(Pao li, 1908); M. minusminus (Paoli, 1908); O. nova (Oudemans, 1902); T. minor
Berlese, 1903.
+ There were 6 species distributed at two survey times (accounting for 14.63% of the total number of species in the day-night cycle): E. crassisetiger Aoki, 1959; S. ruzsinszkyi Mahunka, 1983; S. minima Hammer, 1979; S. ornata (Hammer, 1979); S. africanus (Wallwork, 1964);
P. kaszabi Mahunka, 1988.
There are 13 species distributed only at one survey time (accounting for 31.70% of the total number of species in the day-night cycle), specifically:
+ At 12:00, there were 5 species: C. lata Aoki, 1961; U. clavatus Aoki, 1967; N. crisposetosa (Hammer, 1979); A. cocuyana (Balogh, 1984) and Q. quadricarinata (Michael, 1885).
+ At 18:00 there were 4 species: C. lanatus (Michael, 1885); M. brevipectinata Suzuki, 1976; S. sabahensis Mahunka, 1988; S. magnifera (Mahunka, 1978).
+ At 24:00 there are 4 species, specifically: S. quinquenodosa Balogh, 1968;
Perxylobates sp.; P. margaritata Mahunka, 1989, M. calcarata (Mahunka, 1978).
Thus, the data shows that the species are distributed quite widely between the time points. In particular, it is observed that a large number of taxa are concentrated at all four time points: 6:00 - 12:00 - 18:00 - 24:00, similar to a certain time point. In the four survey times, the time point 6:00 has the lowest number of recorded species-level taxa, the number of taxa increases and reaches the highest at 12:00 and 18:00.
3.4.2. Biodiversity in the day-night cycle
From the data in table 3.14 and figure 3.19, it can be seen that:
Between the time points, the number of species fluctuated from 14 to 25, the highest at 16:00 and 18:00 with 25 species (accounting for 60.97% of the total number of species), at 24:00 it decreased to 20 species (accounting for 48.78% of the total number of species), the lowest decrease at 6:00 with only 14 species (accounting for 34.14% of the total number of species). The difference in the number of species recorded between the time points was statistically significant with (P<0.05).
So in general, in the four survey times, the number of species is usually highest in the period from 12:00 to 18:00 and lower in the other two times. This fluctuation seems to be associated with the fluctuation of the day-night cycle with the most obvious changes in temperature, light, etc.
Number of species
30
Individual MDT/m 2
30000
25
25000
20
17240
20
20000
0
15
15000
10
MDT
S
6760
10000
5
5000
0
6:00
12:00
18:00
24h00
0
Day and night cycle
25
25
2
416
14
4560
Figure 3.19. Number of species and average density of oribatid mite communities during the day-night cycle
The average density (MDTB, individuals/m 2 ) of the four time points ranged from 4560 (individuals/m 2 ) - 24160 (individuals/m 2 ). The average density tended to decrease gradually from 12:00 (24160 individuals/m 2 ) > 18:00 (17240 individuals/m 2 ) > 24:00 (6760 individuals/m 2 ) > 6:00 (4560 individuals/m 2 ). Thus, it can be seen that the number of species and MDTB of individuals have similar fluctuations.
with the number and density usually peaking between 1200 and 1800, and the lowest at 6000 in the morning.
From the data in table 3.14 and figure 3.20 it can be seen that:
Species richness (d) of the oribatid mite community reached its highest at 18:00 with an average of 3.02 ± 0.29, the highest was 3.33 and the lowest was 2.76. Next was 12:00 with 2.76 ± 0.91, the highest was 3.48 and the lowest was 1.74. At 24:00 it reached 2.6 ± 1.04, the highest was 3.45 and the lowest was 1.44. Species richness was lowest at 6:00 with 2.1 ± 0.42, the highest was 2.53 and the lowest was 1.7. The average species richness index (d) at different times was statistically significant (P<0.05).
Table 3.14. Some quantitative indices of the oribatid mite community during the day-night cycle
Day and night cycle
Index
6:00 | 12:00 | 18:00 | 24h00 | |
S | 14 | 25 | 25 | 20 |
Average population (individuals/m 2 ) | 4560 | 24160 | 17240 | 6760 |
d | 2.1 ± 0.42 | 2.76 ± 0.91 | 3.02 ± 0.29 | 2.6 ± 1.04 |
J' | 0.78 ± 0.11 | 0.65 ± 0.19 | 0.75 ± 0.19 | 0.80 ± 0.21 |
H' | 1.59 ± 0.10 | 1.75 ± 0.56 | 2.00 ± 0.33 | 1.63 ± 0.67 |
1-Lambda' | 0.74 ± 0.08 | 0.72 ± 0.16 | 0.80 ± 0.12 | 0.78 ± 0.16 |
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-
Total Number of Reptile Species Recorded in Laos by Period -
![Mobile Phone Usage in Hanoi Inner City Area
zt2i3t4l5ee
zt2a3gsconsumer,consumption,consumer behavior,marketing,mobile marketing
zt2a3ge
zc2o3n4t5e6n7ts
- Test the relationship between demographic variables and consumer behavior for Mobile Marketing activities
The analysis method used is the Chi-square test (χ2), with statistical hypotheses H0 and H1 and significance level α = 0.05. In case the P index (p-value) or Sig. index in SPSS has a value less than or equal to the significance level α, the hypothesis H0 is rejected and vice versa. With this testing procedure, the study can evaluate the difference in behavioral trends between demographic groups.
CHAPTER 4
RESEARCH RESULTS
During two months, 1,100 survey questionnaires were distributed to mobile phone users in the inner city of Hanoi using various methods such as direct interviews, sending via email or using questionnaires designed on the Internet. At the end of the survey, after checking and eliminating erroneous questionnaires, the study collected 858 complete questionnaires, equivalent to a rate of about 78%. In addition, the research subjects of the thesis are only people who are using mobile phones, so people who do not use mobile phones are not within the scope of the thesis, therefore, the questionnaires with the option of not using mobile phones were excluded from the scope of analysis. The number of suitable survey questionnaires included in the statistical analysis was 835.
4.1 Demographic characteristics of the sample
The structure of the survey sample is divided and statistically analyzed according to criteria such as gender, age, occupation, education level and personal income. (Detailed statistical table in Appendix 6)
- Gender structure: Of the 835 completed questionnaires, 49.8% of respondents were male, equivalent to 416 people, and 50.2% were female, equivalent to 419 people. The survey results of the study are completely consistent with the gender ratio in the population structure of Vietnam in general and Hanoi in particular (Male/Female: 49/51).
- Age structure: 36.6% of respondents are <23 years old, equivalent to 306 people. People from 23-34 years old
accounting for the highest proportion: 44.8% equivalent to 374 people, people aged 35-45 and >45 are 70 and 85 people equivalent to 8.4% and 10.2% respectively. Looking at the results of this survey, we can see that the young people - youth account for a large proportion of the total number of people participating in the survey. Meanwhile, the middle-aged people including two age groups of 35 - 45 and >45 have a low rate of participation in the survey. This is completely consistent with the reality when Mobile Marketing is identified as a Marketing service aimed at young people (people under 35 years old).
- Structure by educational level: among 835 valid responses, 541 respondents had university degrees, accounting for the highest proportion of ~ 75%, 102 had secondary school degrees, ~ 13.1%, and 93 had post-graduate degrees, ~ 11.9%.
- Occupational structure: office workers and civil servants are the group with the highest rate of participation with 39.4%, followed by students with 36.6%. Self-employed people account for 12%, retired housewives are 7.8% and other occupational groups account for 4.2%. The survey results show that the student group has the same rate as the group aged <23 at 36.6%. This shows the accuracy of the survey data. In addition, the survey results distributed by occupational criteria have a rate almost similar to the sample division rate in chapter 3. Therefore, it can be concluded that the survey data is suitable for use in analysis activities.
- Income structure: the group with income from 3 to 5 million has the highest rate with 39% of the total number of respondents. This is consistent with the income structure of Hanoi people and corresponds to the average income of the group of civil servants and office workers. Those
People with no income account for 23%, income under 3 million VND accounts for 13% and income over 5 million VND accounts for 25%.
4.2 Mobile phone usage in Hanoi inner city area
According to the survey results, most respondents said they had used the phone for more than 1 year, specifically: 68.4% used mobile phones from 4 to 10 years, 23.2% used from 1 to 3 years, 7.8% used for more than 10 years. Those who used mobile phones for less than 1 year accounted for only a very small proportion of ~ 0.6%. (Table 4.1)
Table 4.1: Time spent using mobile phones
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Alid
<1 year
5
.6
.6
.6
1-3 years
194
23.2
23.2
23.8
4-10 years
571
68.4
68.4
92.2
>10 years
65
7.8
7.8
100.0
Total
835
100.0
100.0
The survey indexes on the time of using mobile phones of consumers in the inner city of Hanoi are very impressive for a developing country like Vietnam and also prove that Vietnamese consumers have a lot of experience using this high-tech device. Moreover, with the majority of consumers surveyed having a relatively long time of use (4-10 years), it partly proves that mobile phones have become an important and essential item in peoples daily lives.
When asked about the mobile phone network they are using, 31% of respondents said they are using the network of Vietel company, 29% use the network of
of Mobifone company, 27% use Vinaphone companys network and 13% use networks of other providers such as E-VN telecom, S-fone, Beeline, Vietnammobile. (Figure 4.1).
Figure 4.1: Mobile phone network in use
Compared with the announced market share of mobile telecommunications service providers in Vietnam (Vietel: 36%, Mobifone: 29%, Vinaphone: 28%, the remaining networks: 7%), we see that the survey results do not have many differences. However, the statistics show that there is a difference in the market share of other networks because the Hanoi market is one of the two main markets of small networks, so their market share in this area will certainly be higher than that of the whole country.
According to a report by NielsenMobile (2009) [8], the number of prepaid mobile phone subscribers in Hanoi accounts for 95% of the total number of subscribers, however, the results of this survey show that the percentage of prepaid subscribers has decreased by more than 20%, only at 70.8%. On the contrary, the number of postpaid subscribers tends to increase from 5% in 2009 to 19.2%. Those who are simultaneously using both types of subscriptions account for 10%. (Table 4.2).
Table 4.2: Types of mobile phone subscribers
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Valid
Prepay
591
70.8
70.8
70.8
Pay later
160
19.2
19.2
89.9
Both of the above
84
10.1
10.1
100.0
Total
835
100.0
100.0
The above figures show the change in the psychology and consumption habits of Vietnamese consumers towards mobile telecommunications services, when the use of prepaid subscriptions and junk SIMs is replaced by the use of two types of subscriptions for different purposes and needs or switching to postpaid subscriptions to enjoy better customer care services.
In addition, the majority of respondents have an average spending level for mobile phone services from 100 to 300 thousand VND (406 ~ 48.6% of total respondents). The high spending level (> 500 thousand VND) is the spending level with the lowest number of people with only 8.4%, on the contrary, the low spending level (under 100 thousand VND) accounts for the second highest proportion among the groups of respondents with 25.4%. People with low spending levels mainly fall into the group of students and retirees/housewives - those who have little need to use or mainly use promotional SIM cards. (Table 4.3).
Table 4.3: Spending on mobile phone charges
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Valid
<100,000
212
25.4
25.4
25.4
100-300,000
406
48.6
48.6
74.0
300,000-500,000
147
17.6
17.6
91.6
>500,000
70
8.4
8.4
100.0
Total
835
100.0
100.0
The statistics in Table 4.3 are similar to the percentages in the NielsenMobile survey results (2009) with 73% of mobile phone users having medium spending levels and only 13% having high spending levels.
The survey results also showed that up to 31% ~ nearly one-third of respondents said they sent more than 10 SMS messages/day, meaning that on average they sent 1 SMS message for every working hour. Those with an average SMS message volume (from 3 to 10 messages/day) accounted for 51.1% and those with a low SMS message volume (less than 3 messages/day) accounted for 17%. (Table 4.4)
Table 4.4: Number of SMS messages sent per day
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Valid
<3 news
142
17.0
17.0
17.0
3-10 news
427
51.1
51.1
68.1
>10 news
266
31.9
31.9
100.0
Total
835
100.0
100.0
Similar to sending messages, those with an average message receiving rate (from 3-10 messages/day) accounted for the highest percentage of ~ 55%, followed by those with a high number of messages (over 10 messages/day) ~ 24% and those with a low number of messages received daily (under 3 messages/day) remained at the bottom with 21%. (Table 4.5)
Table 4.5: Number of SMS messages received per day
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Valid
<3 news
175
21.0
21.0
21.0
3-10 news
436
55.0
55.0
76.0
>10 news
197
24.0
24.0
100.0
Total
835
100.0
100.0
When comparing the data of the two result tables 4.4 and 4.5, we can see the reasonableness between the ratio of the number of messages sent and the number of messages received daily by the interview participants.
4.3 Current status of SMS advertising and Mobile Marketing
According to the interview results, in the 3 months from the time of the survey and before, 94% of respondents, equivalent to 785 people, said they received advertising messages, while only a very small percentage of 6% (only 50 people) did not receive advertising messages (Table 4.6).
Table 4.6: Percentage of people receiving advertising messages in the last 3 months
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Valid
Have
785
94.0
94.0
94.0
Are not
50
6.0
6.0
100.0
Total
835
100.0
100.0
The results of Table 4.6 show that consumers in the inner city of Hanoi are very familiar with advertising messages. This result is also the basis for assessing the knowledge, experience and understanding of the respondents in the interview. This is also one of the important factors determining the accuracy of the survey results.
In addition, most respondents said they had received promotional messages, but only 24% of them had ever taken the action of registering to receive promotional messages, while 76% of the remaining respondents did not register to receive promotional messages but still received promotional messages every day. This is the first sign indicating the weaknesses and shortcomings of lax management of this activity in Vietnam. (Table 4.7)
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Mobile Phone Usage in Hanoi Inner City Area
zt2i3t4l5ee
zt2a3gsconsumer,consumption,consumer behavior,marketing,mobile marketing
zt2a3ge
zc2o3n4t5e6n7ts
- Test the relationship between demographic variables and consumer behavior for Mobile Marketing activities
The analysis method used is the Chi-square test (χ2), with statistical hypotheses H0 and H1 and significance level α = 0.05. In case the P index (p-value) or Sig. index in SPSS has a value less than or equal to the significance level α, the hypothesis H0 is rejected and vice versa. With this testing procedure, the study can evaluate the difference in behavioral trends between demographic groups.
CHAPTER 4
RESEARCH RESULTS
During two months, 1,100 survey questionnaires were distributed to mobile phone users in the inner city of Hanoi using various methods such as direct interviews, sending via email or using questionnaires designed on the Internet. At the end of the survey, after checking and eliminating erroneous questionnaires, the study collected 858 complete questionnaires, equivalent to a rate of about 78%. In addition, the research subjects of the thesis are only people who are using mobile phones, so people who do not use mobile phones are not within the scope of the thesis, therefore, the questionnaires with the option of not using mobile phones were excluded from the scope of analysis. The number of suitable survey questionnaires included in the statistical analysis was 835.
4.1 Demographic characteristics of the sample
The structure of the survey sample is divided and statistically analyzed according to criteria such as gender, age, occupation, education level and personal income. (Detailed statistical table in Appendix 6)
- Gender structure: Of the 835 completed questionnaires, 49.8% of respondents were male, equivalent to 416 people, and 50.2% were female, equivalent to 419 people. The survey results of the study are completely consistent with the gender ratio in the population structure of Vietnam in general and Hanoi in particular (Male/Female: 49/51).
- Age structure: 36.6% of respondents are <23 years old, equivalent to 306 people. People from 23-34 years old
accounting for the highest proportion: 44.8% equivalent to 374 people, people aged 35-45 and >45 are 70 and 85 people equivalent to 8.4% and 10.2% respectively. Looking at the results of this survey, we can see that the young people - youth account for a large proportion of the total number of people participating in the survey. Meanwhile, the middle-aged people including two age groups of 35 - 45 and >45 have a low rate of participation in the survey. This is completely consistent with the reality when Mobile Marketing is identified as a Marketing service aimed at young people (people under 35 years old).
- Structure by educational level: among 835 valid responses, 541 respondents had university degrees, accounting for the highest proportion of ~ 75%, 102 had secondary school degrees, ~ 13.1%, and 93 had post-graduate degrees, ~ 11.9%.
- Occupational structure: office workers and civil servants are the group with the highest rate of participation with 39.4%, followed by students with 36.6%. Self-employed people account for 12%, retired housewives are 7.8% and other occupational groups account for 4.2%. The survey results show that the student group has the same rate as the group aged <23 at 36.6%. This shows the accuracy of the survey data. In addition, the survey results distributed by occupational criteria have a rate almost similar to the sample division rate in chapter 3. Therefore, it can be concluded that the survey data is suitable for use in analysis activities.
- Income structure: the group with income from 3 to 5 million has the highest rate with 39% of the total number of respondents. This is consistent with the income structure of Hanoi people and corresponds to the average income of the group of civil servants and office workers. Those
People with no income account for 23%, income under 3 million VND accounts for 13% and income over 5 million VND accounts for 25%.
4.2 Mobile phone usage in Hanoi inner city area
According to the survey results, most respondents said they had used the phone for more than 1 year, specifically: 68.4% used mobile phones from 4 to 10 years, 23.2% used from 1 to 3 years, 7.8% used for more than 10 years. Those who used mobile phones for less than 1 year accounted for only a very small proportion of ~ 0.6%. (Table 4.1)
Table 4.1: Time spent using mobile phones
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Alid
<1 year
5
.6
.6
.6
1-3 years
194
23.2
23.2
23.8
4-10 years
571
68.4
68.4
92.2
>10 years
65
7.8
7.8
100.0
Total
835
100.0
100.0
The survey indexes on the time of using mobile phones of consumers in the inner city of Hanoi are very impressive for a developing country like Vietnam and also prove that Vietnamese consumers have a lot of experience using this high-tech device. Moreover, with the majority of consumers surveyed having a relatively long time of use (4-10 years), it partly proves that mobile phones have become an important and essential item in people's daily lives.
When asked about the mobile phone network they are using, 31% of respondents said they are using the network of Vietel company, 29% use the network of
of Mobifone company, 27% use Vinaphone company's network and 13% use networks of other providers such as E-VN telecom, S-fone, Beeline, Vietnammobile. (Figure 4.1).
Figure 4.1: Mobile phone network in use
Compared with the announced market share of mobile telecommunications service providers in Vietnam (Vietel: 36%, Mobifone: 29%, Vinaphone: 28%, the remaining networks: 7%), we see that the survey results do not have many differences. However, the statistics show that there is a difference in the market share of other networks because the Hanoi market is one of the two main markets of small networks, so their market share in this area will certainly be higher than that of the whole country.
According to a report by NielsenMobile (2009) [8], the number of prepaid mobile phone subscribers in Hanoi accounts for 95% of the total number of subscribers, however, the results of this survey show that the percentage of prepaid subscribers has decreased by more than 20%, only at 70.8%. On the contrary, the number of postpaid subscribers tends to increase from 5% in 2009 to 19.2%. Those who are simultaneously using both types of subscriptions account for 10%. (Table 4.2).
Table 4.2: Types of mobile phone subscribers
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Valid
Prepay
591
70.8
70.8
70.8
Pay later
160
19.2
19.2
89.9
Both of the above
84
10.1
10.1
100.0
Total
835
100.0
100.0
The above figures show the change in the psychology and consumption habits of Vietnamese consumers towards mobile telecommunications services, when the use of prepaid subscriptions and junk SIMs is replaced by the use of two types of subscriptions for different purposes and needs or switching to postpaid subscriptions to enjoy better customer care services.
In addition, the majority of respondents have an average spending level for mobile phone services from 100 to 300 thousand VND (406 ~ 48.6% of total respondents). The high spending level (> 500 thousand VND) is the spending level with the lowest number of people with only 8.4%, on the contrary, the low spending level (under 100 thousand VND) accounts for the second highest proportion among the groups of respondents with 25.4%. People with low spending levels mainly fall into the group of students and retirees/housewives - those who have little need to use or mainly use promotional SIM cards. (Table 4.3).
Table 4.3: Spending on mobile phone charges
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Valid
<100,000
212
25.4
25.4
25.4
100-300,000
406
48.6
48.6
74.0
300,000-500,000
147
17.6
17.6
91.6
>500,000
70
8.4
8.4
100.0
Total
835
100.0
100.0
The statistics in Table 4.3 are similar to the percentages in the NielsenMobile survey results (2009) with 73% of mobile phone users having medium spending levels and only 13% having high spending levels.
The survey results also showed that up to 31% ~ nearly one-third of respondents said they sent more than 10 SMS messages/day, meaning that on average they sent 1 SMS message for every working hour. Those with an average SMS message volume (from 3 to 10 messages/day) accounted for 51.1% and those with a low SMS message volume (less than 3 messages/day) accounted for 17%. (Table 4.4)
Table 4.4: Number of SMS messages sent per day
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Valid
<3 news
142
17.0
17.0
17.0
3-10 news
427
51.1
51.1
68.1
>10 news
266
31.9
31.9
100.0
Total
835
100.0
100.0
Similar to sending messages, those with an average message receiving rate (from 3-10 messages/day) accounted for the highest percentage of ~ 55%, followed by those with a high number of messages (over 10 messages/day) ~ 24% and those with a low number of messages received daily (under 3 messages/day) remained at the bottom with 21%. (Table 4.5)
Table 4.5: Number of SMS messages received per day
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Valid
<3 news
175
21.0
21.0
21.0
3-10 news
436
55.0
55.0
76.0
>10 news
197
24.0
24.0
100.0
Total
835
100.0
100.0
When comparing the data of the two result tables 4.4 and 4.5, we can see the reasonableness between the ratio of the number of messages sent and the number of messages received daily by the interview participants.
4.3 Current status of SMS advertising and Mobile Marketing
According to the interview results, in the 3 months from the time of the survey and before, 94% of respondents, equivalent to 785 people, said they received advertising messages, while only a very small percentage of 6% (only 50 people) did not receive advertising messages (Table 4.6).
Table 4.6: Percentage of people receiving advertising messages in the last 3 months
Frequency
Ratio (%)
Valid Percentage
Cumulative Percentage
Valid
Have
785
94.0
94.0
94.0
Are not
50
6.0
6.0
100.0
Total
835
100.0
100.0
The results of Table 4.6 show that consumers in the inner city of Hanoi are very familiar with advertising messages. This result is also the basis for assessing the knowledge, experience and understanding of the respondents in the interview. This is also one of the important factors determining the accuracy of the survey results.
In addition, most respondents said they had received promotional messages, but only 24% of them had ever taken the action of registering to receive promotional messages, while 76% of the remaining respondents did not register to receive promotional messages but still received promotional messages every day. This is the first sign indicating the weaknesses and shortcomings of lax management of this activity in Vietnam. (Table 4.7)
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![Qos Assurance Methods for Multimedia Communications
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low. The EF PHB requires a sufficiently large number of output ports to provide low delay, low loss, and low jitter.
EF PHBs can be implemented if the output ports bandwidth is sufficiently large, combined with small buffer sizes and other network resources dedicated to EF packets, to allow the routers service rate for EF packets on an output port to exceed the arrival rate λ of packets at that port.
This means that packets with PHB EF are considered with a pre-allocated amount of output bandwidth and a priority that ensures minimum loss, minimum delay and minimum jitter before being put into operation.
PHB EF is suitable for channel simulation, leased line simulation, and real-time services such as voice, video without compromising on high loss, delay and jitter values.
Figure 2.10 Example of EF installation
Figure 2.10 shows an example of an EF PHB implementation. This is a simple priority queue scheduling technique. At the edges of the DS domain, EF packet traffic is prioritized according to the values agreed upon by the SLA. The EF queue in the figure needs to output packets at a rate higher than the packet arrival rate λ. To provide an EF PHB over an end-to-end DS domain, bandwidth at the output ports of the core routers needs to be allocated in advance to ensure the requirement μ > λ. This can be done by a pre-configured provisioning process. In the figure, EF packets are placed in the priority queue (the upper queue). With such a length, the queue can operate with μ > λ.
Since EF was primarily used for real-time services such as voice and video, and since real-time services use UDP instead of TCP, RED is generally
not suitable for EF queues because applications using UDP will not respond to random packet drop and RED will strip unnecessary packets.
2.2.4.2 Assured Forwarding (AF) PHB
PHB AF is defined by RFC 2597. The purpose of PHB AF is to deliver packets reliably and therefore delay and jitter are considered less important than packet loss. PHB AF is suitable for non-real-time services such as applications using TCP. PHB AF first defines four classes: AF1, AF2, AF3, AF4. For each of these AF classes, packets are then classified into three subclasses with three distinct priority levels.
Table 2.8 shows the four AF classes and 12 AF subclasses and the DSCP values for the 12 AF subclasses defined by RFC 2597. RFC 2597 also allows for more than three separate priority levels to be added for internal use. However, these separate priority levels will only have internal significance.
PHB Class
PHB Subclass
Package type
DSCP
AF4
AF41
Short
100010
AF42
Medium
100100
AF43
High
100110
AF3
AF31
Short
011010
AF32
Medium
011100
AF33
High
011110
AF2
AF21
Short
010010
AF22
Medium
010100
AF23
High
010110
AF1
AF11
Short
001010
AF12
Medium
001100
AF13
High
001110
Table 2.8 AF DSCPs
The AF PHB ensures that packets are forwarded with a high probability of delivery to the destination within the bounds of the rate agreed upon in an SLA. If AF traffic at an ingress port exceeds the pre-priority rate, which is considered non-compliant or “out of profile”, the excess packets will not be delivered to the destination with the same probability as the packets belonging to the defined traffic or “in profile” packets. When there is network congestion, the out of profile packets are dropped before the in profile packets are dropped.
When service levels are defined using AF classes, different quantity and quality between AF classes can be realized by allocating different amounts of bandwidth and buffer space to the four AF classes. Unlike
EF, most AF traffic is non-real-time traffic using TCP, and the RED queue management strategy is an AQM (Adaptive Queue Management) strategy suitable for use in AF PHBs. The four AF PHB layers can be implemented as four separate queues. The output port bandwidth is divided into four AF queues. For each AF queue, packets are marked with three “colors” corresponding to three separate priority levels.
In addition to the 32 DSCP 1 groups defined in Table 2.8, 21 DSCPs have been standardized as follows: one for PHB EF, 12 for PHB AF, and 8 for CSCP. There are 11 DSCP 1 groups still available for other standards.
2.2.5.Example of Differentiated Services
We will look at an example of the Differentiated Service model and mechanism of operation. The architecture of Differentiated Service consists of two basic sets of functions:
Edge functions: include packet classification and traffic conditioning. At the inbound edge of the network, incoming packets are marked. In particular, the DS field in the packet header is set to a certain value. For example, in Figure 2.12, packets sent from H1 to H3 are marked at R1, while packets from H2 to H4 are marked at R2. The labels on the received packets identify the service class to which they belong. Different traffic classes receive different services in the core network. The RFC definition uses the term behavior aggregate rather than the term traffic class. After being marked, a packet can be forwarded immediately into the network, delayed for a period of time before being forwarded, or dropped. We will see that there are many factors that affect how a packet is marked, and whether it is forwarded immediately, delayed, or dropped.
Figure 2.12 DiffServ Example
Core functionality: When a DS-marked packet arrives at a Diffservcapable router, the packet is forwarded to the next router based on
Per-hop behavior is associated with packet classes. Per-hop behavior affects router buffers and the bandwidth shared between competing classes. An important principle of the Differentiated Service architecture is that a routers per-hop behavior is based only on the packets marking or the class to which it belongs. Therefore, if packets sent from H1 to H3 as shown in the figure receive the same marking as packets from H2 to H4, then the network routers treat the packets exactly the same, regardless of whether the packet originated from H1 or H2. For example, R3 does not distinguish between packets from h1 and H2 when forwarding packets to R4. Therefore, the Differentiated Service architecture avoids the need to maintain router state about separate source-destination pairs, which is important for network scalability.
Chapter Conclusion
Chapter 2 has presented and clarified two main models of deploying and installing quality of service in IP networks. While the traditional best-effort model has many disadvantages, later models such as IntServ and DiffServ have partly solved the problems that best-effort could not solve. IntServ follows the direction of ensuring quality of service for each separate flow, it is built similar to the circuit switching model with the use of the RSVP resource reservation protocol. IntSer is suitable for services that require fixed bandwidth that is not shared such as VoIP services, multicast TV services. However, IntSer has disadvantages such as using a lot of network resources, low scalability and lack of flexibility. DiffServ was born with the idea of solving the disadvantages of the IntServ model.
DiffServ follows the direction of ensuring quality based on the principle of hop-by-hop behavior based on the priority of marked packets. The policy for different types of traffic is decided by the administrator and can be changed according to reality, so it is very flexible. DiffServ makes better use of network resources, avoiding idle bandwidth and processing capacity on routers. In addition, the DifServ model can be deployed on many independent domains, so the ability to expand the network becomes easy.
Chapter 3: METHODS TO ENSURE QoS FOR MULTIMEDIA COMMUNICATIONS
In packet-switched networks, different packet flows often have to share the transmission medium all the way to the destination station. To ensure the fair and efficient allocation of bandwidth to flows, appropriate serving mechanisms are required at network nodes, especially at gateways or routers, where many different data flows often pass through. The scheduler is responsible for serving packets of the selected flow and deciding which packet will be served next. Here, a flow is understood as a set of packets belonging to the same priority class, or originating from the same source, or having the same source and destination addresses, etc.
In normal state when there is no congestion, packets will be sent as soon as they are delivered. In case of congestion, if QoS assurance methods are not applied, prolonged congestion can cause packet drops, affecting service quality. In some cases, congestion is prolonged and widespread in the network, which can easily lead to the network being frozen, or many packets being dropped, seriously affecting service quality.
Therefore, in this chapter, in sections 3.2 and 3.3, we introduce some typical network traffic load monitoring techniques to predict and prevent congestion before it occurs through the measure of dropping (removing) packets early when there are signs of impending congestion.
3.1. DropTail method
DropTail is a simple, traditional queue management method based on FIFO mechanism. All incoming packets are placed in the queue, when the queue is full, the later packets are dropped.
Due to its simplicity and ease of implementation, DropTail has been used for many years on Internet router systems. However, this algorithm has the following disadvantages:
− Cannot avoid the phenomenon of “Lock out”: Occurs when 1 or several traffic streams monopolize the queue, making packets of other connections unable to pass through the router. This phenomenon greatly affects reliable transmission protocols such as TCP. According to the anti-congestion algorithm, when locked out, the TCP connection stream will reduce the window size and reduce the packet transmission speed exponentially.
− Can cause Global Synchronization: This is the result of a severe “Lock out” phenomenon. Some neighboring routers have their queues monopolized by a number of connections, causing a series of other TCP connections to be unable to pass through and simultaneously reducing the transmission speed. After those monopolized connections are temporarily suspended,
Once the queue is cleared, it takes a considerable amount of time for TCP connections to return to their original speed.
− Full Queue phenomenon: Data transmitted on the Internet often has an explosion, packets arriving at the router are often in clusters rather than in turn. Therefore, the operating mechanism of DropTail makes the queue easily full for a long period of time, leading to the average delay time of large packets. To avoid this phenomenon, with DropTail, the only way is to increase the routers buffer, this method is very expensive and ineffective.
− No QoS guarantee: With the DropTail mechanism, there is no way to prioritize important packets to be transmitted through the router earlier when all are in the queue. Meanwhile, with multimedia communication, ensuring connection and stable speed is extremely important and the DropTail algorithm cannot satisfy.
The problem of choosing the buffer size of the routers in the network is to “absorb” short bursts of traffic without causing too much queuing delay. This is necessary in bursty data transmission. The queue size determines the size of the packet bursts (traffic spikes) that we want to be able to transmit without being dropped at the routers.
In IP-based application networks, packet dropping is an important mechanism for indirectly reporting congestion to end stations. A solution that prevents router queues from filling up while reducing the packet drop rate is called dynamic queue management.
3.2. Random elimination method – RED
3.2.1 Overview
RED (Random Early Detection of congestion; Random Early Drop) is one of the first AQM algorithms proposed in 1993 by Sally Floyd and Van Jacobson, two scientists at the Lawrence Berkeley Laboratory of the University of California, USA. Due to its outstanding advantages compared to previous queue management algorithms, RED has been widely installed and deployed on the Internet.
The most fundamental point of their work is that the most effective place to detect congestion and react to it is at the gateway or router.
Source entities (senders) can also do this by estimating end-to-end delay, throughput variability, or the rate of packet retransmissions due to drop. However, the sender and receiver view of a particular connection cannot tell which gateways on the network are congested, and cannot distinguish between propagation delay and queuing delay. Only the gateway has a true view of the state of the queue, the link share of the connections passing through it at any given time, and the quality of service requirements of the
traffic flows. The RED gateway monitors the average queue length, which detects early signs of impending congestion (average queue length exceeding a predetermined threshold) and reacts appropriately in one of two ways:
− Drop incoming packets with a certain probability, to indirectly inform the source of congestion, the source needs to reduce the transmission rate to keep the queue from filling up, maintaining the ability to absorb incoming traffic spikes.
− Mark “congestion” with a certain probability in the ECN field in the header of TCP packets to notify the source (the receiving entity will copy this bit into the acknowledgement packet).
Figure 3. 1 RED algorithm
The main goal of RED is to avoid congestion by keeping the average queue size within a sufficiently small and stable region, which also means keeping the queuing delay sufficiently small and stable. Achieving this goal also helps: avoid global synchronization, not resist bursty traffic flows (i.e. flows with low average throughput but high volatility), and maintain an upper bound on the average queue size even in the absence of cooperation from transport layer protocols.
To achieve the above goals, RED gateways must do the following:
− The first is to detect congestion early and react appropriately to keep the average queue size small enough to keep the network operating in the low latency, high throughput region, while still allowing the queue size to fluctuate within a certain range to absorb short-term fluctuations. As discussed above, the gateway is the most appropriate place to detect congestion and is also the most appropriate place to decide which specific connection to report congestion to.
− The second thing is to notify the source of congestion. This is done by marking and notifying the source to reduce traffic. Normally the RED gateway will randomly drop packets. However, if congestion
If congestion is detected before the queue is full, it should be combined with packet marking to signal congestion. The RED gateway has two options: drop or mark; where marking is done by marking the ECN field of the packet with a certain probability, to signal the source to reduce the traffic entering the network.
− An important goal that RED gateways need to achieve is to avoid global synchronization and not to resist traffic flows that have a sudden characteristic. Global synchronization occurs when all connections simultaneously reduce their transmission window size, leading to a severe drop in throughput at the same time. On the other hand, Drop Tail or Random Drop strategies are very sensitive to sudden flows; that is, the gateway queue will often overflow when packets from these flows arrive. To avoid these two phenomena, gateways can use special algorithms to detect congestion and decide which connections will be notified of congestion at the gateway. The RED gateway randomly selects incoming packets to mark; with this method, the probability of marking a packet from a particular connection is proportional to the connections shared bandwidth at the gateway.
− Another goal is to control the average queue size even without cooperation from the source entities. This can be done by dropping packets when the average size exceeds an upper threshold (instead of marking it). This approach is necessary in cases where most connections have transmission times that are less than the round-trip time, or where the source entities are not able to reduce traffic in response to marking or dropping packets (such as UDP flows).
3.2.2 Algorithm
This section describes the algorithm for RED gateways. RED gateways calculate the average queue size using a low-pass filter. This average queue size is compared with two thresholds: minth and maxth. When the average queue size is less than the lower threshold, no incoming packets are marked or dropped; when the average queue size is greater than the upper threshold, all incoming packets are dropped. When the average queue size is between minth and maxth, each incoming packet is marked or dropped with a probability pa, where pa is a function of the average queue size avg; the probability of marking or dropping a packet for a particular connection is proportional to the bandwidth share of that connection at the gateway. The general algorithm for a RED gateway is described as follows: [5]
For each packet arrival
Caculate the average queue size avg If minth ≤ avg < maxth
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Qos Assurance Methods for Multimedia Communications
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low. The EF PHB requires a sufficiently large number of output ports to provide low delay, low loss, and low jitter.
EF PHBs can be implemented if the output port's bandwidth is sufficiently large, combined with small buffer sizes and other network resources dedicated to EF packets, to allow the router's service rate for EF packets on an output port to exceed the arrival rate λ of packets at that port.
This means that packets with PHB EF are considered with a pre-allocated amount of output bandwidth and a priority that ensures minimum loss, minimum delay and minimum jitter before being put into operation.
PHB EF is suitable for channel simulation, leased line simulation, and real-time services such as voice, video without compromising on high loss, delay and jitter values.
Figure 2.10 Example of EF installation
Figure 2.10 shows an example of an EF PHB implementation. This is a simple priority queue scheduling technique. At the edges of the DS domain, EF packet traffic is prioritized according to the values agreed upon by the SLA. The EF queue in the figure needs to output packets at a rate higher than the packet arrival rate λ. To provide an EF PHB over an end-to-end DS domain, bandwidth at the output ports of the core routers needs to be allocated in advance to ensure the requirement μ > λ. This can be done by a pre-configured provisioning process. In the figure, EF packets are placed in the priority queue (the upper queue). With such a length, the queue can operate with μ > λ.
Since EF was primarily used for real-time services such as voice and video, and since real-time services use UDP instead of TCP, RED is generally
not suitable for EF queues because applications using UDP will not respond to random packet drop and RED will strip unnecessary packets.
2.2.4.2 Assured Forwarding (AF) PHB
PHB AF is defined by RFC 2597. The purpose of PHB AF is to deliver packets reliably and therefore delay and jitter are considered less important than packet loss. PHB AF is suitable for non-real-time services such as applications using TCP. PHB AF first defines four classes: AF1, AF2, AF3, AF4. For each of these AF classes, packets are then classified into three subclasses with three distinct priority levels.
Table 2.8 shows the four AF classes and 12 AF subclasses and the DSCP values for the 12 AF subclasses defined by RFC 2597. RFC 2597 also allows for more than three separate priority levels to be added for internal use. However, these separate priority levels will only have internal significance.
PHB Class
PHB Subclass
Package type
DSCP
AF4
AF41
Short
100010
AF42
Medium
100100
AF43
High
100110
AF3
AF31
Short
011010
AF32
Medium
011100
AF33
High
011110
AF2
AF21
Short
010010
AF22
Medium
010100
AF23
High
010110
AF1
AF11
Short
001010
AF12
Medium
001100
AF13
High
001110
Table 2.8 AF DSCPs
The AF PHB ensures that packets are forwarded with a high probability of delivery to the destination within the bounds of the rate agreed upon in an SLA. If AF traffic at an ingress port exceeds the pre-priority rate, which is considered non-compliant or “out of profile”, the excess packets will not be delivered to the destination with the same probability as the packets belonging to the defined traffic or “in profile” packets. When there is network congestion, the out of profile packets are dropped before the in profile packets are dropped.
When service levels are defined using AF classes, different quantity and quality between AF classes can be realized by allocating different amounts of bandwidth and buffer space to the four AF classes. Unlike
EF, most AF traffic is non-real-time traffic using TCP, and the RED queue management strategy is an AQM (Adaptive Queue Management) strategy suitable for use in AF PHBs. The four AF PHB layers can be implemented as four separate queues. The output port bandwidth is divided into four AF queues. For each AF queue, packets are marked with three “colors” corresponding to three separate priority levels.
In addition to the 32 DSCP 1 groups defined in Table 2.8, 21 DSCPs have been standardized as follows: one for PHB EF, 12 for PHB AF, and 8 for CSCP. There are 11 DSCP 1 groups still available for other standards.
2.2.5.Example of Differentiated Services
We will look at an example of the Differentiated Service model and mechanism of operation. The architecture of Differentiated Service consists of two basic sets of functions:
Edge functions: include packet classification and traffic conditioning. At the inbound edge of the network, incoming packets are marked. In particular, the DS field in the packet header is set to a certain value. For example, in Figure 2.12, packets sent from H1 to H3 are marked at R1, while packets from H2 to H4 are marked at R2. The labels on the received packets identify the service class to which they belong. Different traffic classes receive different services in the core network. The RFC definition uses the term behavior aggregate rather than the term traffic class. After being marked, a packet can be forwarded immediately into the network, delayed for a period of time before being forwarded, or dropped. We will see that there are many factors that affect how a packet is marked, and whether it is forwarded immediately, delayed, or dropped.
Figure 2.12 DiffServ Example
Core functionality: When a DS-marked packet arrives at a Diffservcapable router, the packet is forwarded to the next router based on
Per-hop behavior is associated with packet classes. Per-hop behavior affects router buffers and the bandwidth shared between competing classes. An important principle of the Differentiated Service architecture is that a router's per-hop behavior is based only on the packet's marking or the class to which it belongs. Therefore, if packets sent from H1 to H3 as shown in the figure receive the same marking as packets from H2 to H4, then the network routers treat the packets exactly the same, regardless of whether the packet originated from H1 or H2. For example, R3 does not distinguish between packets from h1 and H2 when forwarding packets to R4. Therefore, the Differentiated Service architecture avoids the need to maintain router state about separate source-destination pairs, which is important for network scalability.
Chapter Conclusion
Chapter 2 has presented and clarified two main models of deploying and installing quality of service in IP networks. While the traditional best-effort model has many disadvantages, later models such as IntServ and DiffServ have partly solved the problems that best-effort could not solve. IntServ follows the direction of ensuring quality of service for each separate flow, it is built similar to the circuit switching model with the use of the RSVP resource reservation protocol. IntSer is suitable for services that require fixed bandwidth that is not shared such as VoIP services, multicast TV services. However, IntSer has disadvantages such as using a lot of network resources, low scalability and lack of flexibility. DiffServ was born with the idea of solving the disadvantages of the IntServ model.
DiffServ follows the direction of ensuring quality based on the principle of hop-by-hop behavior based on the priority of marked packets. The policy for different types of traffic is decided by the administrator and can be changed according to reality, so it is very flexible. DiffServ makes better use of network resources, avoiding idle bandwidth and processing capacity on routers. In addition, the DifServ model can be deployed on many independent domains, so the ability to expand the network becomes easy.
Chapter 3: METHODS TO ENSURE QoS FOR MULTIMEDIA COMMUNICATIONS
In packet-switched networks, different packet flows often have to share the transmission medium all the way to the destination station. To ensure the fair and efficient allocation of bandwidth to flows, appropriate serving mechanisms are required at network nodes, especially at gateways or routers, where many different data flows often pass through. The scheduler is responsible for serving packets of the selected flow and deciding which packet will be served next. Here, a flow is understood as a set of packets belonging to the same priority class, or originating from the same source, or having the same source and destination addresses, etc.
In normal state when there is no congestion, packets will be sent as soon as they are delivered. In case of congestion, if QoS assurance methods are not applied, prolonged congestion can cause packet drops, affecting service quality. In some cases, congestion is prolonged and widespread in the network, which can easily lead to the network being "frozen", or many packets being dropped, seriously affecting service quality.
Therefore, in this chapter, in sections 3.2 and 3.3, we introduce some typical network traffic load monitoring techniques to predict and prevent congestion before it occurs through the measure of dropping (removing) packets early when there are signs of impending congestion.
3.1. DropTail method
DropTail is a simple, traditional queue management method based on FIFO mechanism. All incoming packets are placed in the queue, when the queue is full, the later packets are dropped.
Due to its simplicity and ease of implementation, DropTail has been used for many years on Internet router systems. However, this algorithm has the following disadvantages:
− Cannot avoid the phenomenon of “Lock out”: Occurs when 1 or several traffic streams monopolize the queue, making packets of other connections unable to pass through the router. This phenomenon greatly affects reliable transmission protocols such as TCP. According to the anti-congestion algorithm, when locked out, the TCP connection stream will reduce the window size and reduce the packet transmission speed exponentially.
− Can cause Global Synchronization: This is the result of a severe “Lock out” phenomenon. Some neighboring routers have their queues monopolized by a number of connections, causing a series of other TCP connections to be unable to pass through and simultaneously reducing the transmission speed. After those monopolized connections are temporarily suspended,
Once the queue is cleared, it takes a considerable amount of time for TCP connections to return to their original speed.
− Full Queue phenomenon: Data transmitted on the Internet often has an explosion, packets arriving at the router are often in clusters rather than in turn. Therefore, the operating mechanism of DropTail makes the queue easily full for a long period of time, leading to the average delay time of large packets. To avoid this phenomenon, with DropTail, the only way is to increase the router's buffer, this method is very expensive and ineffective.
− No QoS guarantee: With the DropTail mechanism, there is no way to prioritize important packets to be transmitted through the router earlier when all are in the queue. Meanwhile, with multimedia communication, ensuring connection and stable speed is extremely important and the DropTail algorithm cannot satisfy.
The problem of choosing the buffer size of the routers in the network is to “absorb” short bursts of traffic without causing too much queuing delay. This is necessary in bursty data transmission. The queue size determines the size of the packet bursts (traffic spikes) that we want to be able to transmit without being dropped at the routers.
In IP-based application networks, packet dropping is an important mechanism for indirectly reporting congestion to end stations. A solution that prevents router queues from filling up while reducing the packet drop rate is called dynamic queue management.
3.2. Random elimination method – RED
3.2.1 Overview
RED (Random Early Detection of congestion; Random Early Drop) is one of the first AQM algorithms proposed in 1993 by Sally Floyd and Van Jacobson, two scientists at the Lawrence Berkeley Laboratory of the University of California, USA. Due to its outstanding advantages compared to previous queue management algorithms, RED has been widely installed and deployed on the Internet.
The most fundamental point of their work is that the most effective place to detect congestion and react to it is at the gateway or router.
Source entities (senders) can also do this by estimating end-to-end delay, throughput variability, or the rate of packet retransmissions due to drop. However, the sender and receiver view of a particular connection cannot tell which gateways on the network are congested, and cannot distinguish between propagation delay and queuing delay. Only the gateway has a true view of the state of the queue, the link share of the connections passing through it at any given time, and the quality of service requirements of the
traffic flows. The RED gateway monitors the average queue length, which detects early signs of impending congestion (average queue length exceeding a predetermined threshold) and reacts appropriately in one of two ways:
− Drop incoming packets with a certain probability, to indirectly inform the source of congestion, the source needs to reduce the transmission rate to keep the queue from filling up, maintaining the ability to absorb incoming traffic spikes.
− Mark “congestion” with a certain probability in the ECN field in the header of TCP packets to notify the source (the receiving entity will copy this bit into the acknowledgement packet).
Figure 3. 1 RED algorithm
The main goal of RED is to avoid congestion by keeping the average queue size within a sufficiently small and stable region, which also means keeping the queuing delay sufficiently small and stable. Achieving this goal also helps: avoid global synchronization, not resist bursty traffic flows (i.e. flows with low average throughput but high volatility), and maintain an upper bound on the average queue size even in the absence of cooperation from transport layer protocols.
To achieve the above goals, RED gateways must do the following:
− The first is to detect congestion early and react appropriately to keep the average queue size small enough to keep the network operating in the low latency, high throughput region, while still allowing the queue size to fluctuate within a certain range to absorb short-term fluctuations. As discussed above, the gateway is the most appropriate place to detect congestion and is also the most appropriate place to decide which specific connection to report congestion to.
− The second thing is to notify the source of congestion. This is done by marking and notifying the source to reduce traffic. Normally the RED gateway will randomly drop packets. However, if congestion
If congestion is detected before the queue is full, it should be combined with packet marking to signal congestion. The RED gateway has two options: drop or mark; where marking is done by marking the ECN field of the packet with a certain probability, to signal the source to reduce the traffic entering the network.
− An important goal that RED gateways need to achieve is to avoid global synchronization and not to resist traffic flows that have a sudden characteristic. Global synchronization occurs when all connections simultaneously reduce their transmission window size, leading to a severe drop in throughput at the same time. On the other hand, Drop Tail or Random Drop strategies are very sensitive to sudden flows; that is, the gateway queue will often overflow when packets from these flows arrive. To avoid these two phenomena, gateways can use special algorithms to detect congestion and decide which connections will be notified of congestion at the gateway. The RED gateway randomly selects incoming packets to mark; with this method, the probability of marking a packet from a particular connection is proportional to the connection's shared bandwidth at the gateway.
− Another goal is to control the average queue size even without cooperation from the source entities. This can be done by dropping packets when the average size exceeds an upper threshold (instead of marking it). This approach is necessary in cases where most connections have transmission times that are less than the round-trip time, or where the source entities are not able to reduce traffic in response to marking or dropping packets (such as UDP flows).
3.2.2 Algorithm
This section describes the algorithm for RED gateways. RED gateways calculate the average queue size using a low-pass filter. This average queue size is compared with two thresholds: minth and maxth. When the average queue size is less than the lower threshold, no incoming packets are marked or dropped; when the average queue size is greater than the upper threshold, all incoming packets are dropped. When the average queue size is between minth and maxth, each incoming packet is marked or dropped with a probability pa, where pa is a function of the average queue size avg; the probability of marking or dropping a packet for a particular connection is proportional to the bandwidth share of that connection at the gateway. The general algorithm for a RED gateway is described as follows: [5]
For each packet arrival
Caculate the average queue size avg If minth ≤ avg < maxth
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Identify Rating Levels and Rating Scales
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of the islanders. Therefore, this indicator will be divided into two sub-indicators:
a1. Natural tourism attractiveness a2. Cultural tourism attractiveness
b. Tourist capacity
The two island communes in Quan Lan have different capacities to receive tourists. Minh Chau Commune is home to many standard hotels and resorts, attracting high-income domestic and international tourists. Meanwhile, Quan Lan Commune has many motels mainly built and operated by local people, so the scale and quality are not high, and will be suitable for ordinary tourists such as students.
c. Time of exploitation of Quan Lan Island Commune:
Quan Lan tourism is seasonal due to weather and climate conditions and festivals only take place on certain days of the year, specifically in spring. In Quan Lan commune, the period from April to June and from September to November is considered the best time to visit Quan Lan because the cultural tourism activities are mainly associated with festivals taking place during this time.
Minh Chau island commune:
Tourism exploitation time is all year round, because this is a place with a number of tourist attractions with diverse ecosystems such as Bai Tu Long National Park Research Center, Tram forest, Turtle Laying Beach, so besides coming to the beach for tourism and vacation in the summer, Minh Chau will attract research groups to come for tourism combined with research at other times of the year.
d. Sustainability
The sustainability of ecotourism sites in Quan Lan and Minh Chau communes depends on the sensitivity of the ecosystems to climate changes.
landscape. In general, these tourist destinations have a fairly high level of sustainability, because they are natural ecosystems, planned and protected. However, if a large number of tourists gather at certain times, it can exceed the carrying capacity and affect the sustainability of the environment (polluted beaches, damaged trees, animals moving away from their habitats, etc.), then the sustainability of the above ecosystems (natural ecosystems, human ecosystems) will also be affected and become less sustainable.
e. Location and accessibility
Both island communes have ports to take tourists to visit from Van Don wharf:
- Quan Lan – Van Don traffic route:
Phuc Thinh – Viet Anh high-speed boat and Quang Minh high-speed boat, depart at 8am and 2pm from Van Don to Quan Lan, and at 7am and 1pm from Quan Lan to Van Don. There are also wooden boats departing at 7am and 1pm.
- Van Don - Minh Chau traffic route:
Chung Huong high-speed train, Minh Chau train, morning 7:30 and afternoon 13:30 from Van Don to Minh Chau, morning 6:30 and afternoon 13:00 from Minh Chau to Van Don.
f. Infrastructure
Despite receiving investment attention, the issue of infrastructure and technical facilities for tourism on Quan Lan Island is still an issue that needs to be resolved because it has a direct impact on the implementation of ecotourism activities. The minimum conditions for serving tourists such as accommodation, electricity, water, communication, especially medical services, and security work need to be given top priority. Ecotourism spots in Minh Chau commune are assessed to have better infrastructure and technical facilities for tourism because there are quite complete and synchronous conditions for serving tourists, meeting many needs of domestic and foreign tourists.
3.2.1.4. Determine assessment levels and assessment scales
Corresponding to the levels of each criterion, the index is the score of those levels in the order of 4, 3, 2, 1 decreasing according to the standard of each level: very attractive (4), attractive (3), average (2), less attractive (1).
3.2.1.5. Determining the coefficients of the criteria
For the assessment of DLST in the two communes of Quan Lan and Minh Chau islands, the students added evaluation coefficients to show the importance of the criteria and indicators as follows:
Coefficient 3 with criteria: Attractiveness, Exploitation time. These are the 2 most important criteria for attracting tourists to tourism in general and eco-tourism in particular, so they have the highest coefficient.
Coefficient 2 with criteria: Capacity, Infrastructure, Location and accessibility . Because the assessment area is an island commune of Van Don district, the above criteria are selected by the author with appropriate coefficients at the average level.
Coefficient 1 with criteria: Sustainability. Quan Lan has natural and human-made ecotourism sites, with high biodiversity and little impact from local human factors. Most of the ecotourism sites are still wild, so they are highly sustainable.
3.2.1.6. Results of DLST assessment on Quan Lan island
a. Assessment of the potential for natural tourism development
For Minh Chau commune:
+ Natural tourism attractiveness is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12.
+ Capacity is determined as average (2 points) and the coefficient is quite important (coefficient 2), then the score of Capacity criterion is 2 x 2 = 4.
+ Exploitation time is long (4 points), the most important coefficient (coefficient 3) so the score of the Exploitation time criterion is 4 x 3 = 12.
+ Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points
+ Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points.
+ Infrastructure is assessed as good (3 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 3 x 2 = 6 points.
The total score for evaluating DLST in Minh Chau commune according to 6 evaluation criteria is determined as: 12 + 4 + 12 + 4 + 4 + 6 = 42 points
Similar assessment for Quan Lan commune, we have the following table:
Table 3.3: Assessment of the potential for natural ecotourism development in Quan Lan and Minh Chau communes
Attractiveness of self-tourismof course
Capacity
Mining time
Sustainability
Location and accessibility
Infrastructure
Result
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
CommuneMinh Chau
12
12
4
8
12
12
4
4
4
8
6
8
42/52
Quan CommuneLan
6
12
6
8
9
12
4
4
4
8
4
8
33/52
b. Assessment of the potential for humanistic tourism development
For Quan Lan commune:
+ The attractiveness of human tourism is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12.
+ Capacity is determined to be large (3 points) and the coefficient is quite important (coefficient 2), then the score of the Capacity criterion is 3 x 2 = 6.
+ Mining time is average (3 points), the most important coefficient (coefficient 3) so the score of the Mining time criterion is 3 x 3 = 9.
+ Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points.
+ Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points.
+ Infrastructure is rated as average (2 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 2 x 2 = 4 points.
The total score for evaluating DLST in Quan Lan commune according to 6 evaluation criteria is determined as: 12 + 6 + 6 + 4 + 4 + 4 = 36 points.
Similar assessment with Minh Chau commune we have the following table:
Table 3.4: Assessment of the potential for developing humanistic eco-tourism in Quan Lan and Minh Chau communes
Attractiveness of human tourismliterature
Capacity
Mining time
Sustainability
Location and accessibility
Infrastructure
Result
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Quan CommuneLan
12
12
6
8
9
12
4
4
4
8
4
8
39/52
Minh CommuneChau
6
12
4
8
12
12
4
4
4
8
6
8
36/52
Basically, both Minh Chau and Quan Lan localities have quite favorable conditions for developing ecotourism. However, Quan Lan commune has more advantages to develop ecotourism in a humanistic direction, because this is an area with many famous historical relics such as Quan Lan Communal House, Quan Lan Pagoda, Temple worshiping the hero Tran Khanh Du, ... along with local festivals held annually such as the wind praying ceremony (March 15), Quan Lan festival (June 10-19); due to its location near the port and long exploitation time, the beaches in Quan Lan commune (especially Quan Lan beach) are no longer hygienic and clean to ensure the needs of tourists coming to relax and swim; this is also an area with many beautiful landscapes such as Got Beo wind pass, Ong Phong head, Voi Voi cave, but the ability to access these places is still very limited (dirt hill road, lots of gravel and rocks), especially during rainy and windy times; In addition, other natural resources such as mangrove forests and sea worms have not been really exploited for tourism purposes and ecotourism development. On the contrary, Minh Chau commune has more advantages in developing ecotourism in the direction of natural tourism, this is an area with diverse ecosystems such as at Rua De Beach, Bai Tu Long National Park Conservation Center...; Minh Chau beach is highly appreciated for its natural beauty and cleanliness, ranked in the top ten most beautiful beaches in Vietnam; Minh Chau commune is also home to Tram forest with a large area and a purity of up to 90%, suitable for building bridges through the forest (a very effective type of natural ecotourism currently applied by many countries) for tourists to sightsee, as well as for the purpose of studying and researching.
Figure 3.1: Thenmala Forest Bridge (India) Source: https://www.thenmalaecotourism.com/(August 21, 2019)
3.2.2. Using SWOT matrix to evaluate Quan Lan island tourism
General assessment of current tourism activities of Quan Lan island is shown through the following SWOT matrix:
Table 3.5: SWOT matrix evaluating tourism activities on Quan Lan island
Internal agent
Strengths- There is a lot of potential for tourism development, especially natural ecotourism and humanistic ecotourism.- The unskilled labor force is relatively abundant.- resource environmentunpolluted, still
Weaknesses- Poorly developed infrastructure, especially traffic routes to tourist destinations on the island.- The team of professional staff is still weak.- Tourism products in general
quite wild, originalintact
general and DLST in particularalone is monotonous.
External agents
Opportunity- Tourism is a key industry in the socio-economic development strategy of the province and Van Don economic zone.- Quan Lan was selected as a pilot area for eco-tourism development within the framework of the green growth project between Quang Ninh province and the Japanese organization JICA.- The flow of tourists and especially ecotourism in the world tends toincreasing
Challenge- Weather and climate change abnormally.- Competition in tourism products is increasingly fierce, especially with other localities in the province such as Ha Long, Mong Cai...- Awareness of tourists, especially domestic tourists, about ecotourism and nature conservation is not high.
Through summary analysis using SWOT matrix we see that:
To exploit strengths and take advantage of opportunities, it is necessary to:
- Diversify products and service types (build more tourism routes aimed at specific needs of tourists: experiential tourism immersed in nature, spiritual cultural tourism...)
- Effective exploitation of resources and differentiated products (natural resources and human resources)
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Number of Accommodation Establishments in the Country and the Central Region Through the Years 2011 - 2012
The uniformity (J') of the oribatid mite community between time points was generally not high, ranging from 0.65 ± 0.19 to 0.80 ± 0.21. (J') reached its highest at 24:00 on average with 0.80 ± 0.21, the highest was 0.95 and the lowest was 0.55, this index gradually decreased in the order 24:00 (0.80 ± 0.21) > 6:00 (0.78 ± 0.11) > 18:00 (0.75 ± 0.19) > 12:00 (0.65 ±
0.19). The average uniformity index (J') at different times was statistically significant (P<0.05).
4.5 Indicators
4
3.5
3
2.5
2
1.5
1
0.5
0
2.1
2.76
3.02
2.6
d
J'
H'
1-Lambda'
6:00 12:00 18:00 24:00 Day and night rhythm
Figure 3.20. Diversity of the oribatid mite community during the day-night cycle
The data show that in general, the reverse dominance of the community is not high, proving that there is a possibility of dominant species appearing in all four research periods, specifically:
The negative dominance (1 -λ) of the community fluctuated from 0.72 ± 0.16 to 0.80 ± 0.12, of which the highest was at 18:00, the average was 0.80 ± 0.12, the highest was 0.9, the lowest was 0.67, this index gradually decreased to 24:00 with 0.78 ± 0.16, the highest was 0.9, the lowest was 0.6. At 6:00, it reached 0.74 ± 0.08, the highest was 0.81, the lowest was 0.66. This index was lowest at 12:00 with 0.72 ± 0.16, the highest was 0.9, the lowest was 0.6. The average difference between the time points was statistically significant (P<0.05). Thus, the dominance index will be opposite to this value, highest at 12:00, or the possibility of the dominant species appearing at this time is very high, the dominance gradually decreases in the order 12:00 6:00 24:00 18:00, because the average inverse dominance index at the different times is statistically significant (P<0.05), so the dominance index is also statistically significant between the times.
Time
6:00
12:00
18:00
24h00
100
80
Cumulative Dominance%
60
40
20
0
1 10 100
Species rank
Figure 3.21. K-dominance curve showing species diversity during the day-night cycle
Figure 3.21 shows that the k-dominance curve in this graph has three times with the three lowest curves: 18:00 - 12:00 - 24:00, these are also the three times with the highest diversity (H'), specifically the highest average species diversity (H') at
At 18:00, the index reached 2.00 ± 0.33, the highest was 2.26, the lowest was 1.62. This index decreased in order, at 12:00, it reached 1.75 ± 0.56, the highest was 2.39 and the lowest was 1.37; at 24:00, the average was 1.63 ± 0.67, the highest was 2.35 and the lowest was 1.04, finally the species diversity level (H') reached the lowest value at 6:00 with an average of 1.59 ± 0.10, the highest was 1.67 and the lowest was 1.48. The average diversity index (H') between the different survey times was statistically significant (P<0.05).
Thus, 18:00 is the time with the highest level of species biodiversity and community richness. These values are lowest at 6:00, while the community stability is highest at 24:00.
3.4.3. Dominant species structure
The dominant species structure of the oribatid mite community in four time periods is shown in Table 3.15, recording 9 dominant oribatid mite species with a ratio ranging from 5.26% - 52.63% in four time periods, specifically:
At 6:00 a.m., there were six dominant and very dominant species of oribatid mites, accounting for 5.26% - 52.63% of the total number of individuals, of which only R. ovulum ovulum was very dominant with a very high rate of 52.63%, the remaining five dominant species M. tropica, A. arcualis, M. tamdao, S. mahunkai, P. brevisetus, with rates ranging from 5.26% - 7.89%.
At 12:00, there were five dominant and very dominant species of oribatid mites, accounting for 7.12% - 38.91%, including two dominant species B. ornatissimus, A. arcualis with rates ranging from 7.12% - 7.78%, and three very dominant species M. minus minus, R. pinifera, R. ovulum ovulum with rates ranging from 10.43% - 38.91%.
At 18:00, there were five dominant and very dominant species of oribatid mites with a ratio ranging from 5.34% to 45.24%, of which two dominant species M. tamdao, R. pinifera, both accounting for 5.34%, and three very dominant species B. ornatissimus, A. arcualis , R. ovulum ovulum , accounting for a ratio of 11.14% to 45.24%.
At 24:00, there were four dominant and very dominant species of oribatid mites accounting for 5.92% - 50.3%, of which two dominant species A. arcualis, S. mahunkai, both accounting for 5.92% and two very dominant species B. ornatissimus, R. ovulum ovulum, accounting for 10.65% - 50.3%.
Table 3.15. Dominant oribatid mite species assemblages during the day-night cycle
STT
Species | D dominance (%) | ||||
6:00 | 12:00 | 18:00 | 24h00 | ||
1 | Microtritia tropica Märkel, 1964 | 5.26 | |||
2 | Multioppia tamdao Mahunka, 1988 | 6.14 | 5.34 | ||
3 | Arcoppia arcualis (Berlese, 1913) | 5.26 | 7.78 | 12.53 | 5.92 |
4 | Scheloribates mahunkai Subias, 2010 | 7.89 | 5.92 | ||
5 | Perxylobates brevisetus Mahunka, 1988 | 7.89 | |||
6 | Rostrozetes ovulum ovulum (Berlese, 1908) | 52.63 | 38.91 | 45.24 | 50.3 |
7 | Ramusella pinifera Mahunka, 1988 | 15.73 | 5.34 | ||
8 | Microppia minus minus (Paoli, 1908) | 10.43 | |||
9 | Berlesezetes ornatissimus (Berlese, 1913) | 7.12 | 11.14 | 10.65 | |
Thus: there are two species A. arcualis, R. ovulum ovulum, dominant or very dominant consecutively in all four survey periods, especially R. ovulum ovulum has a very high proportion, which can account for up to half of the individuals recorded at one time (Figure 3.28).
B. ornatissimus was dominant or very dominant at three survey times 12:00
- 18:00 - 24:00.
There were three dominant or very dominant species at two time points, M. tamdao was dominant at 6:00 and 18:00 with a ratio of 5.34% - 6.14%, S. mahunkai was dominant at 6:00 and 24:00 with a ratio of 5.92% - 7.89%, R. pinifera was dominant from 12:00 and 18:00 with a ratio of 5.34% - 15.73%.
The remaining three species were dominant at only one time point, M. tropica and P. brevisetus were dominant at 6:00 with 5.26% and 7.89%, respectively. M. minus minus was very dominant at 12:00 with 10.43%.
Thus, each time point has a different set and number of dominant species. In the four time points, the possibility of the dominant species group appearing is quite high, however, the dominance level at 12:00 tends to be more stable with the ratio between species groups having a small difference. The remaining three survey times all have a larger difference in the dominance ratio caused by a few species that are very dominant in the community. So it is possible that the 12:00 period has potential for species growth, but note that this is not a truly favorable factor because in this time condition, it also partly creates greater selectivity, there are also species that do not adapt to this time, and there are also new species that appear and are more active, specifically the data shows that from the 6:00 am period to the 12:00 noon period, the two species E. japonica and P. kaszabi are not seen again and there is the appearance of a new group of species including 13 species ( E. crassisetiger, C. lata, A. cocuyana, M. minus, O. minutissima, O. nova, Q. quadricarinata, N. crisposetosa, S. ruzsinszkyi, S. minima, S. ornata, T. minor, U. clavatus ).
During the four survey periods, it is noteworthy that there are two species that are continuously dominant and very dominant, Arcoppia arcualis and Rostrozetes ovulum ovulum , especially R. ovulum ovulum , which has a very high dominance level, even at 6:00 and 24:00, they account for half of the number of individuals in the community. During the research period, the data recorded that R. ovulum ovulum is also a very dominant species continuously in all four seasons in the tea monoculture habitat. In addition, A. arcualis, which is continuously dominant in four periods, was also found in all four seasons, and S. mahunkai , which is dominant in four seasons, was also found in this cultivated habitat with quite high numbers. Thus, the increase in dominance and dominance of these species is very significant, they can be considered as an indicator factor related to the perennial tea monoculture regime of the research area.
3.4.4. Similarity in species composition of oribatid mite communities during the day-night cycle
When analyzing the data using ANOSIM (Analysis of similarites), it was shown that the level of similarity between the times in the day-night cycle (R = -0.006; P = 0.47 > 0.05) had no statistically significant difference (P>0.05), between repetitions in a
time (R = 0.45; P = 0.01 < 0.05) the difference is statistically significant (P < 0.05), this also means that the species composition between times in the day-night cycle will be more similar, while the research habitat may have heterogeneous environmental conditions so the impact on species composition at each repetition will be more or less different.
The Bray-Curtis similarity coefficient (S jk ) was used to assess the similarity in species composition of oribatid mites between the survey periods. The data were presented in the form of CLUSTER analysis. The species richness data were converted to (square root) form before performing the similarity matrices.
Table 3.16. Similarity ratio of species composition of oribatid mites between four time points
6:00 | 12:00 | 18:00 | 24h00 | |
6:00 | ||||
12:00 | 48.57 | |||
18:00 | 55.26 | 79.59 | ||
24h00 | 69.80 | 58.91 | 64.13 |
Analysis of table 3.16 and figure 3.22 shows:
The similarity of oribatid species composition between the four time periods in the day-night cycle fluctuated quite high from 56.72% - 79.59%. The similarity of oribatid species composition between the four time periods can be divided into three community groups:
The first group includes the Oribatid mite community between 12:00 and 18:00 with a similarity ratio of >70%: specifically, the similarity of species composition reached the highest between 12:00 and 18:00, reaching 79.59%.
The second group consists of the oribatid mite community with similar species composition ratio (60%).
-70%): specifically, the similarity of species composition between 6:00 and 24:00 was 69.8%.
The third group includes the Oribatid mite community with species composition similarity <60%, with species composition similarity between the two groups at 12:00-18:00 and 6:00-24:00 at 56.72%.