CN115633314A - Distributed edge computing information scheduling method based on information priority - Google Patents
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Abstract
The invention belongs to the technical field of edge computing, and particularly relates to a distributed edge computing information scheduling method based on information priority, which comprises the following steps: s1, constructing a distributed communication scene; s2, constructing a dynamic network topological structure and an information injection model, and endowing different priorities for the injected information by the terminal node according to the emergency degree of the information; s3, broadcasting the benchmark probability of the current round by the edge node, and setting the transmission probability of the current round by the terminal node according to the benchmark probability; s4, the terminal equipment randomly selects a message to be sent from the message queue according to the proportion of the message priority, the message is transmitted according to the transmission probability of the current round obtained in the S3 in the transmission stage, and meanwhile, the edge node monitors a channel and dynamically adjusts the reference probability of the next round according to the current channel state; and S5, the edge node feeds back the information successfully received in the transmission stage. The method has the advantage of solving the problem of wireless communication between a plurality of terminal nodes and an edge device.
Description
Technical Field
The invention belongs to the technical field of edge computing, and particularly relates to a distributed edge computing information scheduling method based on information priority.
Background
In order to solve the problems of high delay, instability, low bandwidth and the like caused by the traditional cloud computing, edge computing is rapidly developed in recent years as an emerging technology. The edge calculation migrates the calculation resources from the center to the edge, so that the edge node has the capability of processing certain simple requests, the calculation burden of the cloud can be effectively reduced, and the response speed is improved. In a practical scenario, one edge node may need to be responsible for handling requests of multiple end nodes, and how to solve contention and interference problems in a wireless channel, and at the same time, ensuring that a request arrives at the edge node from an end node as quickly as possible is a key challenge facing edge computing.
However, most scheduling algorithms adopt a centralized method, that is, all requests are scheduled by a central node (usually a base station). The method can ensure the high efficiency of scheduling, but in the mode, all messages need to be gathered to the central node, the information flow is mixed, the algorithm design is complex, and meanwhile, the method has higher requirements on energy consumption. With the increase of network scale, centralized scheduling is difficult to ensure efficient communication.
Compared with a centralized method, the distributed method adopts the concept of decentralization, all nodes run the same algorithm and cooperate with each other to complete one task. The method does not need to know the global information of the network, so that the method is more suitable for large-scale networks and energy-limited scenes. However, how to design distributed algorithms to achieve efficiency as close as possible to that in a centralized environment is a challenge in distributed algorithm design.
On the other hand, most of the centralized scheduling algorithms and the distributed scheduling algorithms do not consider the priority of the request message and treat all messages equally. However, in an actual edge calculation scenario, there are often some more urgent messages that need to be responded to first, such as a signal of a natural disaster being detected. Therefore, designing a distributed scheduling algorithm with priority and high efficiency is crucial to the development of edge computing.
Disclosure of Invention
In order to solve the problems and improve the utilization rate of a wireless channel, the invention provides a distributed edge computing information scheduling algorithm based on information priority. The algorithm gives a priority to each message, and transmits the messages by using a random method, so that the probability of each round of transmission of the messages is in direct proportion to the priority of the messages. Meanwhile, the transmission probability is dynamically adjusted by using a binary exponential backoff method so as to solve the problems of competition and interference in a wireless channel. While considering fairness, the method can ensure that the channel utilization rate is constant and optimal.
A distributed edge computing information scheduling method based on information priority comprises the following steps:
s1, constructing a distributed communication scene;
s2, constructing a dynamic network topological structure and an information injection model, and endowing different priorities for the injected information by the terminal nodes according to the emergency degree of the information;
s3, broadcasting the reference probability of the current round by the edge node, and setting the transmission probability of the current round by the terminal node according to the reference probability;
s4, the terminal equipment randomly selects a message to be sent from the message queue according to the proportion of the message priority, the message is transmitted according to the transmission probability of the current round obtained in the S3 in the transmission stage, and meanwhile, the edge node monitors a channel and dynamically adjusts the reference probability of the next round according to the current channel state;
and S5, the edge node feeds back the information successfully received in the transmission stage.
Preferably, the step S2 is to construct a dynamic network topology model, specifically comprising the steps of,
constructing 1 edge node with fixed position, which is equipped with server and half-duplex signal transmitter and is responsible for processing various edge calculation requests;
constructing n terminal nodes, wherein each terminal node is responsible for monitoring the respective responsible environment and possibly generating various information requesting edge computing service; the terminal nodes are fixed positions or can move freely in the communication range of the edge nodes; the edge nodes and the terminal nodes both adopt a half-duplex mode for communication, and the nodes can only select one of the sending information or the monitoring channel at each moment and cannot send while monitoring.
Preferably, the information injection model is constructed in the step S2, and the specific process is that in each time round t, each terminal node generates a new message according to a specific probability for simulating the dynamics generated by the information in the actual edge calculation scene, and for a specific edge node i, the new message is generated by pi i Generating a new message; or at 1-pi i Does not generate a new message.
Preferably, the edge node assigns different priorities to the newly injected message according to the message urgency:
analyzing all possible edge computing request services according to a specific scene, and classifying according to the emergency degree of the services; for each round of generated new messages, the decider of the terminal node assigns an integer of 1-K as a priority according to the type, and the larger the number is, the more urgent the message is, the faster the message should be processed by the edge device.
Preferably, a random algorithm is adopted between the terminal nodes and the edge nodes for communication scheduling, the terminal nodes can set the transmission probability of the current round according to the reference probability obtained in each round, and meanwhile, the edge nodes can dynamically adjust the reference probability according to the channel state of the current round so as to improve the channel utilization rate, specifically:
s41, broadcasting reference probability p by edge nodes e The terminal node is set according to the probabilitySetting own transmission probability: each edge node i maintains a message queue Q i It records all the unsuccessfully transmitted messages of the edge node i; the edge node i sets the transmission probability according to the obtained reference probability
Wherein K i Represents Q i The sum of the priorities of all messages; edge node i according to Q i Selecting a message to be transmitted according to the priority ratio of each message;
s42, dynamically adjusting the reference probability p by the edge node according to the channel state sensed in the transmission stage and the binary exponential backoff method e (ii) a The edge node can observe the result of each round of transmission, if at least one terminal node selects transmission in the round, the edge node is set
Otherwise, if no terminal node selects transmission in the current round, p is set e ＝p e *2；
S43, if the edge node successfully receives the message from the terminal node in the transmission stage, a corresponding ACK signal is generated to feed back the received message, and if the terminal node receives the feedback signal of the transmitted message, the terminal node can know that the transmission is successful and remove the successfully transmitted message from the message queue.
Preferably, the SINR model is used to characterize the interference of the radio channel, and in particular, for the transmitting node u and the receiving node v,
Signal(u,v)＝P u ·d(b,v) -α
where Signal (u, v) is the Signal strength experienced by node v from node u, P u RepresentTransmission power of node u, d (u, v) is the Euclidean distance between u and v, and α ∈ (2, 6)]Representative path loss index, determined by the transmission medium and other factors in the environment; the Signal from u to v, whose strength Signal (u, v) weakens with distance, Σ when v tries to decode the Signal from u w∈S \ { u } Signal (w, v) + N is interference, where S is a node set transmitted simultaneously with u and N is environmental noise, and when SINR (u, v) ≧ beta, we call the transmission of u to v successful, where beta ≧ 1 is the threshold, and is determined by hardware.
Preferably, both the terminal device and the edge device have a half-duplex wireless signal transmitter, that is, each round can only select one of monitoring a channel or transmitting; if the selection of the listening channel is chosen, the node can determine three states of the channel according to the sensed signal strength and the received information, namely:
state one, channel idle: no node selects transmission in the current round;
state two, successful transmission: only one node selects transmission in the round, and all nodes monitoring the channel receive the transmitted information;
state three, channel busy: two or more nodes transmit at the same time, conflict is generated, and the node monitoring the channel cannot receive the transmitted information at the moment;
in a single-hop network, the channel states observed by all nodes are consistent, and if the nodes select to send information, the information to be sent is transmitted at a fixed power P.
Preferably, in step S5,
feeding back the message, if the edge node successfully receives the message from the terminal node, broadcasting an ACK signal to feed back the message; in this phase, the edge node i selects the listening channel, and if it has successfully received feedback on the transmitted message, indicating that the message was successfully transmitted, it can remove the message from the message queue and update the sum K of the priority levels of the message i (ii) a If a new message is generated, it is added to the message queue and K is updated i 。
Preferably, for a message with priority k, the weighted information age a (t) in the t-th round is:
advantageous effects
The invention adopts a binary exponential backoff method to realize a scheduling method between messages with different priorities in an edge calculation scene, the method utilizes a random algorithm to transmit the messages, dynamically adjusts the transmission probability according to the channel state, solves the problems of competition and interference in wireless communication, considers the real scene of edge calculation application, not only ensures the fairness of the utilization of the channels between the messages with different priorities, but also ensures the communication efficiency with the optimal constant.
Drawings
FIG. 1 is a diagram of an edge computation scenario in an embodiment of the invention;
FIG. 2 is a pseudo-code description of an information priority based distributed scheduling algorithm in an embodiment of the present invention;
fig. 3 is an experimental result display of a distributed scheduling algorithm based on information priority in the embodiment of the present invention.
Figure 4 is a graph shown for the results of an age experiment for maximum weighted information.
Detailed Description
In order to facilitate an understanding of the invention, reference will now be made in detail to the present embodiments of the invention, examples of which are illustrated in the accompanying drawings.
FIG. 1 is a schematic diagram of an edge calculation scenario according to the present invention. Four common scenarios for requesting edge computing services are given in the figure, which are: intelligent transportation, mobile device, security system, intelligent workshop. These end nodes will generate various requirements according to the situation and will be given specific priority by the corresponding decision procedures. For example, a car accident occurred in scene one, the camera responsible for monitoring generated a message with priority of 100. The terminal nodes and the edge nodes are communicated by adopting a wireless network, and the distributed edge computing information scheduling algorithm proposed by the inventor is operated in the wireless network. It is worth noting that in the scheduling process, the algorithm of the application can ensure that the priority of the information is only known by the edge node which possesses the information and is not known by other edge nodes and terminal nodes, thereby realizing privacy protection. The algorithm of the application can ensure that nodes with higher priority are more likely to arrive at edge nodes first, so as to ensure the fairness among information with different priorities. After the message reaches the edge node, the message is processed according to different edge calculation scenarios, for example, the first application in fig. 1 responsible for handling intelligent traffic may perform various operations after receiving the message according to the content of the message, such as notifying a nearby traffic police to handle an accident.
Specifically, the step of constructing the edge calculation scene according to the present invention includes:
s1, constructing a distributed communication scene;
s2, constructing a dynamic network topological structure and an information injection model, and endowing different priorities for the injected information by the terminal node according to the emergency degree of the information;
(1) And constructing a dynamic network topology model: 1 fixed-location edge node is constructed, equipped with a high-performance computing server and a half-duplex signal transmitter, which can be responsible for handling various edge computing requests. And constructing n terminal nodes, wherein each terminal node is responsible for monitoring the respective responsible environment and possibly generating various information for requesting edge computing service. The end nodes may be fixed in location or may be arbitrarily moved within the communication range of the edge nodes. This means that the network topology can be changed in real time, which is more consistent with the actual situation of the internet of vehicles, unmanned aerial vehicles, etc. The edge nodes and the terminal nodes both adopt a half-duplex mode for communication, which means that the nodes can only select one of information transmission or channel monitoring at each time, and cannot transmit while listening.
And analyzing all possible edge computing request services according to a specific scene, and classifying according to the emergency degree. Each terminal node is provided with a judgment program, each request is endowed with an integer of 1-K as priority, and the larger the number is, the tighter the message isAnd (4) emergency treatment. While each end node i maintains a message queue Q i For storing all requests that are not responded to. If the message queue is empty, a message with the priority level of 1 is generated and added into the message queue, and the message is used for informing the edge node that the condition is normal. When an emergency occurs, the message is removed.
(2) And a physical interference model: it is considered that in a wireless channel, the strength of a signal becomes weaker with increasing distance, and the strength of a plurality of signals causes superposition of the signals at the receiving place. Therefore, the invention adopts the SINR model to depict the interference situation of the wireless channel. Compared with the traditional graph interference model, the method better describes the fading characteristic and the superposition characteristic of the wireless channel. Specifically, for the transmitting node u and the receiving node v, we have:
Signal(u,v)＝P u ·d(b,v) -α
in the above equation, signal (u, v) is the Signal strength perceived by node v from node u. P u Represents the transmission power of node u, d (u, v) is the Euclidean distance between u and v, and α ∈ (2, 6)]Representing the path loss index, is determined by the transmission medium and other factors in the environment. The Signal from u to v has a strength Signal (u, v) that weakens with distance. When v attempts to decode the signal from u, Σ w∈S\{u} Signal (w, v) + N is interference, where S is a node set transmitting simultaneously with u and N is environmental noise determined by the environment, when SINR (u, v) ≧ β, we call the transmission from u to v successful, where β ≧ 1 is a threshold, and is determined by hardware.
Both the end device and the edge device have a half-duplex radio signal transmitter, which means that the node can only select one of listening to the channel or transmitting on each round. If the selection of the listening channel is chosen, the node can determine three states of the channel according to the sensed signal strength and the received information, namely:
state one, channel idle: no node selects transmission in this round.
State two, successful transmission: only one node selects transmission in the round, and all nodes monitoring the channel receive the transmitted information.
And a third state, namely the channel is busy: two or more nodes transmit simultaneously, and conflict occurs, and the node monitoring the channel can not receive the transmitted information.
In a single hop network, the channel conditions observed by all nodes are consistent. If the node selects to send the information, the message to be sent is transmitted with fixed power P.
(3) And (3) an information injection model, wherein in each time round t, each terminal node generates a new message according to a specific probability to simulate the dynamics generated by the information in the actual edge calculation scene. For example: for a particular edge node i, will be at π i Generating a new message; or in the range of 1-pi i Does not generate a new message.
(4) And a message feedback mechanism: after the communication process of each round is finished, if the edge node successfully receives the message from the terminal node, an ACK signal is broadcasted to feed back the message. The terminal node receiving the corresponding message feedback may determine that the transmitted message was successfully accepted and remove the message from the message queue.
S3, broadcasting the reference probability of the current round by the edge node, and setting the transmission probability of the current round by the terminal node according to the reference probability;
s4, the terminal equipment randomly selects a message to be sent from the message queue according to the proportion of the message priority, the message is transmitted according to the transmission probability of the current round obtained in the S3 in the transmission stage, and meanwhile, the edge node monitors a channel and dynamically adjusts the reference probability of the next round according to the current channel state;
fig. 2 is a pseudo-code description of an information priority based distributed scheduling algorithm. As shown in fig. 2, the communication process of the distributed scheduling algorithm based on information priority includes three stages:
stage 1: broadcasting reference probabilities, in which the edge node broadcasts the round of transmissions at power PReference probability p e All terminal nodes select the monitoring channel. Because no interference influence exists at this time, all terminal nodes within the communication radius of the edge node can be ensured to receive the reference probability p e 。
And (2) stage: and transmitting the message, wherein the edge node selects a monitoring channel. The edge node i first randomly selects a message to be sent according to the priority ratio of the information in the message queue, for example, three messages with the priority of 1, 2 and 3 in the message queue are respectively selected according to the probability
And (6) selecting. Based on received reference probability p e Edge node i sets the transmission probability of the current round
In the transmission phase, the node has a probability p i Sending the selected message in the current round with the probability of 1-p i The silence is maintained.
And S5, the edge node feeds back the information successfully received in the transmission stage.
And (4) feeding back the message, and broadcasting an ACK signal to feed back the message if the edge node successfully receives the message from the terminal node in the stage 2. During this phase, the terminal node i selects the listening channel, and if it has successfully received feedback on the transmitted message, indicating that the message was successfully transmitted, it can remove the message from the message queue and update the sum K of the priority levels of the message i . If a new message is generated, it is added to the message queue and K is updated i 。
Fig. 3 is an experimental result display of a distributed scheduling algorithm based on information priority in the embodiment of the present invention.
To better demonstrate the effectiveness of the invention, the experiment was described using the concept of the age of entitled information (WAOI), which well describes the time required for the information to be successfully transmitted from its generation. Defined as, for a message with priority k, its weighted information age a (t) at round t is:
fig. 3 and 4 show the variation of the average weighted information age and the maximum weighted information age with the number of turns and the new information arrival rate ζ (i.e., the sum of all the node arrival rates) in the case where the node number n =2000, 3000, 4000, 5000. Where fig. 3 primarily considers the average latency required from generation to acceptance by the edge node for all information, and fig. 4 primarily considers the longest latency required from generation to acceptance by the edge node for all information. It can be seen that the algorithm can remain stable when ζ does not exceed 0.09. Also, it can be shown that for a message with priority K, the expected number of waiting rounds is O (Kn/K), where K is the maximum value of priority. Obviously, when the priority k is larger, the expected number of waiting rounds is smaller, and the fairness of the algorithm is reflected. Meanwhile, the algorithm of the application can ensure that the throughput of the channel is constant in a sufficiently long period, namely the efficiency constant of the algorithm of the application is approximately optimal.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (9)
1. A distributed edge computing information scheduling method based on information priority is characterized by comprising the following steps:
s1, constructing a distributed communication scene;
s2, constructing a dynamic network topological structure and an information injection model, and endowing different priorities for the injected information by the terminal node according to the emergency degree of the information;
s3, broadcasting the reference probability of the current round by the edge node, and setting the transmission probability of the current round by the terminal node according to the reference probability;
s4, the terminal equipment randomly selects a message to be sent from the message queue according to the proportion of the message priority, the message is transmitted according to the transmission probability of the current round obtained in the S3 in the transmission stage, and meanwhile, the edge node monitors the channel and dynamically adjusts the reference probability of the next round according to the current channel state;
and S5, the edge node feeds back the information successfully received in the transmission stage.
2. The method for dispatching information based on distributed edge computing of information priority according to claim 1, wherein the step S2 of constructing a dynamic network topology model comprises the following steps,
constructing 1 edge node with fixed position, which is equipped with server and half-duplex signal transmitter and is responsible for processing various edge calculation requests;
constructing n terminal nodes, wherein each terminal node is responsible for monitoring the environment in charge of the terminal node and possibly generating various information requesting edge computing service; the terminal nodes are fixed positions or can move freely in the communication range of the edge nodes; the edge nodes and the terminal nodes both adopt a half-duplex mode for communication, and the nodes can only select one of the sending information or the monitoring channel at each moment and cannot send while monitoring.
3. The method according to claim 1, wherein the step S2 of constructing the information injection model comprises the steps of generating a new message by each terminal node according to a specific probability in each time round t to simulate the dynamics of information generation in an actual edge calculation scene, wherein pi is given to a specific edge node i i Generating a new message; or at 1-pi i Does not generate a new message.
4. The method according to claim 1, wherein the edge node assigns different priorities to the newly injected message according to the urgency of the message:
analyzing all possible edge computing request services according to a specific scene, and classifying according to the emergency degree of the services; for each round of generated new messages, the decider of the terminal node assigns an integer of 1-K as priority to the new messages according to the type, wherein the larger the number is, the more urgent the message is, and the message should be processed by the edge device more quickly.
5. The method according to claim 1, wherein a random algorithm is used between the terminal nodes and the edge nodes for communication scheduling, the terminal nodes set the transmission probability of the current round according to the reference probability obtained in each round, and the edge nodes dynamically adjust the reference probability according to the channel state of the current round so as to improve the channel utilization ratio, specifically:
s41, broadcasting reference probability p by edge nodes e And the terminal node sets the transmission probability of the terminal node according to the probability: each edge node i maintains a message queue Q i It records all the unsuccessfully transmitted messages of the edge node i; the edge node i sets the transmission probability according to the obtained reference probability
Wherein K is i Represents Q i The sum of the priorities of all messages; edge node i is according to Q i Selecting a message to be transmitted according to the priority ratio of each message;
s42, the edge node dynamically adjusts the reference probability p according to the channel state sensed in the transmission stage and the method of backward movement of the binary exponential e (ii) a The edge node can observe the result of each round of transmission, if at least one terminal node selects transmission in the round, the edge node is set
Otherwise, if no terminal node selects transmission in the current round, p is set e ＝p e *2；
S43, if the edge node successfully receives the message from the terminal node in the transmission stage, a corresponding ACK signal is generated to feed back the received message, and if the terminal node receives the feedback signal of the transmitted message, the terminal node can know that the transmission is successful and remove the successfully transmitted message from the message queue.
6. The method of claim 1, wherein the SINR model is used to characterize the interference of wireless channel, specifically, for the transmitting node u and the receiving node v,
Signal(u,v)＝P u ·d(b,v) -α
where Signal (u, v) is the Signal strength experienced by node v from node u, P u Represents the transmission power of node u, d (u, v) is the Euclidean distance between u and v, and α ∈ (2, 6)]Representative path loss index, determined by the transmission medium and other factors in the environment; the Signal from u to v has a strength Signal (u, v) that weakens with distance, and when v attempts to decode the Signal from u, Σ w∈S\{u} Sign (w, v) + N is interference, where S is the node set transmitting simultaneously with u and N is ambient noise, when SINR (u, v) ≧ β, which is greater than or equal to 1 as the threshold, we call the transmission of u to v successful, and it is determined by hardware.
7. The method of claim 2, wherein the terminal device and the edge device have a half-duplex wireless signal transmitter, that is, each round can only select one of listening to a channel or transmitting; if the node chooses to listen to the channel, the node can determine three states of the channel according to the sensed signal strength and the received information, namely:
state one, channel idle: no node selects transmission in the current round;
state two, successful transmission: only one node selects transmission in the round, and all nodes monitoring the channel can receive the transmitted information;
and a third state, namely the channel is busy: two or more nodes transmit simultaneously to generate conflict, and the node monitoring the channel can not receive the transmitted information;
in a single-hop network, the channel states observed by all nodes are consistent, and if the nodes select to send information, the information to be sent is transmitted at a fixed power P.
8. The information scheduling method of claim 1, wherein in step S5,
the feedback of the message is carried out, if the edge node successfully receives the message from the terminal node, an ACK signal is broadcasted to carry out the feedback; in this phase, the edge node i selects the listening channel, and if it has successfully received feedback on the transmitted message, indicating that the message was successfully transmitted, it can remove the message from the message queue and update the sum K of the priority levels of the message i (ii) a If a new message is generated, it is added to the message queue and K is updated i 。
9. The information scheduling method of claim 1, wherein for a message with a priority of k, its weighted information age a (t) in the t-th round is:
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