edge computing node can automatically scan the sub-network and reselect a new main node, so that the self-coordination management of the nodes is realized.
Description
Technical Field
The invention relates to the technical field of edge computing, in particular to a method for self-collaborative management of edge computing nodes.
Background
Edge computing means that an open platform integrating network, computing, storage and application core capabilities is adopted on one side close to an object or a data source to provide nearest-end services nearby. The application program is initiated at the edge side, so that a faster network service response is generated, and the basic requirements of the industry in the aspects of real-time business, application intelligence, safety, privacy protection and the like are met. The edge computation is between the physical entity and the industrial connection, or on top of the physical entity. The edge calculation is used as a bridge from a physical world to a digital world, is a first entrance of data, has a large amount of real-time and complete data, can perform management and value creation based on the whole life cycle of the data, and can better support innovative applications such as predictive maintenance, asset management and efficiency improvement; meanwhile, as a first entry of data, edge computation also faces challenges of data real-time, certainty, integrity, accuracy, diversity and the like.
The actual deployment of edge computing naturally has distributed characteristics, which require that the edge computing supports distributed computing and storage, realizes dynamic scheduling and unified management of distributed resources, supports distributed intelligence, and has the capabilities of distributed security and the like. In a cloud, edge, and end collaborative platform system, edge computing nodes are usually directly connected to a cloud platform, taking an MEC (Mobile Edge computer) reference architecture as an example, each Edge computing node is connected to a Mobile Edge platform in the cloud, and the cloud is also provided with modules such as a Mobile Edge platform manager, a Mobile Edge organizer, an Edge computing application lifecycle manager, and the like, which complete the management functions of the Edge computing nodes together, and the Edge computing nodes are substantially free of mutual communication and collaborative computing.
In fact, according to the specific implementation and deployment conditions of the architecture scheme of the cloud edge-side integrated system, many of the current edge computing nodes have strong computing power, and after the edge computing nodes complete preset execution tasks and services, part of the computing power is idle, computing resources are not fully utilized, and the edge computing nodes lack mutual communication and collaborative computing. In some application scenarios, even the edge computing node is only used as a transit service point for data collection, and a large amount of computing tasks are handed over to the cloud to be completed, which causes the idle computing power of the edge computing node to be more serious.
Disclosure of Invention
In order to achieve the above purpose, the present application provides a method for edge computing node self-collaborative management, which aims to overcome some defects of the existing cloud, edge, and end integrated system, and further excavate the computing power potential of the edge computing node, so that the computing resources of the whole system are more fully utilized, thereby reducing the implementation cost of the whole system.
A method for self-cooperative management of edge computing nodes comprises the following steps:
step 1: the edge computing node N is initialized after deployment, is connected to the network and starts a service program S N And a client program C N 。
The nth edge compute node is denoted by the letter N and each has built in a corresponding service program S and client program C.
The edge computing node is a terminal physical computing device and has certain computing power.
The network connection refers to the connection of the edge computing node to the Internet through an HTTP/HTTPS protocol.
The service program S N And opening a preset port to provide long connection service for the outside.
In the method, preferably, the network protocol for providing the long connection service may adopt a WebSocket protocol or an MQTT protocol.
The client program C N After starting, the subnet to which the current node belongs is automatically scanned, and all edge computing nodes existing in the subnet are searched.
The method above, further, the scanning task may be set as a periodic task, that is, executed at regular time intervals.
And 2, step: the edge computing node N starts a client program C N And performing subnet scanning, and forming the scanning result into formatted data for storage after the subnet scanning is completed.
The formatted data is a dynamic statistical table T of edge computing nodes N The method at least comprises key fields such as node names, node types, node network addresses, node states and the like.
The dynamic statistical table T N The system also comprises self metadata, wherein the self metadata at least comprises information such as version number, time stamp, version change identification, current identification, modification identification, main node identification and the like.
The above method, preferably, may be a dynamic statistical table T N And the data is stored in a memory space, and the access and execution efficiency is accelerated by an edge cache technology.
And step 3: the edge computing node M scanned by the edge computing node N updates the dynamic statistical table T of the edge computing node M M Adding information of node N to T M In the table, and modifies the table metadata.
The scanning is a client program C of the node N N Service program S with scanned node M M The node M and the node N both obtain basic information of each other.
The node M updates the dynamic statistical table T of the node M M Adding a record of node N to the table and updating the table T M Such as version number plus 1, etc.
And 4, step 4: edge computing node N and dynamic statistical table T N According to T N The version number of obtains the table data T of the latest version NN And then the comparison is performed locally.
Said local comparison is T N Watch and T NN The data in the table are compared and there are several cases:
one piece of data is in T N Is not present in the table, but is present at T NN If present in the table, then copy the data to T N And adding 1 to the modification identification metadata of the table.
A piece of dataAt T N Present in the table, and at T NN If not, then T is N Table version number metadata modification to T NN Version number of table and add 1.
One piece of data is in T N Present in the table, and at T NN The data in the tables are not consistent, although they are also present. In this case, a local fault generally exists in the network, and the edge computing node assembles relevant information into a log and sends the log to the cloud.
One piece of data is in T N Present in the table, and at T NN The data in the table are consistent, and the table is not further processed and continues to work next.
The method preferably implements a set of version numbers locally, the version numbers that have been compared being put into the set. Retrieving a version number set before each comparison, and skipping comparison if the set has a version number to be compared; if there is no version number to be compared, a comparison operation is performed.
And 5: and all edge computing nodes in the sub-network reach a consistent state, and one edge computing node is made to be a main node through an election algorithm.
The consistent state is determined by the metadata of the version number of the dynamic statistical table of each node, and when the version numbers of the dynamic statistical tables on all edge computing nodes in the subnet are the same version, the consistent state is judged.
The election algorithm may be a random election algorithm.
The above method, preferably, may also use a weighted random selection algorithm to set a higher weight in advance for the more computationally intensive nodes.
The method preferably can select the secondary main node, the 3 rd main node, the 4 th main node \8230;, so that the secondary main node, the 3 rd main node, the 4 th main node, etc. take over the functions of the main nodes in turn after the main nodes are down.
Step 6: the main node is responsible for communicating with the cloud platform, acquiring the operation tasks and the calculation tasks issued by the cloud end, and then distributing the tasks to other edge calculation nodes to execute and complete the tasks.
The master node assumes the function of task acceptance and assignment to each node.
In the method, preferably, the master node also has the functions of adjusting the node traffic and adjusting the task calculation distribution.
In the method, preferably, the master node is also responsible for monitoring the task execution, resource utilization rate, traffic, load and other conditions of other edge computing nodes.
And 7: when the main node goes down due to a fault, each edge computing node automatically scans the sub-network and reselects a new main node, thereby realizing the self-coordination management of the nodes.
The edge computing is mainly deployed in a distributed mode, and each edge computing node has certain computing and storing capacity and provides corresponding service for terminals connected to the node.
When data and calculated amount of a certain node are suddenly increased, the problems of node overload and the like can occur, or the problems of uneven flow and calculation distribution, large processing pressure of a certain node and idle of a large amount of calculation and storage resources of adjacent nodes can occur. Under the condition, the main node coordinates the tasks of the adjacent nodes of the nodes with large load to ensure that the adjacent nodes cooperate to carry out caching and calculation, and the cooperation mode can effectively reduce the network operation cost and improve the network performance.
The invention has the beneficial effects that: compared with the prior art, the edge computing node self-cooperation management method provided by the invention can further improve the overall efficiency of the cloud, edge and end integrated system and release the computing potential of the edge computing end, thereby achieving the purposes of reducing the cost and improving the efficiency of the overall system.
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FIG. 1 is a schematic diagram of an overall structure of a cloud, edge, and end system in this embodiment;
FIG. 2 is a schematic flow chart illustrating a method for edge computing node self-coordination management according to this embodiment;
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
On the contrary, this application is intended to cover any alternatives, modifications, equivalents and variations that may be made within the spirit and scope of the present application as defined by the appended claims. Furthermore, in the following detailed description of the present application, certain specific details are set forth in order to provide a thorough understanding of the present application. It will be apparent to one skilled in the art that the present application may be practiced without these specific details.
A method for edge computing node self-coordination management according to an embodiment of the present application will be described in detail below with reference to fig. 1-2. It is to be noted that the following examples are only for explaining the present application and do not constitute a limitation to the present application.
A method for self-cooperative management of edge computing nodes comprises the following steps:
step 201: the edge computing node N is initialized after deployment, is connected to the network, and starts a service program SN and a client program CN.
The nth edge computing node is denoted by the letter N, and each edge computing node has a corresponding service program S and client program C built therein.
The edge computing node is terminal physical computing equipment and has certain computing power.
The network connection refers to the connection of the edge computing node to the Internet through an HTTP/HTTPS protocol.
And the service program SN opens a preset port and provides long connection service for the outside.
In the method, preferably, the network protocol for providing the long connection service may adopt a WebSocket protocol or an MQTT protocol.
After the client program CN is started, the subnet to which the current node belongs is automatically scanned, and all edge computing nodes existing in the subnet are searched.
The method as described above, further, the scanning task may be set as a periodic task, that is, performed at regular time intervals.
Step 202: and the edge computing node N starts a client program CN to perform subnet scanning, and forms a scanning result into formatted data for storage after the subnet scanning is completed.
The formatted data is a dynamic statistical table TN of the edge computing node, and at least comprises key fields of a node name, a node type, a node network address, a node state and the like.
The dynamic statistical table TN further includes metadata of its own, which at least includes information such as a version number, a timestamp, a version change identifier, a current identifier, a modification identifier, and a master node identifier.
According to the method, the dynamic statistical table TN can be preferably stored in a memory space, and the access and execution efficiency is accelerated through an edge cache technology.
Step 203: the edge computing node M scanned by the edge computing node N updates the dynamic statistical table TM of the edge computing node M, adds the information of the node N into the TM table, and modifies the table metadata.
The scanning is a long connection communication between a client program CN of the node N and a service program SM of the scanned node M, and both the node M and the node N acquire basic information of each other.
The node M updates its own dynamic statistics table TM by adding a record of the node N to the table and updating metadata of the table TM, such as adding 1 to the version number.
Step 204: the edge computing node N communicates with all nodes in the dynamic statistical table TN, acquires the table data TNN of the latest version according to the version number of the TN, and then compares the table data TNN locally.
The local comparison is to compare the data of the TN table and the TNN table, and the following conditions exist:
a piece of data is not present in the TN table but present in the TNN table, the data is copied to the TN table and the table's modification identification metadata is incremented by 1.
One piece of data exists in the TN table but does not exist in the TNN table, the version number metadata of the TN table is modified to the version number of the TNN table and 1 is added.
One piece of data exists in the TN table and also exists in the TNN table, but the data of the TN table and the TNN table are not consistent. In this case, usually, a local fault exists in the network, and the edge computing node aggregates the relevant information into a log and sends the log to the cloud.
One piece of data exists in the TN table and also exists in the TNN table, and the data of the TN table and the TNN table are consistent, so that the next step is continued without further processing.
The method preferably implements a set of version numbers locally, with the version numbers that have been compared in the set. Retrieving a version number set before each comparison, and skipping comparison if the set has a version number to be compared; if there is no version number to be compared, a comparison operation is performed.
Step 205: and all edge computing nodes in the sub-network reach a consistent state, and one edge computing node is made to be a main node through an election algorithm.
The consistent state is determined by the metadata of the version number of the dynamic statistical table of each node, and when the version numbers of the dynamic statistical tables on all edge computing nodes in the subnet are the same version, the consistent state is judged.
The election algorithm may be a random election algorithm.
The above method, preferably, may also use a weighted random selection algorithm to set a higher weight in advance for the more computationally intensive nodes.
The method preferably can also select the secondary main nodes, the 3 rd main node and the 4 th main node at one time for 8230, so that the secondary main nodes, the 3 rd main node, the 4 th main node and the like sequentially take over the functions of the main nodes after the main nodes are down.
Step 206: the main node is responsible for communicating with the cloud platform, acquiring operation tasks and computing tasks issued by the cloud end, and then distributing various tasks to other edge computing nodes to execute and complete the tasks.
The master node assumes the function of task acceptance and assignment to each node.
In the method, preferably, the master node also performs functions of adjusting node traffic and adjusting task calculation distribution.
In the method, preferably, the master node is also responsible for monitoring the task execution, resource utilization rate, traffic, load and other conditions of other edge computing nodes.
Step 207: when the main nodes are in failure and are down, each edge computing node automatically scans the sub-network and reselects a new main node, thereby realizing the self-cooperation management of the nodes.
The edge computing is mainly deployed in a distributed mode, and each edge computing node has certain computing and storing capacity and provides corresponding service for terminals connected to the node.
When data and calculated amount of a certain node are suddenly increased, the problems of node overload and the like can occur, or the flow and calculation are not uniformly distributed, and a certain node has higher processing pressure and a large amount of calculation and storage resources of adjacent nodes are idle. Under the condition, the main node coordinates the tasks of the adjacent nodes of the nodes with large load to ensure that the adjacent nodes cooperate to carry out caching and calculation, and the cooperation mode can effectively reduce the network operation cost and improve the network performance.
In summary, the invention provides a method for self-coordinated management of edge computing nodes. Compared with the prior art, the invention has the advantages of further improving the overall efficiency of the cloud, edge and end integrated system and releasing the computing power potential of the edge computing end, thereby achieving the purposes of reducing the cost and improving the efficiency of the overall system.
The above description is intended to be illustrative of the preferred embodiment of the present invention and should not be taken as limiting the invention, but rather, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (9)
1. A method for self-cooperative management of edge computing nodes is characterized by comprising the following steps:
step 1: the edge computing node N is initialized after deployment, is connected to the network and starts a service program S N And a client program C N ；
Step 2: the edge computing node N starts a client program C N Performing subnet scanning, and forming a scanning result into formatted data for storage after the subnet scanning is completed;
and step 3: the edge computing node M scanned by the edge computing node N updates the dynamic statistical table T of the edge computing node M M Adding information of node N to T M In the table, and modifying the table metadata;
and 4, step 4: edge calculation node N and dynamic statistical table T N According to T N The version number of obtains the table data T of the latest version NN Then the comparison is performed locally;
and 5: all edge computing nodes in the sub-network reach a consistent state, and one edge computing node is made to be a main node through an election algorithm;
and 6: the main node is used for communicating with the cloud platform, acquiring an operation task and a computing task issued by the cloud end, and distributing the acquired task to other edge computing nodes to execute and complete the task;
and 7: when the main nodes are in failure and are down, each edge computing node automatically scans the sub-network and reselects a new main node, thereby realizing the self-cooperation management of the nodes.
2. The method for self-coordinated management of edge computing nodes according to claim 1, wherein the step 1: initializing after deployment of edge computing node N, connecting to network, and starting service program S N And a client program C N The service program S N Opening a preset port and providing long connection service for the outside;
the client program C N After starting, the subnet to which the current node belongs is automatically scanned, and all edge computing nodes existing in the subnet are searched.
3. The method for edge computing node self-coordinated management according to claim 2, wherein the client program C N After starting, automatically scanning the sub-network to which the current node belongs to execute one operation at regular time intervalsNext, the process is repeated.
4. The method for edge computing node self-cooperative management according to claim 1, wherein the step 2: the edge computing node N starts a client program C N Performing subnet scanning, and forming the scanning result into formatted data for storage after the subnet scanning is completed, wherein the formatted data is a dynamic statistical table T of an edge computing node N At least including node name, node type, node network address and key field of node state;
the dynamic statistical table T N The system also comprises self metadata which at least comprises version number, time stamp, version change identification, current identification, modification identification and main node identification information.
5. The method for self-coordinated management of edge computing nodes according to claim 1, wherein the step 3: the edge computing node M scanned by the edge computing node N updates the dynamic statistical table T of the edge computing node M M Adding the information of the node N to T M In the table, and in the modification of the table metadata:
the scanning is a client program C of the node N N Service program S with scanned node M M The node M and the node N both acquire basic information of each other;
the node M updates the dynamic statistical table T of the node M M Adding a record of node N to the table and updating the table T M The metadata of (1).
6. The method for edge computing node self-cooperative management according to claim 1, wherein the step 4: edge calculation node N and dynamic statistical table T N According to T N The version number of obtains the table data T of the latest version NN Then, comparing locally, including implementing a version number set locally, and putting the compared version numbers into the set; retrieving a set of version numbers before each comparison, if any exist in the set to be comparedIf so, skipping comparison; if there is no version number to be compared, a comparison operation is performed.
7. The method for edge computing node self-cooperative management according to claim 1, wherein the step 5: and when the version numbers of the dynamic statistical tables on all the edge computing nodes in the subnet are the same version, the state consistency is judged.
8. The method for self-coordinated management of edge computing nodes according to claim 1, wherein the step 6: the main node is used for communicating with the cloud platform, comprises an operation task and a computing task which are issued by the cloud end, distributes the acquired task to other edge computing nodes to execute and complete, and bears the functions of receiving and distributing the task to each node.
9. The method for edge computing node self-cooperative management according to claim 1, wherein in step 6, the node also takes on the functions of adjusting node traffic and adjusting task computation distribution.
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