CN114675845A - Information age optimization method and device, computer equipment and storage medium - Google Patents
Information age optimization method and device, computer equipment and storage medium Download PDFInfo
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- CN114675845A CN114675845A CN202210323496.0A CN202210323496A CN114675845A CN 114675845 A CN114675845 A CN 114675845A CN 202210323496 A CN202210323496 A CN 202210323496A CN 114675845 A CN114675845 A CN 114675845A
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Abstract
The application belongs to the field of communication, and relates to an information age optimization method, an information age optimization device, computer equipment and a storage medium, wherein the method comprises the following steps: performing a first round of pre-deployment on the service function diagram; judging whether the current information age of each device is lower than the preset maximum tolerated information age of the device or not according to the first round of pre-deployment condition; the equipment which meets the maximum tolerance information age is not processed, and the sampling frequency of the equipment which does not meet the maximum tolerance information age is adjusted by adopting a sampling frequency adjustment algorithm; and carrying out second-round pre-deployment on the service function diagram, and adjusting the sampling frequency of the equipment which does not meet the conditions until the information ages of all the equipment meet the maximum tolerance information age. The distributed deployment method of the service graph is combined with the sampling frequency adjustment algorithm, the information age of the equipment is reduced, and the fine perception degree of data is improved while the information timeliness is improved.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for optimizing information age, a computer device, and a storage medium.
Background
The mass equipment needs to be automated and intelligentized. If the equipment is monitored in real time so as to achieve predictive maintenance, the equipment can be operated efficiently, safely, reliably and at low cost. The method is used for monitoring equipment, and is not independent of an Industrial Control System (ICS), wherein the ICS is a computer-based System and is widely applied to national key infrastructure industries such as energy, traffic, water conservancy, security, food, large-scale manufacturing and the like. The ICS architecture shown in FIG. 1 provides a good reference for the construction of ICS in various industries.
For a monitoring system, timeliness of equipment information is an important performance index, and if an information destination end receives outdated information, accuracy and reliability of system decision may be reduced, and huge potential safety hazards are caused. An important index for measuring the timeliness Of the Information is the Age (AoI), i.e. the time elapsed from the generation Of the latest received data packet to the reception thereof. After data is generated, the obsolescence degree of data information is increased along with the unidirectional passing of time and is aged continuously. In addition, in order to implement accurate decision and control, the monitoring system requires to implement refined sensing on data, that is, refined sampling on data.
In order to realize real-time fine perception of data, the existing method generally expects an information acquisition node to acquire information as fast as possible, that is, a higher sampling frequency is set. Due to limited network resources, when the node performs information acquisition at a faster rate, the network load is large, even the network is congested, and the data packet cannot effectively reach the destination node.
Disclosure of Invention
An object of the embodiments of the present application is to provide an information age optimization method, an information age optimization device, a computer device, and a storage medium, so as to solve the problem in the prior art that network resources are limited, and when a node performs information acquisition at a fast rate, a network load is large, even a network congestion is caused, and a data packet cannot effectively reach a destination node.
In order to solve the above technical problem, the present application provides an information age optimization method, which adopts the following technical scheme, including the following steps:
performing first-round pre-deployment on the service function diagram;
judging whether the current information age of each device is lower than the preset maximum tolerance information age of the device or not according to the first round of pre-deployment condition;
the equipment which meets the maximum tolerance information age is not processed, and the sampling frequency of the equipment which does not meet the maximum tolerance information age is adjusted by adopting a sampling frequency adjustment algorithm;
and carrying out second-round pre-deployment on the service function diagram, and adjusting the sampling frequency of the equipment which does not meet the conditions until the information ages of all the equipment meet the maximum tolerance information age.
Further, the step of performing the first round of pre-deployment on the service function diagram specifically includes:
the equipment makes a deployment request to a server;
and the server determines whether to accept the deployment request from the equipment or not according to the income condition.
Further, the step of determining whether the current information age of each device is lower than a preset maximum tolerated information age of the device according to the first round of pre-deployment condition specifically includes:
presetting maximum tolerance information age for each device);
each device is according to the formula
Calculating the age of the information, where T represents the sampling frequency, TdRepresents the time that the data packet passes from generation to transmission to the destination, i.e. the service delay in the network;
and comparing the calculated information age with the maximum tolerance information age, and judging whether the current information age of each device is lower than the preset maximum tolerance information age of the device.
Further, the step of adjusting the sampling frequency of the device which meets the maximum tolerance information age by using a sampling frequency adjustment algorithm includes:
finding out equipment with the information age not meeting the maximum tolerance information age;
for the equipment which does not meet the maximum tolerance information age, a sampling frequency adjusting algorithm is called to adjust,
further, performing a second round of pre-deployment on the parallelization service chain, and adjusting the sampling frequency of the devices which do not meet the condition until the information ages of all the devices meet the maximum tolerance information age.
S41, respectively calculating the deployment schemes of the SFGs, and knowing the requirement release condition of the SFGs on the network resources;
s42, adjusting the sampling frequency of the AoI equipment which does not meet the maximum tolerance, and randomly adjusting the sampling frequency or reducing the sampling frequency by 5-15% until all the equipment meet the requirement of the maximum tolerance AoI in an ideal network environment;
s43, if the information age of the device is adjusted for the first time, executing step S44, otherwise, if the device is judged to be the first time, the maximum tolerance AoI is not satisfied, executing step S42, if the device information age is such that the second occurrence does not meet the maximum tolerance AoI, then a AoI for the current round may be greater or less than AoI for the previous round, and for devices greater than AoI, the sampling frequency of the device is inversely operated with the originally performed regulation rule on the basis of the initial sampling frequency, namely, the previous round is up-regulated, the current round is down-regulated, the previous round is down-regulated, the current round is up-regulated, for less than the previous round AoI, the same adjustment rules are performed for the previous round, if the device does not meet the maximum tolerance AoI for the third or more occurrence, then the sampling frequency of the partial wheels AoI is adjusted according to three conditions, namely, the partial wheels are all larger than AoI of the front two wheels, are all smaller than AoI of the front two wheels and are between AoI of the front two wheels;
s44, deploying all SFGs by adopting a distributed game matching algorithm under the condition of a given sampling frequency;
s45, recalculating average AoI of each device;
s46, if the average AoI is improved compared with the arrangement of the previous round, continuously adjusting AoI the maximum sampling frequency of the equipment, and repeating the steps S43-S45, otherwise, terminating the algorithm.
Further, the step of adjusting the sampling frequency in the case that all of the partial wheels AoI are larger than AoI of the front two wheels, all of the partial wheels are smaller than AoI of the front two wheels, and the partial wheels are located between AoI of the front two wheels specifically includes:
if the AoI of the current round is larger than AoI of the previous two rounds, setting the sampling frequency of the next round by adopting a bisection method, taking the sampling frequency of the previous round as a terminal point, taking the sampling frequency of the previous round as a starting point, carrying out bisection, and determining the sampling frequency of the next round;
if the AoI rounds are all smaller than AoI of the previous two rounds, executing the same sampling rule of the previous round and determining the sampling frequency of the next round;
if the current round AoI is between AoI of the previous two rounds, the next round of sampling frequency is determined by dividing by two with the larger sampling frequency of the current round and the upper round as the end point and the smaller sampling frequency as the starting point.
In order to solve the above technical problem, the present application further provides an information age optimizing apparatus, which adopts the following technical scheme, including:
the pre-deployment module is used for carrying out first-round pre-deployment on the service function graph;
the judging module is used for judging whether the current information age of each device is lower than the preset maximum tolerated information age of the device or not according to the first round of pre-deployment condition;
the adjusting module is used for not processing the equipment which meets the maximum tolerance information age, and adjusting the sampling frequency of the equipment which does not meet the maximum tolerance information age by adopting a sampling frequency adjusting algorithm;
and the redeployment module is used for carrying out second-round redeployment on the service function diagram, and adjusting the sampling frequency of the equipment which does not meet the conditions until the information ages of all the equipment meet the maximum tolerance information age. Further, the deployment module includes:
the request module is used for making a deployment request to the server by the equipment;
and the response module is used for determining whether to accept the deployment request from the equipment or not by the server according to the income condition.
In order to solve the above technical problem, the present application further provides a computer device, which adopts the following technical solution, and includes a memory and a processor, where the memory stores computer readable instructions, and the processor implements the steps of the information age optimization method when executing the computer readable instructions.
In order to solve the above technical problem, the present application further provides a computer-readable storage medium, which adopts the following technical solution, and the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions, when executed by a processor, implement the steps of the information age optimization method described above.
Compared with the prior art, the application mainly has the following beneficial effects: the method comprises the steps that through multi-round pre-deployment on a service function diagram, for the sampling frequency of equipment which does not meet the maximum tolerance information age, a sampling frequency adjusting algorithm is adopted for adjustment until the information ages of all the equipment meet the maximum tolerance information age; the deployment process of the service graph is introduced, so that the processing time delay of the information is reduced, the service time delay of the information in the edge network is also reduced, and the information age of the equipment is reduced; the sampling frequency is adjusted by adopting a sampling frequency adjustment algorithm and a dichotomy, so that the average sampling frequency is reduced, and the refinement degree of data is improved; the distributed deployment method of the service graph is combined with the sampling frequency adjustment algorithm, the information age of the equipment is reduced, and the fine perception degree of data is improved while the information timeliness is improved.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1is a schematic diagram of a prior art industrial control system architecture;
FIG. 2 is an exemplary system architecture diagram to which the present application may be applied;
FIG. 3 is a flow diagram of one embodiment of an information age optimization method of the present application;
FIG. 4 is a Service Function Graph (SFG) of the present application;
FIG. 5 is a graph of sampling frequency versus average information age for the present application;
FIG. 6 is a schematic diagram of the device information of the present application older than the first two rounds;
FIG. 7 is a schematic diagram of the device information of the present application all being younger than the first two rounds;
FIG. 8 is a schematic diagram of the device information of the present application aged between the first two rounds;
FIG. 9 is a computing framework diagram of the present application;
FIG. 10 is a flow chart of a concurrent services graph distributed deployment and sampling frequency adjustment joint algorithm of the present application;
fig. 11 is a schematic structural diagram of an embodiment of the information age optimizing apparatus of the present application;
FIG. 12 is a block diagram of one embodiment of a computer device of the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 2, the system architecture 100 may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium of communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the first terminal device 101, the second terminal device 102, the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like, may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.
The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, motion Picture Experts compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, motion Picture Experts compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, such as a background server that provides support for pages displayed on the first terminal apparatus 101, the second terminal apparatus 102, and the third terminal apparatus 103.
It should be noted that the information age optimization method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the information age optimization apparatus is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Example one
With continuing reference to FIG. 3, a flow diagram of one embodiment of the information age optimization method of the present application is shown. The information age optimization method comprises the following steps:
and step S1, performing a first round of pre-deployment on the service function diagram.
In this embodiment, step S1 further includes the steps of:
the equipment makes a deployment request to a server;
and the server determines whether to accept the deployment request from the equipment or not according to the income condition.
The first pre-deployment of the service function diagram is completed before the formal work of the equipment. Before the equipment works formally, the sampling frequency is continuously adjusted by using the information age optimization method, and finally the sampling frequency of each equipment is determined, so that the equipment starts formally working.
Fig. 4 is a service function diagram (SFG) of the present application. As shown in fig. 4, each device has a preference list for the general-purpose servers, i.e., which general-purpose server is willing to be preferentially deployed and which server is the last choice. Meanwhile, each server has different profits for receiving the deployment request of the equipment, and also has a preference list of the equipment, which is used for evaluating the profits of the deployment request applied later and whether the deployed request is rejected, and finally, the deployment of all the equipment is finished.
The gateway w will connect with different general servers, i.e. access the network from different places, so the distance to other general servers is different and the time delay is also different. The shortest distance between the general server accessed by the gateway w and other general servers in the network is obtained through a Floyd algorithm, the shortest distance is stored into a list, then ascending sequencing is carried out, and the sequenced list is the preference list of the equipment.
The universal server evaluates the applications selected by the equipment, and performs priority ranking on the SFGs used by the applications, wherein the time delay requirement is high, the priority is high, the time delay requirement is not so high, and the priority is relatively later, so that the universal server preferentially deploys VNFs in the SFGs with high priorities, ensures that data packets of the applications with high time delay requirements can be preferentially processed, and meets the requirements of the equipment. The universal server preference list is a list formed by sorting the priorities.
The process of requesting deployment at the device side is as follows: firstly, each device sends a deployment request to the first general server in the preference list of the device, when the server is satisfied and the deployment request is refused, the device deployed in the current round is abandoned, and in the next round of deployment, the target server is the next server in the preference list of the device.
The process of requesting deployment at the server side is as follows: when a deployment request from a device end is received, the request can be processed in two modes according to the resource condition of the universal server, and when the universal server is not fully loaded, namely the resource required by the instantiation request can be met, the deployment request can be directly accepted; when the universal server is fully loaded, namely resources required by instantiation of the request cannot be met, the request is processed, and when the request VNF has a higher priority than the deployed part or all of the requests, the request with the lowest priority in the deployed requests is rejected and the current deployment request is accepted; when the request VNF (virtual network feature virtual network function) has a lower priority than all deployed requests, the current request will be directly rejected. Whether to queue or not is selected by the device.
And repeating the matching of the equipment end and the server end until the deployment requests of all the equipment are accepted.
In this embodiment, the electronic device (for example, the server/terminal device shown in fig. 1) on which the information age optimization method operates may receive the information age optimization request through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G/5G connection, a WiFi connection, a bluetooth connection, a wimax x connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
And step S2, judging whether the current information age of each device is lower than the preset maximum tolerated information age of the device according to the first round of pre-deployment condition.
In this embodiment, step S2 further includes the steps of:
presetting maximum tolerance information age for each device);
each device is according to the formula
Calculating the age of the information, where T represents the sampling frequency, TdRepresents the time elapsed from generation to transmission of the data packet to the destination, i.e. the service delay in the network;
and comparing the calculated information age with the maximum tolerance information age, and judging whether the current information age of each device is lower than the preset maximum tolerance information age of the device.
And step S3, not processing the equipment meeting the maximum tolerance information age, and adjusting the sampling frequency of the equipment not meeting the maximum tolerance information age by adopting a sampling frequency adjustment algorithm.
In this embodiment, step S3 further includes the steps of:
finding out equipment with the information age not meeting the maximum tolerance information age;
and for the equipment which does not meet the maximum tolerance information age, calling a sampling frequency adjustment algorithm for adjustment. The sampling frequency adjustment can be composed of a plurality of rounds, for a device, the third time and the above times of occurrence do not meet the maximum tolerance information age, the whole device has undergone frequency adjustment at least twice, and the adjustment of the third time frequency needs to determine the adjustment strategy of the next round by taking the influence of the adjustment of the sampling frequency of the previous two rounds on the information age into consideration.
And step S4, performing a second round of pre-deployment on the parallelization service chain, and adjusting the sampling frequency of the devices which do not meet the conditions until the information ages of all the devices meet the maximum tolerance information age.
Fig. 5 is a graph of the sampling frequency versus the average information age of the present application. Firstly, considering that the performance of the equipment is limited and the processing efficiency of the information is relatively low, the data is unloaded to an Industrial Edge Computing (IEC) network for computing processing, and the processing efficiency is improved. In IEC, Network Function Virtualization (NFV) technology decouples Network Functions of data processing from proprietary hardware, which improves flexibility of Network management, and service delay of data in a Network can be reduced by distributed deployment of Service Function Graphs (SFGs) in NFV, thereby degrading information age. As for the sampling frequency, the relation between the sampling frequency and the average information age is shown in fig. 5, which is a parabolic relation, when the sampling frequency is higher, a large number of data packets will be transmitted in one sampling period, when a large number of devices transmit a large number of data packets simultaneously, the processing performance of the network server node may be reduced, and the data packets which are not processed in time will be queued for processing, so that the queuing delay may increase AoI. When the sampling frequency is small, the sampling period is large, and the receiving end needs to wait for a long time to receive a new data packet. During the wait, AoI for old data will continue to grow.
The sampling frequency adjustment process is as follows:
(1) firstly, in an ideal network environment, the deployment schemes of the SFGs are calculated respectively, so as to know the requirement and the release conditions of the SFGs on network resources.
(2) The sampling frequency of the device which does not meet the maximum tolerance AoI is adjusted, and the sampling frequency is randomly adjusted up or reduced by 5% -15% according to a certain probability, wherein the sampling frequency is 10% until all the devices can meet the requirement of the maximum tolerance AoI in an ideal network environment.
(3) If the adjustment is carried out for the first time, jumping to (4), otherwise, executing the following adjustment:
a) the device does not meet the maximum tolerant AoI case for the first occurrence: the adjustment rule of (2) is executed, i.e. random up-regulation or down-regulation by 5% to 15%, here 10%.
b) The device does not meet the maximum tolerance AoI case for the second occurrence: then AoI is the case when AoI of the current round is greater or less than AoI of the previous round, and for a device greater than AoI of the previous round, the sampling frequency of the device will be reversed from the original adjustment rule based on the initial sampling frequency, i.e., the previous round is up-adjusted, the current round is down-adjusted, the previous round is down-adjusted, and the current round is up-adjusted. For less than the previous pass AoI, the same adjustment rule is performed for the previous pass.
c) The device does not meet the maximum tolerance AoI condition for the third or more occurrences: AoI appears in three cases, all being greater than AoI for the first two wheels and all being less than AoI for the first two wheels, between AoI for the first two wheels.
For the case that the data is larger than the first two rounds, as shown in fig. 6, fig. 6 is a schematic diagram that the device information age of the present application is larger than the first two rounds, a dichotomy is adopted to set the sampling frequency of the next round, the sampling frequency of the previous round is used as an end point, the sampling frequency of the previous round is used as a starting point, and the dichotomy is performed to determine the sampling frequency of the next round.
For the case that all the data are less than the data, as shown in fig. 7, fig. 7 is a schematic diagram that the device information ages of the present application are less than the data of the previous two rounds, the same sampling rule of the previous round is executed, and the sampling frequency of the next round is determined.
For the case between the two, as shown in fig. 8, fig. 8 is a schematic diagram of the device information age of the present application between the first two rounds. And (3) also adopting a bisection method, taking the larger sampling frequency of the current round and the upper round as a terminal point and the smaller sampling frequency as a starting point, and carrying out bisection to determine the sampling frequency of the next round.
(4) Next, all SFGs are deployed using a distributed game matching algorithm at a given sampling frequency.
(5) The average AoI for each device is recalculated.
(6) If the average AoI is an improvement over the previous round of deployment, continue adjusting AoI the sampling frequency of the largest device and repeat (3) - (5); otherwise the algorithm is terminated.
FIG. 9 is a computing framework flow diagram of the present application. As shown in fig. 9, the SFG is deployed first, the sampling frequency of the device that does not meet the condition is adjusted, and the device is re-deployed according to the new sampling frequency, so as to obtain a satisfactory sampling frequency finally.
Fig. 10 is a flow chart of the SFG and sampling frequency adjustment joint algorithm of the present application, and as shown in fig. 10, the calculating of the final sampling frequency includes the following steps:
inputting: network _ topo, device initial sampling frequency (list _ initial _ T), maximum tolerance of the device AoI (list _ MAX _ AoI).
And (3) outputting: the final sampling frequency of the device (list _ final _ T).
S101, stop < -False, wherein stop is a sampling frequency algorithm stop identification.
S102, calculating and obtaining a preference list, a list _ device _ reference and a list _ server _ reference of the device side and the server side respectively.
The service graph of each device is expected to be deployed on a server with low time delay. The result of the calculations for many servers in the network is in fact the time from the device to the server to the destination. The preference list of the server is that different SFGs will be deployed with different amounts of revenue.
S103, defining a list to store AoI, list _ current _ AoI of each device after SFG deployment.
And S104, while (flag), do// starting to perform cycle operation, and searching for the optimal sampling frequency, wherein the flag refers to a mark for judging whether the information ages of all the devices meet the condition that the information ages are smaller than the maximum tolerance information age.
And S105, deploying the service function diagram.
S106, AoI of each device is calculated and stored in list _ current _ AoI.
Each element in S107 and if list _ current _ AoI is less than or equal to the corresponding element, do, in 1ist _ MAX _ AoI.
list _ current _ AoI stores the value of the current information age of each device, and an element refers to the value of the information age.
S108, assigning the sampling frequency at the moment to 1ist _ final _ T and outputting.
S109、End。
S110、E1se。
And S111, adjusting the devices which do not meet the AoI according to the adjustment rule of the sampling frequency.
S112, continuing to execute the steps S105 to S111.
Therefore, in the existing method, an optimal information updating period is obtained by solving an optimization problem which takes the minimum average information age or the peak information age as an objective function. In order to ensure the timeliness of the information, the current method minimizes the average AoI by optimizing the scheduling policy of the node queue, and the method based on the queue scheduling policy optimization aims to find an optimal information scheduling policy, reduce the queuing delay of data, and thus reduce the service delay, so as to minimize the average information age or the peak information age. In addition, the sampling frequency can also have a significant impact on the size of AoI. Therefore, to achieve a reduction AoI and an increase in the perception of data refinement, a comprehensive consideration of the data transmission process and the data sampling process is required.
By adopting the embodiment, the service function diagram is subjected to multi-round pre-deployment, and the sampling frequency of the equipment which does not meet the maximum tolerance information age is adjusted by adopting a sampling frequency adjustment algorithm until the information ages of all the equipment meet the maximum tolerance information age; the deployment process of the service graph is introduced, so that the processing time delay of the information is reduced, the service time delay of the information in the edge network is reduced, and the information age of the equipment is reduced; the sampling frequency is adjusted by adopting a sampling frequency adjustment algorithm and a dichotomy, so that the average sampling frequency is reduced, and the refinement degree of data is improved; the distributed deployment method of the service graph is combined with the sampling frequency adjustment algorithm, the information age of the equipment is reduced, and the fine perception degree of data is improved while the information timeliness is improved.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The construction of the smart city can not be developed in industrialization, so that the method can be applied to the field of construction of the smart city, and the construction of the smart city is promoted.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
Example two
With further reference to fig. 11, as an implementation of the method shown in fig. 3, the present application provides an embodiment of an information age optimization apparatus, which corresponds to the embodiment of the method shown in fig. 3, and which can be applied to various electronic devices.
As shown in fig. 11, the information age optimizing apparatus 400 according to the present embodiment includes: a pre-deployment module 401, a determination module 402, an adjustment module 403, and a redeployment module 404. Wherein:
a pre-deployment module 401, configured to perform a first round of pre-deployment on the service function graph;
a judging module 402, configured to judge whether a current information age of each device is lower than a preset maximum tolerated information age of the device according to a first round of pre-deployment condition;
the adjusting module 403 is configured to not process the device that meets the maximum tolerance information age, and adjust the sampling frequency of the device that does not meet the maximum tolerance information age by using a sampling frequency adjustment algorithm;
and the redeployment module 404 is configured to perform a second round of redeployment on the service function graph, and adjust the sampling frequency of the devices that do not meet the condition until the information ages of all the devices meet the maximum tolerance information age.
In some optional implementations of this embodiment, the deployment module 404 further includes:
the request module is used for making a deployment request to the server by the equipment;
and the response module is used for determining whether to accept the deployment request from the equipment or not by the server according to the income condition.
By adopting the embodiment, through carrying out multi-round pre-deployment on the service function diagram, for the sampling frequency of the equipment which does not meet the maximum tolerance information age, the sampling frequency adjustment algorithm is adopted for adjustment until the information ages of all the equipment meet the maximum tolerance information age; the deployment process of the service graph is introduced, so that the processing time delay of the information is reduced, the service time delay of the information in the edge network is also reduced, and the information age of the equipment is reduced; the sampling frequency is adjusted by adopting a sampling frequency adjustment algorithm and a dichotomy, so that the average sampling frequency is reduced, and the refinement degree of data is improved; the distributed deployment method of the service graph is combined with the sampling frequency adjustment algorithm, the information age of the equipment is reduced, and the fine perception degree of data is improved while the information timeliness is improved.
EXAMPLE III
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 6, fig. 6 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only the computer device 6 having the component memory 61, the processor 62 and the network interface 63 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 61 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal storage unit of the computer device 6 and an external storage device thereof. In this embodiment, the memory 61 is generally used for storing an operating system installed in the computer device 6 and various types of application software, such as computer readable instructions of an information age optimization method. Further, the memory 61 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 62 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute computer readable instructions stored in the memory 61 or process data, such as computer readable instructions for executing the information age optimization method.
The network interface 63 may comprise a wireless network interface or a wired network interface, and the network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.
By adopting the embodiment, through carrying out multi-round pre-deployment on the service function diagram, for the sampling frequency of the equipment which does not meet the maximum tolerance information age, the sampling frequency adjustment algorithm is adopted for adjustment until the information ages of all the equipment meet the maximum tolerance information age; the deployment process of the service graph is introduced, so that the processing time delay of the information is reduced, the service time delay of the information in the edge network is reduced, and the information age of the equipment is reduced; the sampling frequency is adjusted by adopting a sampling frequency adjustment algorithm and a dichotomy, so that the average sampling frequency is reduced, and the refinement degree of data is improved; the distributed deployment method of the service graph is combined with the sampling frequency adjustment algorithm, the information age of the equipment is reduced, and the fine perception degree of data is improved while the information timeliness is improved.
Example four
The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the information age optimization method as described above.
By adopting the embodiment, through carrying out multi-round pre-deployment on the service function diagram, for the sampling frequency of the equipment which does not meet the maximum tolerance information age, the sampling frequency adjustment algorithm is adopted for adjustment until the information ages of all the equipment meet the maximum tolerance information age; the deployment process of the service graph is introduced, so that the processing time delay of the information is reduced, the service time delay of the information in the edge network is reduced, and the information age of the equipment is reduced; the sampling frequency is adjusted by adopting a sampling frequency adjustment algorithm and a dichotomy, so that the average sampling frequency is reduced, and the refinement degree of data is improved; the distributed deployment method of the service graph is combined with the sampling frequency adjustment algorithm, the information age of the equipment is reduced, and the fine perception degree of data is improved while the information timeliness is improved.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. An information age optimization method, characterized by comprising the steps of:
s1, carrying out first-round pre-deployment on the service function diagram;
s2, judging whether the current information age of each device is lower than the preset maximum tolerated information age of the device according to the first round of pre-deployment condition;
s3, the equipment meeting the maximum tolerance information age is not processed, and the sampling frequency of the equipment not meeting the maximum tolerance information age is adjusted by adopting a sampling frequency adjustment algorithm;
and S4, carrying out second-round pre-deployment on the service function diagram, and adjusting the sampling frequency of the equipment which does not meet the conditions until the information ages of all the equipment meet the maximum tolerance information age.
2. The information age optimization method according to claim 1, wherein the step of performing the first round of pre-deployment on the service function graph of the step S1 specifically comprises:
the equipment makes a deployment request to a server;
and the server determines whether to accept the deployment request from the equipment or not according to the income condition.
3. The information age optimization method according to claim 1, wherein the step S2 of determining whether the current information age of each device is lower than the preset device maximum tolerance information age according to the first round of pre-deployment specifically comprises:
presetting maximum tolerance information age for each device;
each device is according to the formula
Calculating the age of the information, where T represents the sampling frequency, TdRepresents the time elapsed from generation to transmission of the data packet to the destination, i.e. the service delay in the network;
and comparing the calculated information age with the maximum tolerance information age, and judging whether the current information age of each device is lower than the preset maximum tolerance information age of the device.
4. The information age optimization method according to claim 3, wherein the step S3 of not processing the devices that meet the maximum tolerated information age, and for the sampling frequency of the devices that do not meet the maximum tolerated information age, adjusting the sampling frequency by using a sampling frequency adjustment algorithm specifically comprises:
finding out equipment with the information age not meeting the maximum tolerance information age;
and for the equipment which does not meet the maximum tolerance information age, calling a sampling frequency adjustment algorithm for adjustment.
5. The information age optimization method according to any one of claims 1 to 4, wherein the step S4 of performing a second pre-deployment on the service function graph and adjusting the sampling frequency of the devices that do not satisfy the condition until the information ages of all the devices satisfy the maximum tolerance information age specifically includes:
s41, respectively calculating the deployment schemes of the SFGs, and knowing the requirement release condition of the SFGs on the network resources;
s42, adjusting the sampling frequency of the devices which do not meet the maximum tolerance AoI, and randomly adjusting the sampling frequency or reducing the sampling frequency by 5-15% until all the devices meet the requirement of the maximum tolerance AoI in an ideal network environment;
s43, if the information age of the device is adjusted for the first time, executing step S44, otherwise, if the device is judged to be the first time, the maximum tolerance AoI is not satisfied, executing step S42, if the device information age is such that the second occurrence does not meet the maximum tolerance AoI, then the current round of AoI is greater than or less than the previous round of AoI, and for devices greater than the previous round of AoI, the sampling frequency of the device is inverse to the originally performed regulation rule on the basis of the initial sampling frequency, namely, the previous round is up-regulated, the current round is down-regulated, the previous round is down-regulated, the current round is up-regulated, for less than the previous round AoI, the same adjustment rules are performed for the previous round, if the device does not meet the maximum tolerance AoI for the third or more occurrence, then the sampling frequency of the partial wheels AoI is adjusted according to three conditions, namely, the partial wheels are all larger than AoI of the front two wheels, are all smaller than AoI of the front two wheels and are between AoI of the front two wheels;
s44, deploying all SFGs by adopting a distributed game matching algorithm under the condition of a given sampling frequency;
s45, recalculating average AoI of each device;
s46, if the average AoI is improved compared with the arrangement of the previous round, continuously adjusting AoI the maximum sampling frequency of the equipment, and repeating the steps S43-S45, otherwise, terminating the algorithm.
6. The information age optimization method of claim 5, wherein the step of adjusting the sampling frequency in three cases, namely, AoI for each partial round AoI being greater than the first two rounds, AoI for each partial round being less than the first two rounds, and AoI for each partial round being between the first two rounds, specifically comprises:
if the AoI of the current round is larger than AoI of the previous two rounds, setting the sampling frequency of the next round by adopting a bisection method, taking the sampling frequency of the previous round as a terminal point, taking the sampling frequency of the previous round as a starting point, carrying out bisection, and determining the sampling frequency of the next round;
if the AoI rounds are all smaller than AoI of the previous two rounds, executing the same sampling rule of the previous round and determining the sampling frequency of the next round;
if the current round AoI is between AoI of the previous two rounds, the next round of sampling frequency is determined by dividing by two with the larger sampling frequency of the current round and the upper round as the end point and the smaller sampling frequency as the starting point.
7. An information age optimizing apparatus, comprising:
the pre-deployment module is used for carrying out first-round pre-deployment on the service function graph;
the judging module is used for judging whether the current information age of each device is lower than the preset maximum tolerated information age of the device or not according to the first round of pre-deployment condition;
the adjusting module is used for not processing the equipment which meets the maximum tolerance information age, and adjusting the sampling frequency of the equipment which does not meet the maximum tolerance information age by adopting a sampling frequency adjusting algorithm;
and the redeployment module is used for carrying out second-round redeployment on the service function diagram, and adjusting the sampling frequency of the equipment which does not meet the conditions until the information ages of all the equipment meet the maximum tolerance information age.
8. The information age optimization device of claim 7, wherein the deployment module comprises:
the request module is used for making a deployment request to the server by the equipment;
and the response module is used for determining whether to accept the deployment request from the equipment or not by the server according to the income condition.
9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of the information age optimization method of any one of claims 1 to 6.
10. A computer-readable storage medium having computer-readable instructions stored thereon which, when executed by a processor, implement the steps of the information age optimization method of any one of claims 1 to 6.
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| CN115086190B (en) * | 2022-06-29 | 2024-03-01 | 中国电信股份有限公司 | Data processing method and device and computer storage medium |
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