CN113438743B - Self-adaptive multi-server polling access control method and system - Google Patents
Self-adaptive multi-server polling access control method and system Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W74/00—Wireless channel access, e.g. scheduled or random access
- H04W74/04—Scheduled or contention-free access
- H04W74/06—Scheduled or contention-free access using polling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/147—Network analysis or design for predicting network behaviour
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0852—Delays
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1004—Server selection for load balancing
- H04L67/1008—Server selection for load balancing based on parameters of servers, e.g. available memory or workload
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a self-adaptive multi-server polling access control method and a system, wherein the method comprises the following steps: acquiring polling historical data, constructing a neural network prediction model based on the historical data, counting the number of stations and idle servers polled currently, measuring the traffic volume in the network by using the cell arrival rate of the stations during polling, adaptively selecting the number of accessed servers according to the cell arrival rate of the stations and the prediction feedback of the prediction model, providing complete service for the stations, outputting the average waiting time delay of the polling to the prediction model after the polling is finished, and performing time delay prediction by the prediction model to form the prediction feedback of the next polling; the method can adjust the number of the servers accessed into the system according to the cell arrival rate and the time delay predicted value, so that the data transmission is more efficient, the network time delay is reduced, the service quality of the system is improved, the problem of resource waste caused by a fixed number of servers is avoided, and the method has good self-adaptive capacity.
Description
Technical Field
The invention relates to the technical field of wireless network polling systems, in particular to a self-adaptive multi-server polling access control method and a self-adaptive multi-server polling access control system.
Background
In the 5G era, mass data transmission and access to highly dense sites have led to a drastic increase in network scale and traffic, and wireless networks need to have a greater processing capacity to meet user requirements on the premise of guaranteeing network latency. As an important resource scheduling method, the polling system realizes collision-free transmission of data through a shared channel during data acquisition, and is widely used in wireless networks. A traditional polling system uses a single server to inquire all stations and complete data acquisition, and when the service volume in the system is large, the energy efficiency and the throughput are limited, the time delay of a network is high, and the user experience is seriously influenced.
In the current stage, research on polling systems mainly focuses on the aspects of service policy and query sequence conversion, and the polling systems are improved in priority and energy efficiency based on three most basic service policies of completeness, threshold and limitation. However, in the polling system with multi-server access control, since the relative movement of the servers at the scheduling time between the sites is complicated, the research on the polling system is less. On the other hand, a wireless network is affected by the environment, the traffic in the network varies, and when the traffic is small, resources are easily wasted due to the fixed number of servers. Therefore, the research can adaptively select the polling systems with the number of the access servers according to the network flow, and the feedback is formed through the prediction of the network operation parameters, so that the method has practical significance for deepening the application of the polling systems in the wireless network.
Disclosure of Invention
In order to solve the defects in the prior art, the inventor provides a self-adaptive multi-server polling access control method and a self-adaptive multi-server polling access control system, and the access number of servers is selected in a self-adaptive mode according to the traffic and the time delay predicted value in a network, so that the data transmission is more efficient, the network time delay is reduced, and the service quality is improved.
Specifically, the present invention is realized by:
according to a first aspect, the present invention provides an adaptive multi-server polling access control method, which is characterized by comprising the following steps:
step S1, establishing a prediction model, including
Acquiring polling historical data, wherein the historical data comprises cell arrival rate and site cell delay corresponding to the cell arrival rate;
constructing a prediction model based on historical data, performing time delay prediction and outputting a time delay prediction value;
step S2, polling conversion, including
Counting the number of currently polled sites and the number of idle servers;
receiving a service request of a current site;
step S3, the station access includes
Accessing a site and determining the cell arrival rate of the current site;
step S4, adaptive server access, including
Acquiring a time delay predicted value formed after the last polling is finished, adopting a rand () function to pre-select the number of servers accessed into the system according to the cell arrival rate determined when the station is accessed, then adjusting the number of the servers accessed finally according to the time delay predicted value of the last polling, and if the feedback time delay is increased, increasing the number of the servers, otherwise, reducing the number of the servers;
step S5, cell transmission, includes
Counting the time consumed by the polling transmission cell by using a service time accumulation variable, accumulating a service time corresponding to the service time accumulation variable every time a cell is transmitted, and outputting the average waiting time delay of the polling;
step S6, predicting feedback, including
And after the polling is finished, receiving the average waiting time delay from the cell transmission, obtaining a time delay predicted value according to the cell arrival rate and the average waiting time delay of the current polling based on a prediction model, and forming prediction feedback of the next polling according to the time delay predicted value.
Further, the historical data in the step S1 includes data of the system in three states of idle, balanced and saturated;
the step S1 of building a prediction model based on the historical data includes:
s11: preparing data;
s12: forming station cell time delays under different cell arrival rates into a series of discrete data, and constructing a neural network prediction model;
s13: and training the prediction model, using circulation, taking the prediction value output by the prediction model as input, and optimizing the prediction model.
Further, in the polling conversion in step S2, the counting the number of currently polled sites and the number of idle servers includes:
inquiring the sites in sequence, judging whether the current site sends a service request, if so, receiving the service request of the current site, and determining the cell arrival rate of the site; and otherwise, inquiring the next station, and judging whether the next station sends the request or not until the station sends the request.
Further, the adaptive server accessing in step S4 includes:
step S41: preselecting the number of accessed servers according to the cell arrival rate;
step S42: and adjusting the number of the accessed servers according to the time delay predicted value.
Further, the step S6 includes:
after the service is finished, judging whether the polling is finished or not, if so, outputting average waiting time delay, and forming a time delay predicted value of next polling based on the average waiting time delay of the polling; if not, switching to the next station, and repeating the steps S2, S3, S4 and S5 until the current polling is finished.
Further, the step S11 includes:
the acquired historical data is divided into a training set, a test set and a verification set according to the proportion of 70%, 15% and 15%, and then each sample value is scaled to (-1, 1) by using a minmax _ scale () function.
Further, the number of the neurons of the input layer of the neural network prediction model is 3, the number of the neurons of the hidden layer is 20, the number of the neurons of the output layer is 1, the MSE is selected as the error function, and the maximum iteration of the network is 103Next, the expected error is 10-5The learning efficiency is 0.01, and the training algorithm is a back propagation algorithm.
According to a second aspect, the invention also provides a computer-readable storage medium, on which a computer program is stored, characterized in that the program is executable by a processor to implement the steps of the method according to any one of claims 1 to 7.
According to a third aspect, the present invention further provides an adaptive multi-server polling access control system, which comprises a plurality of servers and a plurality of sites, and is characterized by further comprising a control computer, and a program module for establishing a prediction model, a polling conversion program module, a site access program module, an adaptive server access program module, a cell transmission program module and a prediction feedback program module, which are arranged in the control computer;
the program module for establishing a prediction model is used for realizing the step S1;
the polling conversion program module is used for realizing the step S2;
the station access program module is used for realizing the step S3;
the adaptive server access program module is used for realizing the step S4;
the cell transmission program module is used for realizing the step S5;
the predictive feedback program module is used to implement step S6.
Further, the control computer has built therein a computer-readable storage medium as described above.
Compared with the prior art, the invention has the beneficial effects that:
the adaptive multi-server polling access control method and the system provided by the invention use the arrival rate of the site cells to measure the traffic volume in the network, combine the neural network prediction model to predict and feed back the time delay, and can adjust the number of the servers accessed into the system according to the traffic volume in the network and the predicted value of the average waiting time delay of the cells, so that the data transmission is more efficient, the network time delay is reduced, the service quality of the system is improved, the problem of resource waste caused by a fixed number of servers is avoided, and the adaptive multi-server polling access control method and the system have good adaptive capacity.
Drawings
Fig. 1 is a system block diagram of an adaptive multi-server polling access system in embodiment 1 of the present invention;
FIG. 2 is a flowchart of the predictive model training process according to embodiment 1 of the present invention;
fig. 3 is a flowchart illustrating an operation of the adaptive multi-server polling access system in embodiment 1 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings.
The cell arrival rate refers to the number of cells which arrive in a queue in a station within unit time, and is obtained by simulating through a Monte Carlo experiment, generating a Poisson distribution two-dimensional sequence group with the mean value of lambda based on Matlab pseudo random numbers, and then simulating the number of cells which arrive in a queue in each station within unit time in a system.
The delay is the difference between the data generation time and the service time, the time consumed by the polling transmission cell is counted by using the service time accumulation variable, every time a cell is transmitted, the service time is accumulated by the corresponding service time accumulation variable, and the average waiting delay is obtained by dividing the service time accumulation of the polling by the number of the polled cells.
Example 1
As shown in fig. 1, the present invention provides an adaptive multi-server polling access control system, which includes: establishing a prediction model program module 6, a site access program module 1, a polling conversion program module 2, an adaptive server access program module 3, a cell transmission program module 4 and a prediction feedback program module 5, wherein the site access program module 1 is used for accessing a site and determining the cell arrival rate of the current site. The polling conversion program module 2 is used for counting the number of currently polled sites and the number of idle servers, and is also used for sequentially inquiring the sites, judging whether the current site sends a service request, and if so, receiving the service request of the current site. The cells arrive at the site according to the poisson distribution, and when the site accesses the site, the program module 1 determines the cell arrival rate of the site. And if the station does not send the service request, inquiring the next station, and judging whether the next station sends the request or not until the station sends the request.
The adaptive server access program module 3 is used for obtaining the prediction feedback formed after the last polling is finished, preselecting the number of servers accessed into the system by using a rand () function according to the cell arrival rate determined by the station access program module 1, and then adjusting the number of the servers finally accessed according to the prediction feedback of the last polling. The number of servers can be selected according to the following table. The size of one cell in the table is set to 1100 bytes and one slot is 20 microseconds.
The cell transmission program module 4 is used to count the time consumed by the current polling transmission cell by using the service time accumulation variable, and when transmitting a cell, the corresponding service time accumulation variable accumulates a service time and outputs the average waiting time delay of the current polling.
And a prediction model establishing program module 6 is used for acquiring historical data of previous polling, wherein the historical data comprises cell arrival rate of each polling and site cell delay corresponding to the cell arrival rate, and the historical data comprises data of the system in three states of idle, balance and saturation in order to make the system operate and fit an actual operation scene. As shown in fig. 2, the model building prediction program module 6 abstracts the relationship between the cell arrival rate and the corresponding site cell delay into a sequence prediction problem, i.e. a series of discrete data is formed by using delays at different cell arrival rates, all data are divided into a training set, a test set and a verification set according to the proportion of 70%, 15% and 15%, and then each sample value is scaled to (-1, 1) by using a minmax _ scale () function, so as to enhance the comparability between each characteristic of the sample and make the solving process smoother. After the NAR regression neural network model is built, training a prediction model, setting the number of neurons of an input layer of the network to be 3, the number of neurons of a hidden layer to be 20, the number of neurons of an output layer to be 1, selecting MSE (mean square error) as an error function, and maximizing the networkIteration 103Then, the expected error is 10-5The learning efficiency is 0.01, the error back propagation algorithm (BP) is selected by the training algorithm, meanwhile, circulation is used, the predicted value output by the prediction model is used as input, multi-step prediction optimization is carried out on the prediction model, and the optimal training target is reached after the network iteration is carried out for 90 times. When polling, the prediction feedback program module 5 receives the average waiting time delay from the cell transmission program module 4, based on the prediction model, obtains a time delay prediction value according to the cell arrival rate and the average waiting time delay of the current polling, and forms the prediction feedback of the next polling according to the time delay prediction value, the feedback time delay is increased, the number of servers is increased, otherwise, the feedback time delay is reduced, and the high-efficiency operation of the system is ensured. In particular, assume that the system is at an arrival rate λiThe time delay of the lower site cell is E [ w ]i]Then the arrival rate at (λ) can be usedi-N，λi) The N historical data within the range are predicted as inputs, and are represented by a matrix as:
wherein i represents station i (i ═ 1, 2, …, N).
Specifically, as shown in fig. 3, the workflow of the system is as follows:
firstly, initializing parameters, and counting the number of stations and the number of idle servers. Then inquiring the sites in sequence, judging whether the current site sends a service request, if so, receiving the service request of the current site, and determining the cell arrival rate of the site. And otherwise, inquiring the next station, and judging whether the next station sends the request or not until the station sends the request. And after the cell arrival rate of the site is determined, the number of the accessed servers is preselected, and then the number of the accessed servers is adjusted according to the prediction feedback made by the prediction model after the last polling is finished. The accessed server provides complete service for the current site (when a plurality of servers are scheduled, the polling of the servers among various sites is realized by adopting an asynchronous control mode). After the service is finished, judging whether the polling is finished or not, if so, outputting average waiting time delay, and forming prediction feedback of next polling based on the average waiting time delay of the polling; if not, switching to the next station for inquiry, and repeating the inquiry until the current polling is finished.
The invention provides a self-adaptive multi-server polling access control method and a system, which measure the traffic volume in a network by using a cell arrival rate, preselect the number of accessed servers according to the cell arrival rate, and further adjust the number of the accessed servers according to the time delay prediction fed back by a prediction model after the last polling is finished. The service efficiency of the polling system is improved, the network delay is reduced, the problem of resource waste caused by a fixed number of servers is avoided, and the self-adaptive capacity is good.
Example 2
Defining service time beta as 2 time slots, counting to obtain 6 station numbers N, 1-6, 4 idle servers S, A-D, sequentially inquiring 6 stations from the station number 1 by the polling conversion program module 2, sequentially serving the 6 stations if all the stations need to be served, taking the station number 1 as an example, receiving a service request of the station number 1, accessing the station by the station access program module 1, determining the cell arrival rate lambda of the station to be 0.05 cell/time slot when accessing the station number 1, and then generating a poisson distribution sequence with the arrival rate of 0.05 cell/time slot by using an exprand () function, wherein the poisson distribution sequence is used for representing the cell number, and the cell number is 674683. Then, the adaptive server access module 3 obtains the prediction feedback formed after the last polling is finished, the prediction delay value is 3.0397 time slots, the reference table is selected according to the number of servers in embodiment 1, the preselection server A, B, C, D serves the station No. 1, and since the delay prediction value is 3.0397 time slots which are smaller than 3 time slots, it is finally determined that the server A, B, C serves the station No. 1, and the cell transmission program module 4 uses the service time accumulation variable to count the polling transmission signal of this timeThe time consumed by a cell is accumulated for a service time corresponding to the service time accumulation variable every time a cell is transmitted. After the end of the polling, the accumulated service time is 2.1247 × 106And (4) time slot, wherein the average waiting time delay is 3.1492, after the polling is finished, the prediction model predicts according to the average time delay, and the time delay prediction value is 3.3119 time slot.
Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments may be implemented by hardware, or may be implemented by computer programs. When all or part of the functions of the above embodiments are implemented by a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above may be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.
Claims (10)
1. A self-adaptive multi-server polling access control method is characterized by comprising the following steps:
step S1, establishing a prediction model, including
Acquiring polling historical data, wherein the historical data comprises cell arrival rate and site cell delay corresponding to the cell arrival rate;
constructing a prediction model based on historical data, performing time delay prediction and outputting a time delay prediction value;
step S2, polling conversion, including
Counting the number of currently polled sites and the number of idle servers;
receiving a service request of a current site;
step S3, the station access includes
Accessing a site and determining the cell arrival rate of the current site;
step S4, adaptive server access, including
Acquiring a time delay predicted value formed after the last polling is finished, adopting a rand () function to pre-select the number of servers accessed into the system according to the cell arrival rate determined when the station is accessed, then adjusting the number of the servers accessed finally according to the time delay predicted value of the last polling, and if the feedback time delay is increased, increasing the number of the servers, otherwise, reducing the number of the servers;
step S5, cell transmission, includes
Counting the time consumed by the polling transmission cell by using a service time accumulation variable, accumulating a service time corresponding to the service time accumulation variable every time a cell is transmitted, and outputting the average waiting time delay of the polling;
step S6, predicting feedback, including
And after the polling is finished, receiving the average waiting time delay from the cell transmission, obtaining a time delay predicted value according to the cell arrival rate and the average waiting time delay of the current polling based on a prediction model, and forming prediction feedback of the next polling according to the time delay predicted value.
2. The adaptive multi-server polling access control method of claim 1, wherein the historical data in step S1 includes data of the system in three states of idle, balanced and saturated;
the step S1 of building a prediction model based on the historical data includes:
step S11: preparing data;
step S12: forming station cell time delays under different cell arrival rates into a series of discrete data, and constructing a neural network prediction model;
step S13: and training the prediction model, using circulation, taking the prediction value output by the prediction model as input, and optimizing the prediction model.
3. The adaptive multi-server polling access control method of claim 1, wherein the polling transition of step S2, counting the number of currently polled sites and the number of idle servers comprises:
inquiring the sites in sequence, judging whether the current site sends a service request, if so, receiving the service request of the current site, and determining the cell arrival rate of the site; and otherwise, inquiring the next station, and judging whether the next station sends the request or not until the station sends the request.
4. The adaptive multi-server polling access control method of claim 1, wherein the adaptive server access in step S4 comprises:
step S41: preselecting the number of accessed servers according to the cell arrival rate;
step S42: and adjusting the number of the accessed servers according to the time delay predicted value.
5. The adaptive multi-server polling access control method of any one of claims 1-4, wherein the step S6 comprises:
after the service is finished, judging whether the polling is finished or not, if so, outputting average waiting time delay, and forming a time delay predicted value of next polling based on the average waiting time delay of the polling; if not, switching to the next station, and repeating the steps S2, S3, S4 and S5 until the current polling is finished.
6. The adaptive multi-server polling access control method of claim 2, wherein the step S11 comprises:
the acquired historical data is divided into a training set, a test set and a verification set according to the proportion of 70%, 15% and 15%, and then each sample value is scaled to (-1, 1) by using a minmax _ scale () function.
7. The adaptive multi-server polling access control method of claim 2, wherein the neural network prediction model has an input layer with 3 neurons, a hidden layer with 20 neurons, an output layer with 1 neuron, an error function selected from MSE, and a network maximum iteration of 103Next, the expected error is 10-5The learning efficiency is 0.01, and the training algorithm is a back propagation algorithm.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executable by a processor to implement the steps of the method according to any of claims 1 to 7.
9. A self-adaptive multi-server polling access control system comprises a plurality of servers and a plurality of sites, and is characterized by also comprising a control computer, a program module for establishing a prediction model, a polling conversion program module, a site access program module, a self-adaptive server access program module, a cell transmission program module and a prediction feedback program module, wherein the program module is arranged in the control computer;
the program module for establishing the prediction model is used for acquiring polling historical data, establishing a prediction model based on the historical data, performing time delay prediction and outputting a time delay prediction value; the historical data comprises cell arrival rate and site cell delay corresponding to the cell arrival rate;
the polling conversion program module is used for counting the number of the current polled sites and the number of idle servers and receiving the service request of the current site;
the station access program module is used for accessing the station and determining the cell arrival rate of the current station;
the self-adaptive server access program module is used for acquiring a time delay predicted value formed after the last polling is finished, adopting a rand () function to pre-select the number of servers accessed to the system according to the cell arrival rate determined when the station is accessed, then adjusting the number of the servers accessed finally according to the time delay predicted value of the last polling, wherein the number of the servers is increased if the feedback time delay is increased, otherwise, the number of the servers is reduced;
the cell transmission program module is used for counting the time consumed by the polling transmission cell at the time by using the service time accumulated variable, accumulating a service time corresponding to the service time accumulated variable every time a cell is transmitted, and outputting the average waiting time delay of the polling at the time;
and the prediction feedback program module is used for receiving the average waiting time delay from the cell transmission after the polling is finished, obtaining a time delay prediction value according to the cell arrival rate and the average waiting time delay of the current polling based on a prediction model, and forming prediction feedback of the next polling according to the time delay prediction value.
10. The adaptive multi-server polling access control system of claim 9, wherein the control computer has the computer-readable storage medium of claim 8 disposed therein.
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