CN103457772A - Application-oriented network performance evaluation method for Internet of things - Google Patents
Application-oriented network performance evaluation method for Internet of things Download PDFInfo
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Abstract
The invention relates to an application-oriented network performance evaluation method for the Internet of things. The method overcomes the defect of isomerism of the Internet of things, and carries out analysis and evaluation on the Internet of things on an application layer, the fact that a network manager or a user can know the running state of the network timely and accurately is facilitated, and decision basis are provided for the network manager to improve the performance of the network. Compared with a traditional network performance evaluation method, the method combines the qualitative network performance evaluation method and the quantitative network performance evaluation method, and provides sensuous understanding and rational understanding for the network performance. The method firstly divides the network performance into four levels which are the blue level, the yellow level, the orange level and the red level from good to poor in sequence. The application-oriented network performance evaluation method for the Internet of things puts forward a new network performance evaluation index of an accumulated network performance evaluation value.
Description
Technical field
The invention belongs to network performance analysis and estimate field, particularly a kind of application oriented Internet of Things network performance evaluation method.
Background technology
Network performance is carried out to scientific and reasonable evaluation, contribute to promptly and accurately awareness network operation conditions of network manager or user, for the network management personnel improves network performance, provide decision-making foundation.Due to the difference of demand, the evaluation method of network performance is also varied.
The evaluation method of network performance is divided into two kinds of quantitative and qualitative analysis.
Evaluation method qualitatively, refer to by the observation grid operation conditions, perhaps measure simply the index of network operation situation, according to user's susceptibility and network performance evaluation analyst's subjective experience to the overall operation situation of network provide one rough, estimate not too accurately.
With evaluation method qualitatively, compare, quantitative evaluation method evaluation procedure is science more, and the result of evaluation is also more accurate.Quantitative evaluation method mainly comprises four kinds of methods: mensuration, mathematical methods, software simulation method, synthesis.
Mensuration: by measuring the index of network operation state, according to network performance evaluation analyst's previous experiences, the index that ECDC has been measured, provide simple an evaluation to network performance.
Mathematical methods: in the situation that network topology and network communication protocol are definite, analyzed the impact of network parameter index on network performance, by network parameter index and mathematical formulae, expressed network performance.Mathematical formulae, reflected the relation between network parameters index and network performance.
Software simulation method: be again Monte Carlo method, by setting up the method for model, simulate and need in true environment for a long time or network condition that other occurs under exacting terms very much, then, observation grid operation conditions in this case, make evaluation to network performance.
Synthesis: actual carry out network performance evaluation in, often above several method is combined to use, to reaching better more preferably evaluation result.
Evaluation method can allow the people, to network performance, preliminary, understanding perception is arranged qualitatively, but is inaccurate; Quantitative evaluation method is often based on mathematical method, and the evaluation result drawn is often based on numerical value, although relatively accurate, can not give the understanding of people's perception and process complexity.
Summary of the invention
The object of the present invention is to provide a kind of application oriented Internet of Things network performance evaluation method, solved the problems referred to above that prior art exists.For the isomerism of Internet of Things bottom, designed a kind of application oriented Internet of Things network performance evaluation method, contribute to promptly and accurately awareness network operation conditions of network manager or user, for improving network performance, the network management personnel provides decision-making foundation.The present invention combines the evaluation method of quantitative and qualitative analysis, will combine the understanding of network performance perception and the understanding of rationality, and process is relatively simple.
Above-mentioned purpose of the present invention is achieved through the following technical solutions:
Application oriented Internet of Things network performance evaluation method, combine qualitative evaluating method and method for quantitatively evaluating, comprises following steps:
(a) choose evaluation attributes, determine main evaluation attributes and less important evaluation attributes;
(b) measure evaluation of estimate, definite threshold and weight;
(c) qualitative analysis evaluation, determine level of network performance;
(d) evaluation attributes value normalization;
(e) quantitative analysis evaluation, computing network performance evaluation value and cumulative network performance evaluation value;
(f), according to the assay result, the optimized network configuration, improve network performance.
Qualitative analysis evaluation model in described step (c), according to the main evaluation attributes of network and less important evaluation attributes level of living in, be divided into four ranks by network performance, from good to poor, is followed successively by: blue rank, yellow rank, orange rank, red level; Other is defined as follows each level,
Blue rank: all main assessing network attributes meet the threshold value requirement, and all secondary network evaluation attributes meet the threshold value requirement;
Yellow rank: all main assessing network attributes meet the threshold value requirement, have at least secondary network evaluation attributes not meet the threshold value requirement;
Orange rank: have at least a main assessing network attribute not meet the threshold value requirement, all secondary network evaluation attributes meet the threshold value requirement;
Red level: have at least a main assessing network attribute not meet the threshold value requirement, have at least secondary network evaluation attributes not meet the threshold value requirement.
Quantitative analysis evaluation model in described step (e), calculate network performance evaluation value and cumulative network performance evaluation value;
Free section is separated as follows: [t1, t2, t3 ..., ti, tj, tk ... tn], the time can isometricly separate, and also can non-isometricly separate.
Network performance evaluation value calculating method in [ti, the tj] time period is as follows,
V (ti, tj)=w1*v1+w2*v2+ ... + wk*vk+ ... + wn*vn formula 3
Wherein, V (ti, tj) means the network performance evaluation value in [ti, the tj] time period, and wk means the weight of evaluation attributes k, and vk means the value after evaluation attributes k normalization;
If the ti of take is initial time constantly, tk cumulative network performance evaluation value calculating method constantly is as follows,
LV (ti, tk)=wij*V (ti, tj)+wjk*V (tj, tk) formula 4
Wherein, LV (ti, tk) means tk cumulative network performance evaluation value constantly,
here wij means that [ti, tj] accounts for total time the proportion of [ti, tk].
Described step (d) is that the evaluation attributes value is divided into to the mo(u)ld top half attribute with to the mo(u)ld bottom half attribute, and to the mo(u)ld top half attribute, the evaluation attributes value is larger, characterizes network performance in this regard better; To the mo(u)ld bottom half attribute, the evaluation attributes value is less, characterizes network performance in this regard better; The two computational methods is as follows,
Different with the method for normalizing to the mo(u)ld bottom half attribute to the mo(u)ld top half attribute, as follows to the method for normalizing of mo(u)ld top half attribute,
Wherein, AVi means to the property value of mo(u)ld top half evaluation attributes i, and Ti means to the threshold value of mo(u)ld top half evaluation attributes i, and vi means the value of i after normalization.
To the method for normalizing of mo(u)ld bottom half attribute,
Wherein, AVi means to the property value of mo(u)ld bottom half evaluation attributes i, and Ti means to the threshold value of mo(u)ld bottom half evaluation attributes i, and Vi means the value of i after normalization;
For the evaluation attributes value that exceeds threshold range, can think that these evaluation attributes are nonsensical, the numerical value after normalization is 0.
Beneficial effect of the present invention is: with traditional network performance evaluation method, compare, the present invention combines the network performance evaluation method of quantitative and qualitative analysis, and the understanding for network performance perception and rationality is provided; The present invention is divided into network performance four ranks first, from good to poor, is followed successively by: blue rank, yellow rank, orange rank, red level; The present invention proposes a new network performance evaluation index: cumulative network performance evaluation value.
The accompanying drawing explanation
Accompanying drawing described herein is used to provide a further understanding of the present invention, forms the application's a part, and illustrative example of the present invention and explanation thereof the present invention do not form inappropriate limitation of the present invention for explaining.
The isomerism schematic diagram that Fig. 1 is Internet of Things;
Fig. 2 is the level of network performance schematic diagram;
Fig. 3 is application oriented Internet of Things network performance evaluation model flow chart.
Embodiment
Further illustrate detailed content of the present invention and embodiment thereof below in conjunction with accompanying drawing.
Referring to shown in Fig. 1 to Fig. 3, application oriented Internet of Things network performance evaluation method of the present invention comprises following steps:
(1) choose evaluation attributes, determine main evaluation attributes and less important evaluation attributes;
(2) measure evaluation of estimate, definite threshold and weight;
(3) qualitative analysis evaluation, determine level of network performance;
(4) evaluation attributes value normalization;
(5) quantitative analysis evaluation, computing network performance evaluation value and cumulative network performance evaluation value;
(6), according to the assay result, the optimized network configuration, improve network performance.
Application oriented Internet of Things network performance evaluation method of the present invention, combine qualitative evaluating method and method for quantitatively evaluating.
Above-mentioned steps (3) qualitative analysis evaluation model:
Main assessing network attribute: the evaluation attributes relevant to the network main business, each network is because of the major function difference, and main assessing network attribute is also different.As in the real-time network, time delay, delay variation just should be classified main assessing network attribute as.
The secondary network evaluation attributes: little with network main business correlation, but contribute to improve the evaluation attributes of network performance, the secondary network evaluation attributes of each network are not quite similar.In Internet of Things, the repetition rate of packet is exactly secondary network evaluation attributes.
According to the main evaluation attributes of network and less important evaluation attributes level of living in, network performance is divided into to four ranks, from good to poor, be followed successively by: blue rank, yellow rank, orange rank, red level.Other is defined as follows each level,
Blue rank: all main assessing network attributes meet the threshold value requirement, and all secondary network evaluation attributes meet the threshold value requirement;
Yellow rank: all main assessing network attributes meet the threshold value requirement, have at least secondary network evaluation attributes not meet the threshold value requirement;
Orange rank: have at least a main assessing network attribute not meet the threshold value requirement, all secondary network evaluation attributes meet the threshold value requirement;
Red level: have at least a main assessing network attribute not meet the threshold value requirement, have at least secondary network evaluation attributes not meet the threshold value requirement.
The classification of above-mentioned steps (4) evaluation attributes:
The evaluation attributes value is divided into to the mo(u)ld top half attribute with to the mo(u)ld bottom half attribute.To the mo(u)ld top half attribute, the evaluation attributes value is larger, characterizes network performance in this regard better; To the mo(u)ld bottom half attribute, the evaluation attributes value is less, characterizes network performance in this regard better.The two computational methods is as follows,
Different with the method for normalizing to the mo(u)ld bottom half attribute to the mo(u)ld top half attribute, as follows to the method for normalizing of mo(u)ld top half attribute,
Wherein, AVi means to the property value of mo(u)ld top half evaluation attributes i, and Ti means to the threshold value of mo(u)ld top half evaluation attributes i, and vi means the value of i after normalization.
To the method for normalizing of mo(u)ld bottom half attribute,
Wherein, AVi means to the property value of mo(u)ld bottom half evaluation attributes i, and Ti means to the threshold value of mo(u)ld bottom half evaluation attributes i, and vi means the value of i after normalization.
For the evaluation attributes value that exceeds threshold range, can think that these evaluation attributes are nonsensical, the numerical value after normalization is 0.
Above-mentioned steps (5) quantitative analysis evaluation model:
Calculate network performance evaluation value and cumulative network performance evaluation value.
Free section is separated as follows: [t1, t2, t3 ..., ti, tj, tk ..., tn], the time can isometricly separate, and also can non-isometricly separate.
Network performance evaluation value calculating method in [ti, the tj] time period is as follows,
V (ti, tj)=w1*v1+w2*v2+ ... + wk*vk+ ... + wn*vn formula 3
Wherein, V (ti, tj) means the network performance evaluation value in [ti, the tj] time period, and wk means the weight of evaluation attributes k, and vk means the value after evaluation attributes k normalization.
If the ti of take is initial time constantly, tk cumulative network performance evaluation value calculating method constantly is as follows,
LV (ti, tk)=wij*V (ti, tj)+wjk*V (tj, tk) formula 4
Wherein, LV (ti, tk) means tk cumulative network performance evaluation value constantly,
here wij mean [ti, tj] account for total time [ti, tk ］ proportion.
A kind of application oriented Internet of Things network performance evaluation method disclosed by the invention, the method has overcome the isomerism of Internet of Things, in application layer, the Internet of Things network performance is carried out to assay, contribute to promptly and accurately awareness network operation conditions of network manager or user, for the network management personnel improves network performance, provide decision-making foundation.
Application oriented Internet of Things network performance evaluation model flow chart shown in Figure 3.The implementation that the present invention is concrete:
1, choose evaluation attributes
Internet of Things is carried out to cluster analysis, before a concrete Internet of Things is carried out to assay, should choose suitable evaluation attributes according to characteristic and the user's request of the result of cluster analysis and the evaluating network of wanting, and according to demand definite main evaluation attributes with will evaluation attributes.
Evaluation attributes choose complete after, adopt mensuration to measure the property value that calculates evaluation attributes.
2, measure the evaluation attributes value
Build experiment porch, measure the evaluation attributes value.Rule of thumb with actual conditions, determine weight and the thresholding of each attribute.By the evaluation attributes value normalization recorded, concrete grammar is as follows,
Evaluation attributes can be divided into two classes basically: a class is, the evaluation attributes value is larger, characterizes network performance in this regard better, and these evaluation attributes are referred to as to the mo(u)ld top half attribute; Another kind of, the evaluation attributes value is less, characterizes network performance in this regard better, and these evaluation attributes are referred to as to the mo(u)ld bottom half attribute.Network throughput, link utilization etc. are arranged to the mo(u)ld top half attribute; Time delay, delay variation, packet loss and response time etc. are arranged to the mo(u)ld bottom half attribute.
Different with the method for normalizing to the mo(u)ld bottom half attribute to the mo(u)ld top half attribute, as follows to the method for normalizing of mo(u)ld top half attribute,
Wherein, AVi means to the property value of mo(u)ld top half evaluation attributes i, and Ti means to the threshold value of mo(u)ld top half evaluation attributes i, and vi means the value of i after normalization.
To the method for normalizing of mo(u)ld bottom half attribute,
Wherein, AVi means to the property value of mo(u)ld bottom half evaluation attributes i, and Ti means to the threshold value of mo(u)ld bottom half evaluation attributes i, and vi means the value of i after normalization.
For the evaluation attributes value that exceeds threshold range, can think that these evaluation attributes are nonsensical, the numerical value after normalization is 0.Fig. 4 is evaluation attributes test result in experiment.
3, qualitative analysis evaluation
Level of network performance means by color,
Blue: all main assessing network attributes meet the threshold value requirement, and all secondary network evaluation attributes meet the threshold value requirement;
Yellow: all main assessing network attributes meet the threshold value requirement, have at least secondary network evaluation attributes not meet the threshold value requirement;
Orange: have at least a main assessing network attribute not meet the threshold value requirement, all secondary network evaluation attributes meet the threshold value requirement;
Red: as to have at least a main assessing network attribute not meet the threshold value requirement, have at least secondary network evaluation attributes not meet the threshold value requirement.
According to evaluation attributes measurement result (as shown in table 1 below) and network performance qualitative evaluation model (Fig. 3), can make preliminary qualitative evaluation to network, [t1, t2) in the time period, network performance is in yellow rank; [t2, t3) and [t3, t4) in the time period, network performance is in orange rank: network performance totally descends.
Table 1 evaluation attributes measurement result
| ? | Packet loss | Repetition rate | Load balancing degrees | R postpones |
| [t1，t2) | 27.0110% | 17.0932% | 52.3810% | 13.4465s |
| [t2，t3) | 86.2678% | 2.5503% | 61.9048% | 76.4929s |
| [t3，t4) | 64% | 6.2128% | 66.6667% | 25.3014s |
| Threshold value | %:[0，50] | %:[0，10] | %:[50，100] | s:[0，30] |
| Weight | 0.3 | 0.1 | 0.3 | 0.3 |
4, quantitative analysis evaluation
Free section is separated as follows: [t1, t2, t3 ..., ti, tj, tk ..., tn], the time can isometricly separate, and also can non-isometricly separate.
Network performance evaluation value calculating method in [ti, the tj] time period is as follows,
V (ti, tj)=w1*v1+w2*v2+ ... + wk*vk+ ... + wn*vn formula 3
Wherein, V (ti, tj) expression ti, tj] and interior network performance evaluation value of time period, wk means the weight of evaluation attributes k, vk means the value after evaluation attributes k normalization.
If the ti of take is initial time constantly, tk cumulative network performance evaluation value calculating method constantly is as follows,
LV (ti, tk)=wij*V (ti, tj)+wjk*V (tj, tk) formula 4
Wherein, LV (ti, tk) means tk cumulative network performance evaluation value constantly,
here wij means that [ti, tj] accounts for total time the proportion of [ti, tk].
Following table 2 is the result of calculating according to above-mentioned formula in experimentation.From experimental result, rear liter first falls in the network performance evaluation value, but totally on a declining curve; Cumulative network performance evaluation value is not high and descend always; This is consistent with the residing rank of network performance.
Table 2 network performance evaluation result
| ? | The network performance evaluation value | Cumulative network performance evaluation value |
| [t1，t2) | 0.3177 | 0.3177 |
| [t2，t3) | 0.1459 | 0.2319 |
| [t3，t4) | 0.1849 | 0.2162 |
By analysis, be mainly due to [t2, t3) and [t3, t4) packet loss of time period is too high, causes network performance to worsen.According to the assay result, the network manager should upgrade network configuration, and the optimized network structure reduces packet loss, improves network operation performance.
The foregoing is only preferred embodiment of the present invention, be not limited to the present invention, for a person skilled in the art, the present invention can have various modifications and variations.All any modifications made for the present invention, be equal to replacement, improvement etc., within protection scope of the present invention all should be included in.
Claims (4)
1. an application oriented Internet of Things network performance evaluation method is characterized in that: qualitative evaluating method and method for quantitatively evaluating are combined, comprise following steps:
(a) choose evaluation attributes, determine main evaluation attributes and less important evaluation attributes;
(b) measure evaluation of estimate, definite threshold and weight;
(c) qualitative analysis evaluation, determine level of network performance;
(d) evaluation attributes value normalization;
(e) quantitative analysis evaluation, computing network performance evaluation value and cumulative network performance evaluation value;
(f), according to the assay result, the optimized network configuration, improve network performance.
2. application oriented Internet of Things network performance evaluation method according to claim 1, it is characterized in that: qualitative analysis evaluation model in described step (c), according to the main evaluation attributes of network and less important evaluation attributes level of living in, network performance is divided into to four ranks, from good to poor, be followed successively by: blue rank, yellow rank, orange rank, red level; Other is defined as follows each level,
Blue rank: all main assessing network attributes meet the threshold value requirement, and all secondary network evaluation attributes meet the threshold value requirement;
Yellow rank: all main assessing network attributes meet the threshold value requirement, have at least secondary network evaluation attributes not meet the threshold value requirement;
Orange rank: have at least a main assessing network attribute not meet the threshold value requirement, all secondary network evaluation attributes meet the threshold value requirement;
Red level: have at least a main assessing network attribute not meet the threshold value requirement, have at least secondary network evaluation attributes not meet the threshold value requirement.
3. application oriented Internet of Things network performance evaluation method according to claim 1 is characterized in that: quantitative analysis evaluation model in described step (e) calculates network performance evaluation value and cumulative network performance evaluation value;
Free section is separated as follows: [t1, t2, t3 ..., ti, tj, tk ..., tn], the time can isometricly separate, and also can non-isometricly separate.
Network performance evaluation value calculating method in [ti, the tj] time period is as follows,
V (ti, tj)=w1*v1+w2*v2+ ... + wk*vk+ ... + wn*vn formula 3
Wherein, V (ti, tj) means the network performance evaluation value in [ti, the tj] time period, and wk means the weight of evaluation attributes k, and vk means the value after evaluation attributes k normalization;
If the ti of take is initial time constantly, tk cumulative network performance evaluation value calculating method constantly is as follows,
LV (ti, tk)=wij*V (ti, tj)+wjk*V (tj, tk) formula 4
Wherein, LV (ti, tk) means tk cumulative network performance evaluation value constantly,
here wij means that [ti, tj] accounts for total time the proportion of [ti, tk].
4. application oriented Internet of Things network performance evaluation method according to claim 1, it is characterized in that: described step (d) is that the evaluation attributes value is divided into to the mo(u)ld top half attribute with to the mo(u)ld bottom half attribute, to the mo(u)ld top half attribute, the evaluation attributes value is larger, characterizes network performance in this regard better; To the mo(u)ld bottom half attribute, the evaluation attributes value is less, characterizes network performance in this regard better; The two computational methods is as follows,
Different with the method for normalizing to the mo(u)ld bottom half attribute to the mo(u)ld top half attribute, as follows to the method for normalizing of mo(u)ld top half attribute,
Wherein, AVi means to the property value of mo(u)ld top half evaluation attributes i, and Ti means to the threshold value of mo(u)ld top half evaluation attributes i, and vi means the value of i after normalization.
To the method for normalizing of mo(u)ld bottom half attribute,
Wherein, AVi means to the property value of mo(u)ld bottom half evaluation attributes i, and Ti means to the threshold value of mo(u)ld bottom half evaluation attributes i, and vi means the value of i after normalization;
For the evaluation attributes value that exceeds threshold range, can think that these evaluation attributes are nonsensical, the numerical value after normalization is 0.
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Application publication date: 20131218 |