CN117955879A - Time delay jitter optimization algorithm for burst service scene - Google Patents
Time delay jitter optimization algorithm for burst service scene Download PDFInfo
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- CN117955879A CN117955879A CN202311825623.8A CN202311825623A CN117955879A CN 117955879 A CN117955879 A CN 117955879A CN 202311825623 A CN202311825623 A CN 202311825623A CN 117955879 A CN117955879 A CN 117955879A
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- 238000005457 optimization Methods 0.000 title claims abstract description 19
- 230000005540 biological transmission Effects 0.000 claims abstract description 49
- 238000000034 method Methods 0.000 claims abstract description 32
- 238000013468 resource allocation Methods 0.000 claims abstract description 14
- 230000000737 periodic effect Effects 0.000 claims description 11
- 239000006185 dispersion Substances 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 2
- 239000000872 buffer Substances 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003139 buffering effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 238000005538 encapsulation Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000007493 shaping process Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
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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
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
- H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
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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
- 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
- H04L43/087—Jitter
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- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Environmental & Geological Engineering (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The application relates to a delay jitter optimization algorithm in a burst service oriented scene, which comprises the following steps: constructing an end-to-end time sensitive network transmission model oriented to a burst service scene; calculating decoding error probability of different transmission services according to a finite code length formula and feeding back the decoding error probability to a transmitting end to determine whether retransmission is carried out; and constructing a code length resource allocation problem aiming at minimizing delay jitter according to the decoding error probability, and efficiently solving the problem by using a convex optimization method to realize the code length resource allocation for guaranteeing the certainty and reliability requirements of the time sensitive network. The application can greatly ensure the certainty and reliability requirements of the time-sensitive network.
Description
Technical Field
The application relates to the technical field of mobile communication networks, in particular to a delay jitter optimization algorithm for a burst service scene.
Background
The delay jitter of the TSN network is mainly caused by queues or buffers in the network, which is an index for measuring the congestion level of the network, and also affects the quality of experience of the real-time service of the network. The size of the delay jitter depends on a number of factors including network traffic, the number of nodes, node throughput, and data frame encapsulation mode. In the application scenario of the energy internet, a lower delay jitter is required, so that the end-to-end delay jitter needs to be optimized. In conventional ethernet switching, if a switch has begun transmitting ethernet frames on one of its ports, even the highest priority frame must wait for the frame being transmitted to complete. Non-deterministic end-to-end propagation delays are unavoidable due to buffering effects within the switch. In practical applications, besides time triggered flows that need on-time and accurate transmission, there is critical event triggered traffic, which is non-periodic, bursty, and may cause network breakdown. How to control traffic transmission and resource allocation while non-periodic time sensitive flows exist, not only satisfies the transmission quality of the non-periodic flows, but also avoids the influence of the periodic flows, and a suitable solution is not available at present. On the data plane, asynchronous traffic shaping does not require a synchronous mechanism, reshaping the stream by each hop, allowing transmission of non-periodic streams, but with poor performance in the face of high-load periodic traffic. Therefore, in the context of the energy internet, research on how to optimize the end-to-end delay jitter, especially in the case of coexistence of non-periodic traffic and periodic traffic, is one of the problems that needs to be solved currently. Policies in terms of traffic scheduling, resource allocation, and buffer management may need to be taken into account comprehensively to ensure that the network can efficiently and stably support the transmission of various traffic flows.
Disclosure of Invention
The embodiment of the application aims to provide a delay jitter optimization algorithm for a burst service scene, which reduces the delay jitter of transmission information and meets the priority requirement of transmission service.
In order to achieve the above purpose, the present application provides the following technical solutions:
The embodiment of the application provides a delay jitter optimization algorithm for a burst service scene, which comprises the following steps:
Constructing an end-to-end time sensitive network transmission model oriented to a burst service scene;
calculating decoding error probability of different transmission services according to a finite code length formula and feeding back the decoding error probability to a transmitting end to determine whether retransmission is carried out;
And constructing a code length resource allocation problem aiming at minimizing delay jitter according to the decoding error probability, and efficiently solving the problem by using a convex optimization method to realize the code length resource allocation for guaranteeing the certainty and reliability requirements of the time sensitive network.
The end-to-end time sensitive network transmission model comprises a high-priority traffic transmitting node, a low-priority traffic transmitting node, a burst traffic transmitting node and a receiving end, wherein the high-priority traffic and the low-priority traffic arrive in a periodic form, the arrival rate of the high-priority traffic is r h, the arrival rate of the low-priority traffic is r l, the arrival process of the burst traffic is an aperiodic process, and meanwhile, the poisson process with the parameter of r b is assumed.
The calculating of decoding error probability of different transmission services and feeding back to the transmitting end to decide whether to retransmit specifically, according to a finite code length formula, the decoding error probability of the receiving end can be expressed as:
Wherein the method comprises the steps of And as a Q function, m represents the code length of transmission, V represents channel dispersion, C represents shannon channel capacity, r represents the data volume of the flow to be transmitted, and the probability of information transmission failure is the decoding error probability.
The construction aims at minimizing the code length resource allocation problem of delay jitter and utilizes a convex optimization method to efficiently solve the problem:
s.t.:mh+ml+mb≤M,
Wherein the optimization objective Meaning that the delay jitter mean/>, is minimized by optimizing the high priority traffic transmission code length m h, the low priority traffic transmission code length m l and the burst traffic code length m b The constraint M h+ml+mb ∈m means that the sum of the high priority traffic transmission code length M h, the low priority traffic transmission code length M l and the burst traffic code length M b is not greater than a given maximum transmission code length.
Compared with the prior art, the application has the beneficial effects that:
The invention solves the limitation of the traditional method on code length distribution facing the burst service in the time delay sensitive network scene, provides a time delay jitter optimization algorithm facing the burst service scene, reduces the time delay jitter while guaranteeing the communication reliability, and improves the certainty.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a proposed delay jitter optimization algorithm in a burst-oriented service scenario;
Fig. 2 is a proposed end-to-end time-sensitive network transmission model in a bursty traffic oriented scenario;
FIG. 3 is a graph of delay jitter versus burst traffic arrival rate and high priority traffic arrival rate in an embodiment
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Referring to fig. 1, a delay jitter optimization algorithm in a burst service oriented scenario mainly includes constructing an end-to-end time sensitive network transmission model in the burst service oriented scenario; calculating decoding error probability of different transmission services according to a finite code length formula and feeding back the decoding error probability to a transmitting end to determine whether retransmission is carried out; and constructing a code length resource allocation problem aiming at minimizing delay jitter according to the decoding error probability, and efficiently solving the problem by using a convex optimization method to realize the code length resource allocation for guaranteeing the certainty and reliability requirements of the time sensitive network. Specifically:
Step 1: the implementation method of the invention firstly builds an end-to-end time sensitive network transmission model shown in fig. 2. The system comprises a high-priority traffic transmitting node, a low-priority traffic transmitting node, a burst traffic transmitting node and a receiving end. The high priority traffic and the low priority traffic arrive in a periodic fashion, the high priority traffic arriving at a rate r h and the low priority traffic arriving at a rate r l. The arrival process of the burst traffic is an aperiodic process and is assumed to be a poisson process with a parameter of r b. The arrival process distribution of high priority traffic may be expressed as The arrival process distribution of low priority traffic can be expressed as/>The arrival process distribution of bursty traffic can be expressed asThe priority of each type of transmission traffic is that burst traffic is larger than high priority traffic and lower priority traffic.
Step 2: the implementation method of the invention calculates the decoding error probability of different transmission services according to the finite code length formula and feeds back the decoding error probability to the transmitting end to determine whether to retransmit or not. According to the finite code length formula, the decoding error probability of the receiving end can be expressed as:
Wherein the method comprises the steps of As a Q function, m represents the code length of transmission, V represents channel dispersion, C represents shannon channel capacity, and r represents the amount of data that needs to be transmitted in terms of traffic. The probability of failure of information transmission is the decoding error probability.
Step 3: the implementation method of the invention finally establishes the code length resource allocation problem aiming at minimizing delay jitter. Calculating delay jitter of high-priority traffic, low-priority traffic and burst traffic according to the calculated decoding error probability, and constructing an optimization target of the code length resource allocation problem:
s.t.:mh+ml+mb≤M,
The meaning of the above objective is to minimize the delay jitter mean by optimizing the high priority traffic transmission code length m h, the low priority traffic transmission code length m l and the burst traffic code length m b
High priority traffic transmission code length M h, low priority traffic transmission code length M l and burst traffic code length M b constraint maximum transmission code M
mh+ml+mb≤M
I.e. the sum of the high priority traffic transmission code length m h, the low priority traffic transmission code length m l and the burst traffic code length m b is not larger than the given maximum transmission code length.
Assume thatFirst and second derivatives are calculated for the code length according to a finite code length formula:
It can be seen that the decoding error probabilities are convex for the high priority traffic transmission code length m h, the low priority traffic transmission code length m l and the burst traffic code length m b, respectively. The method comprises the steps of firstly solving an optimal value of a burst traffic code length according to priority, then taking the burst traffic code length as input to solve the optimal value of high-priority traffic, then taking the burst traffic code length and the high-priority traffic code length as input to solve the low-priority traffic code length, and finally solving the optimal allocation of the final burst traffic code length, the high-priority traffic code length and the low-priority traffic code length through iteration.
Fig. 3 shows a graph of delay jitter versus bursty traffic arrival rate and high priority traffic arrival rate, it can be seen that the delay jitter of the system increases with the frequent arrival of bursty traffic and high priority traffic.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (4)
1. The delay jitter optimization algorithm for the burst service scene is characterized by comprising the following steps of:
Constructing an end-to-end time sensitive network transmission model oriented to a burst service scene;
calculating decoding error probability of different transmission services according to a finite code length formula and feeding back the decoding error probability to a transmitting end to determine whether retransmission is carried out;
And constructing a code length resource allocation problem aiming at minimizing delay jitter according to the decoding error probability, and efficiently solving the problem by using a convex optimization method to realize the code length resource allocation for guaranteeing the certainty and reliability requirements of the time sensitive network.
2. The algorithm of claim 1, wherein the end-to-end time sensitive network transmission model includes a high priority traffic transmitting node, a low priority traffic transmitting node, a burst traffic transmitting node and a receiving end, the high priority traffic and the low priority traffic arrive in a periodic form, the arrival rate of the high priority traffic is r h, the arrival rate of the low priority traffic is r l, the arrival process of the burst traffic is an aperiodic process, and the poisson process with parameter r b is assumed.
3. The algorithm for optimizing delay jitter under a burst-oriented service scenario of claim 1, wherein the calculating of decoding error probabilities of different transmission services and feeding back to the transmitting end to determine whether to retransmit specifically, according to a finite code length formula, the decoding error probability of the receiving end can be expressed as:
Wherein the method comprises the steps of And as a Q function, m represents the code length of transmission, V represents channel dispersion, C represents shannon channel capacity, r represents the data volume of the flow to be transmitted, and the probability of information transmission failure is the decoding error probability.
4. The algorithm for optimizing delay jitter in a burst-oriented traffic scenario according to claim 1, wherein the construction aims at minimizing the code length resource allocation problem of delay jitter and efficiently solves by using a convex optimization method:
s.t.:mh+ml+mb≤M,
Wherein the optimization objective Meaning that the delay jitter mean/>, is minimized by optimizing the high priority traffic transmission code length m h, the low priority traffic transmission code length m l and the burst traffic code length m b The constraint M h+ml+mb ∈m means that the sum of the high priority traffic transmission code length M h, the low priority traffic transmission code length M l and the burst traffic code length M b is not greater than a given maximum transmission code length.
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