CN115604189A - Data collaboration method and device for edge computing device - Google Patents
Data collaboration method and device for edge computing device Download PDFInfo
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- CN115604189A CN115604189A CN202211207175.0A CN202211207175A CN115604189A CN 115604189 A CN115604189 A CN 115604189A CN 202211207175 A CN202211207175 A CN 202211207175A CN 115604189 A CN115604189 A CN 115604189A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/12—Avoiding congestion; Recovering from congestion
- H04L47/125—Avoiding congestion; Recovering from congestion by balancing the load, e.g. traffic engineering
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/76—Admission control; Resource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions
- H04L47/762—Admission control; Resource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions triggered by the network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/78—Architectures of resource allocation
- H04L47/783—Distributed allocation of resources, e.g. bandwidth brokers
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Abstract
The invention discloses a data collaboration method for edge computing equipment, which is characterized by comprising the following steps: the method comprises the following steps: the edge terminal evaluates whether the current edge terminal load pressure can meet the calculation task of the current request according to the calculation task request of the terminal equipment; step two: if the load of the edge end belongs to the normal range, the calculation force is provided to complete the current calculation task; if the computing power of the edge end cannot meet the current computing task, updating a peripheral available resource table, and inquiring whether the peripheral edge end can provide computing unloading cooperation or not through the broadcasting of a computing unloading fragmentation strategy; step three: if the peripheral edge end meets the calculation unloading cooperation, calculation force is provided; and if the peripheral edge end cannot meet the calculation unloading cooperation, submitting the calculation task to the central cloud for completion. The invention realizes the computation and unloading cooperation between the edge ends, solves the problem of unbalanced resource utilization of the edge ends in the existing edge computation, and reduces the central cloud computation pressure.
Description
Technical Field
The invention relates to the field of edge data collaboration, in particular to a method and a device for edge computing device data collaboration.
Background
In the prior art, because an edge node is deployed at an edge side, usually only a virtualized resource pool composed of a plurality of servers exists, but various devices of a terminal access an edge platform through the edge side, and therefore, the pressure of resource shortage at the edge side is usually large. In many scenes such as medical treatment, industry, car networking and the like, a plurality of terminals and sensors are connected into the edge platform through a network, but at present, a central cloud and each edge end are in interactive cooperation, the edge ends lack mutual cooperation, and the calculated force and pressure of each edge end are different, so that the load of part of the edge ends is overlarge, and the rest edge ends are in an idle state.
The existing edge data cooperation technology has the following defects: 1. the edge computing is organized to perform equipment upgrading and transformation to obtain more computing power or storage space. 2. The edge computer may need more maintenance than the server, and more unexpected events and physical damage may not be possible to achieve edge cooperation, and the edge node is an independent individual and does not form an effective organization cooperation scheduling mechanism. 3. The edge devices have different specifications, the calculation force and the storage provided by the edge are different, a large amount of calculation force distribution is unbalanced at present, the load of part of edge ends is overlarge, and the edge devices are easy to damage. In the prior art, due to unbalanced resource utilization of edge terminals of edge calculation, overload calculation force causes the possibility of damaging the service life of edge terminal equipment, and the load pressure of each edge terminal cannot be balanced.
Disclosure of Invention
The invention aims to: aiming at the existing problems, the edge computing equipment data cooperation method and the device are provided, the edge cooperation method is added on the basis of the existing cloud edge cooperation, the calculation force fragmentation is carried out between the edges, the tasks of other edges are completed in a cooperation mode, the edge expansion is dynamically realized, the idle resources are avoided, and the calculation pressure of a central cloud is reduced.
The technical scheme of the invention is as follows:
the invention discloses a data collaboration method for edge computing equipment, which comprises the following steps:
the method comprises the following steps: the edge terminal evaluates whether the current edge terminal load pressure can meet the calculation task of the current request according to the calculation task request of the terminal equipment;
step two: if the load of the edge end belongs to the normal range, the calculation force is provided to complete the current calculation task; if the edge end computing power cannot meet the current computing task, updating a peripheral available resource table, and inquiring whether the peripheral edge end can provide computing unloading cooperation or not through computing unloading fragmentation strategy broadcasting;
step three: if the peripheral edge end meets the calculation unloading cooperation, calculation force is provided; and if the peripheral edge end cannot meet the calculation unloading cooperation, submitting the calculation task to the central cloud for completion.
Further, if the edge load can provide the computational power, the current edge margin computational power is broadcasted to the periphery.
Further, the edge end detects the running state of the peripheral edge end; if the peripheral edge end state is normal, recording and periodically broadcasting synchronization; if the peripheral edge end state is abnormal, recording and immediately broadcasting the report.
Further, the edge end receives the peripheral request of the calculation unloading task, and if the calculation unloading scheduling cooperation task cannot be completed, the edge end immediately returns the cooperation request rejection; when an edge computing offload task request from the periphery is received, the edge computing offload task resource table is synchronized.
Further, the central cloud carries out calculation force detection of the edge end, collects edge end state information reported by the edge end, and records calculation unloading cooperative scheduling; analyzing and evaluating the computational load pressure of the edge end, and judging whether a pressure overload area needs to additionally store/calculate resources; accounting and calculating unloading scheduling records, reporting and calculating energy consumption/delay resource occupation and other evaluation reports of unloading scheduling.
Further, a specific method for broadcasting between edge terminals is as follows:
the current edge terminal initiates a broadcast to the adjacent edge equipment based on the current base station, searches the physically adjacent edge equipment and establishes a link through a network tunnel;
the edge equipment terminal which receives the broadcast information simultaneously initiates a broadcast addressing request by taking the edge equipment terminal as a center, thereby establishing a broadcast network by taking the edge equipment terminal as the center and returning the acquired communication address which accords with the edge equipment to the original request;
recording peripheral normally-running edge equipment through regular activity detection, and performing polling detection in a broadcast domain if the edge equipment has fault and communication is terminated; and when the detection loss of the peripheral equipment exceeds half of the detection loss, the edge equipment is judged to be a fault point, and the edge state is synchronized.
Further, a specific method for force cooperation between edge ends is as follows:
the method comprises the steps that edge routes which are physically adjacent to a network space and take a base station network edge record as a center are synchronously updated into a calculation force routing table based on calculation force synchronous records of each edge of the edge communication route; and selecting the edge end to cooperate according to the available calculation force.
Further, if the edge end node receives a computing task which cannot be independently processed, a computing force cooperation request of the edge end which can cooperate is carried out through a computing force optimization algorithm, the computing task is divided into storage intensive type, memory intensive type, cpu intensive type and gpu intensive type, weighting and scoring are carried out on each edge end node, and computing force cooperation with the highest comprehensive score is obtained.
Furthermore, if the computing tasks received by the edge terminals are too large, the computing tasks are disassembled when the computing tasks are submitted, the computing tasks are distributed to a plurality of edge terminal nodes for parallel computing, and finally the computing tasks are combined by the main task terminal; and if the calculation task disassembly cannot be met, submitting a calculation task request to the central cloud end by the edge end, and completing the calculation task by the central cloud cluster.
The invention relates to a data collaboration device of edge computing equipment, which comprises a central cloud and at least two edge terminals, wherein the two edge terminals are respectively communicated with the central cloud;
the central cloud is used for computing power detection, gathering edge terminal state information reported by edge terminals, and recording computing unloading cooperative scheduling; analyzing and evaluating the calculated force load pressure of the edge end, and judging whether the pressure overload area needs additional storage/calculation resources; accounting and calculating unloading scheduling records, reporting and calculating energy consumption/delay resource occupation evaluation reports of unloading scheduling and the like;
the edge end is used for providing computing power for computing tasks of the edge equipment end; if the computing power is insufficient, requesting to provide computing unloading cooperation to a peripheral edge end or a central cloud; and meanwhile, detecting the running state of the peripheral edge end, receiving a peripheral request for calculating an unloading task, and judging whether to approve or reject cooperation according to self calculation power.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the invention, by establishing calculation unloading cooperative scheduling between the edge ends, the problems of unbalanced resource utilization of the edge ends and high central cloud computing pressure of the existing edge calculation are solved.
2. The invention realizes cooperative communication between the edge terminals, the edge terminals mutually detect and update the running states of the peripheral edges, and report the running states in a broadcast mode, thereby clearing the computing power condition of the edge terminals, carrying out scheduling cooperation and improving the effective utilization rate of the edge terminal resources.
3. According to the invention, the edge resource/computing power cooperation does not destroy the original cloud-edge-end cooperation mechanism, a layer of edge cooperation detection logic is added, computing power fragmentation can be performed by the edge, and most of the computing tasks needing central cloud participation before can be completed in cooperation.
4. The invention is based on heterogeneous distributed equipment expansion, retains original edge equipment, transfers edge calculation force, balances load pressure of each edge end, avoids calculation force idling or load pressure, and reduces the updating and upgrading cost of the edge equipment.
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The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
fig. 1 is an edge architecture diagram of a conventional scene.
FIG. 2 is an architecture diagram of an edge computing device data coordination apparatus of the present invention.
FIG. 3 is a flowchart of an edge computation edge collaborative computation offload task according to the present invention.
Fig. 4 is a schematic diagram of broadcast discovery in an embodiment.
FIG. 5 is a schematic diagram of computational force synergy in an embodiment.
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification (including any accompanying claims, abstract) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
The features and properties of the present invention are described in further detail below with reference to examples.
As shown in fig. 1, an edge architecture of a conventional scenario includes a central cloud and an edge, where the central cloud and the edge respectively communicate with each other to directly perform a computation offload task: processing the computing tasks of the edge end by the edge end, and submitting the computing tasks which cannot be processed by the edge end to central cloud processing if the computing tasks exist; each edge only processes the calculation and storage tasks of the edge, and the edge does not communicate with each other.
As shown in fig. 2, the present invention discloses an edge computing device data coordination apparatus, which includes a central cloud and edge terminals, wherein the central cloud is located at a cloud end, and the edge cloud end is connected to the central cloud through an ethernet. And submitting the central cloud to perform task computing when the task cannot be processed by the edge end or the cooperative computing task cannot be provided by the edge periphery. And carrying out edge synchronous detection information between edge terminals, and initiating a calculation task cooperation request.
The central cloud is used for computing power detection, summarizing the edge terminal state information reported by the edge terminal and recording computing unloading cooperative scheduling; analyzing and evaluating the calculated force load pressure of the edge end, and judging whether the pressure overload area needs additional storage/calculation resources; accounting and calculating unloading scheduling records, reporting and calculating energy consumption/delay resource occupation evaluation reports of unloading scheduling and the like;
the edge end is used for providing computing power for computing tasks of the edge equipment end; if the computing power is insufficient, requesting to provide computing unloading cooperation to a peripheral edge end or a central cloud; and meanwhile, detecting the running state of the peripheral edge end, receiving a peripheral request for calculating an unloading task, and judging whether to approve or reject cooperation according to self calculation power.
As shown in fig. 3, the present invention discloses an edge computing device data collaboration method, including:
the method comprises the following steps: the edge terminal evaluates whether the current edge terminal load pressure can meet the calculation task of the current request according to the calculation task request of the terminal equipment;
step two: if the load of the edge end belongs to the normal range, the calculation force is provided to complete the current calculation task; if the edge end computing power cannot meet the current computing task, updating a peripheral available resource table, and inquiring whether the peripheral edge end can provide computing unloading cooperation or not through computing unloading fragmentation strategy broadcasting;
step three: if the peripheral edge end meets the calculation unloading cooperation, calculation force is provided; and if the peripheral edge end cannot meet the calculation unloading cooperation, submitting the calculation task to the central cloud for completion.
The method specifically comprises the following steps:
edge end flow (resource shortage):
s1: the terminal equipment initiates a calculation task request to the edge terminal, and receives and evaluates a calculation task close to the edge terminal;
s2: evaluating whether the current edge end load pressure can meet the calculation task of the current request;
s3: filtering the edge terminal resource detection data, if the edge terminal load belongs to a normal range, providing calculation power to complete the current calculation task, finishing the scheduling, completing the calculation task by the current edge terminal, and broadcasting the surplus calculation power of the current edge terminal to the periphery;
s4: if the edge end computing power can not meet the current computing task, updating a peripheral available resource table, and inquiring whether the filtered edge can provide computing unloading cooperation or not through computing unloading slicing strategy broadcasting;
s5: the peripheral edge end cannot meet the calculation and unloading cooperation, the edge end submits the calculation task to the central cloud end, and the central cloud end completes the processing and returns to the user equipment end.
Edge end flow (resource abundant):
s10: detecting the running state of the peripheral edge end, and receiving a peripheral request for calculating an unloading task;
s20: receiving an edge computing unloading task request from the periphery, and synchronizing an edge computing unloading task resource table;
s30: if the current computation unloading task can be satisfied, providing computation power and broadcasting and synchronizing self available computation power resources to the adjacent nodes;
s40: and if the calculation unloading scheduling cooperation task cannot be completed, returning to the cooperation request rejection immediately, and broadcasting the self available calculation resources to the peripheral edge terminal.
Edge end flow (central cloud):
s100: computing power detection, summarizing edge terminal state information reported by an edge terminal, and recording computing unloading cooperative scheduling;
s200: analyzing and evaluating the calculated force load pressure of the edge end, and judging whether the pressure overload area needs additional storage/calculation resources; s300: accounting and calculating unloading scheduling records, reporting and calculating energy consumption/delay resource occupation and other evaluation reports of unloading scheduling.
In an embodiment, as shown in fig. 4, the broadcast discovery includes:
a. the current edge terminal initiates a broadcast to the adjacent edge equipment based on the current base station, searches the physically adjacent edge equipment and establishes a link through a network tunnel;
b. the edge equipment terminal which receives the broadcast information simultaneously initiates a broadcast addressing request by taking the edge equipment terminal as a center, thereby establishing a broadcast network by taking the edge equipment terminal as the center and returning the collected communication address which accords with the edge equipment to the source request;
c. and recording the peripheral normally-operated edge equipment through a strategy of regular detection and alive inspection, carrying out polling detection in the broadcast domain if the fault communication of the edge equipment is terminated, and considering the edge equipment as a fault point and synchronizing the edge state when the detection loss of the peripheral equipment exceeds half.
As shown in fig. 5, the computational cooperation includes:
a. the method comprises the steps that edge routes which are physically adjacent to a network space and take a base station network edge record as a center are synchronously updated into a calculation force routing table based on calculation force synchronous records of each edge of the edge communication route;
b. when the current node receives a calculation task which cannot be independently processed, a calculation force cooperation request of edge ends which can cooperate is carried out through a calculation force optimal calculation algorithm, the calculation task is divided into a storage intensive type, a memory intensive type, a cpu intensive type and a gpu intensive type, weighting and scoring are carried out on each edge node, and calculation force cooperation with the highest comprehensive score is obtained;
c. if the computing tasks received by the edge are too large, the computing tasks can be disassembled when the computing tasks are submitted, the computing tasks are distributed to a plurality of edge nodes for parallel computing, and finally the computing tasks are combined by the main task end;
d. when all the steps can not meet the requirement of disassembling the computing task, the computing task request is submitted to the central cloud end by the edge end, and the computing task is completed by the powerful central cloud cluster.
In the prior art, when the edge end computing force is overloaded, the computing and unloading task is directly carried out through the edge end and the central cloud, but the scheme adds an implementation method of edge computing and unloading cooperation on the original basis, and can dynamically realize edge expansion by changing into the implementation; compared with the prior art, the scheme provides good heterogeneous compatibility of the equipment, realizes dynamic load of the edge end, realizes edge computing unloading cooperative scheduling, avoids resource idling, reduces computing pressure of the central cloud end, is relatively independent at each edge end in the prior art, and adds organization cooperative management at present.
The above-mentioned embodiments only express the specific embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for those skilled in the art, without departing from the technical idea of the present application, several changes and modifications can be made, which are all within the protection scope of the present application.
Claims (10)
1. A method for edge computing device data collaboration, comprising:
the method comprises the following steps: the edge terminal evaluates whether the current edge terminal load pressure can meet the calculation task of the current request according to the calculation task request of the terminal equipment;
step two: if the load of the edge end belongs to the normal range, the calculation force is provided to complete the current calculation task; if the edge end computing power cannot meet the current computing task, updating a peripheral available resource table, and inquiring whether the peripheral edge end can provide computing unloading cooperation or not through computing unloading fragmentation strategy broadcasting;
step three: if the peripheral edge end meets the calculation unloading cooperation, calculation force is provided; and if the peripheral edge end cannot meet the calculation unloading cooperation, submitting the calculation task to the central cloud for completion.
2. The method of claim 1, wherein if the edge load can provide the computing power, broadcasting the current edge margin computing power to the perimeter.
3. The method for data collaboration of an edge computing device according to claim 1 or 2, wherein the edge end detects an operation state of a peripheral edge end; if the peripheral edge end state is normal, recording and periodically broadcasting synchronization; if the peripheral edge end state is abnormal, recording and immediately broadcasting the report.
4. The method for data collaboration of edge computing devices as claimed in claim 3, wherein the edge receives peripheral requests for computation offload tasks, and immediately returns a collaboration request rejection if the computation offload scheduling collaboration tasks cannot be completed; and when receiving the edge computing unloading task request from the periphery, synchronizing the edge computing unloading task resource table.
5. The method for edge computing device data collaboration according to claim 1 or 4, wherein the central cloud performs computation force detection of the edge, summarizes edge state information reported by the edge, and records computation offload collaboration scheduling; analyzing and evaluating the computational load pressure of the edge end, and judging whether a pressure overload area needs to additionally store/calculate resources; accounting and calculating unloading scheduling records, reporting and calculating energy consumption/delay resource occupation and other evaluation reports of unloading scheduling.
6. The method for data collaboration of the edge computing device of claim 1 wherein the specific method of broadcasting between edge terminals is:
the current edge terminal initiates a broadcast to adjacent edge equipment based on the current base station, searches for physically adjacent edge equipment and establishes a link through a network tunnel;
the edge equipment terminal which receives the broadcast information simultaneously initiates a broadcast addressing request by taking the edge equipment terminal as a center, thereby establishing a broadcast network by taking the edge equipment terminal as the center and returning the acquired communication address which accords with the edge equipment to the original request;
recording peripheral normally-running edge equipment through periodic detection and alive inspection, and performing polling detection in a broadcast domain if the edge equipment fails and communication is terminated; and when the detection loss of the peripheral equipment exceeds half of the detection loss, the edge equipment is judged to be a fault point, and the edge state is synchronized.
7. The method for data collaboration of edge computing devices as claimed in claim 1 or 6, wherein the specific method for computational collaboration between edge terminals comprises:
the method comprises the steps that edge routes which are physically adjacent to a network space and take a base station network edge record as a center are synchronously updated into a calculation force routing table based on calculation force synchronous records of each edge of the edge communication route; and selecting the edge end to cooperate according to the available calculation force.
8. The method for data collaboration of edge computing devices according to claim 7, wherein if an edge node receives a computing task that cannot be processed independently, a computing power collaboration request of edge nodes that can collaborate is performed through a computing power optimal computation algorithm, the computing task is divided into storage-intensive type, memory-intensive type, cpu-intensive type and gpu-intensive type, and weighting and scoring are performed on each edge node to obtain the computing power collaboration with the highest comprehensive score.
9. The method for data collaboration of edge computing devices as claimed in claim 7, wherein if the computing tasks received by the edge end are too large, the computing tasks are disassembled when the computing tasks are submitted, the computing tasks are distributed to a plurality of edge end nodes for parallel computing, and finally the computing tasks are merged by the main task end; and if the requirement for disassembling the computing task cannot be met, submitting a computing task request to the central cloud end by the edge end, and finishing the computing task by the central cloud cluster.
10. The device for data collaboration of the edge computing equipment is characterized by comprising a central cloud and at least two edge terminals which are respectively communicated with the central cloud, wherein the edge terminals are communicated with each other;
the central cloud is used for computing power detection, gathering edge terminal state information reported by edge terminals, and recording computing unloading cooperative scheduling; analyzing and evaluating the computational load pressure of the edge end, and judging whether a pressure overload area needs to additionally store/calculate resources;
accounting calculation unloading scheduling records, reporting evaluation reports such as energy consumption/delay resource occupation of calculation unloading scheduling;
the edge end is used for providing computing power for computing tasks of the edge equipment end; if the computing power is insufficient, requesting to provide computing unloading cooperation to a peripheral edge end or a central cloud; and meanwhile, detecting the running state of the peripheral edge end, receiving a peripheral request for calculating an unloading task, and judging whether to approve or reject cooperation according to self calculation power.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116418124A (en) * | 2023-06-12 | 2023-07-11 | 广东采日能源科技有限公司 | Micro-grid control system and energy storage power station control system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116418124A (en) * | 2023-06-12 | 2023-07-11 | 广东采日能源科技有限公司 | Micro-grid control system and energy storage power station control system |
| CN116418124B (en) * | 2023-06-12 | 2023-10-13 | 广东采日能源科技有限公司 | Micro-grid control system and energy storage power station control system |
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