It is possible to indicate which factors in the model have the strongest impact on the competitiveness of the DDL and some observed variables in the model are still quite general, with too few criteria for evaluation (for example, evaluating socio-economic prosperity implies "welfare".
2.2.1.4. Goffi G's model for assessing the competitiveness and sustainability of tourist destinations
Adapted from the NLCT model of DDD - Crouch and Ritchie, the model of Goffi G (2012) was built to measure 610 destinations in Italy. The model includes 7 independent variables with 64 observed variables affecting the NLCT of DDD: (1) Core resources and main attractiveness (10 observed variables); (2) Service provision (5 observed variables); (3) General infrastructure (6 observed variables); (4) Supporting factors and conditions (13 observed variables);
(5) Tourism policy, planning and development (12 observed variables); (6) Tourism management (11 observed variables); (7) Demand factors (7 observed variables). The model uses the online survey tool Limesurvey to collect, identify, monitor and analyze information. The database has been integrated, the online survey application is applied by the author for this study (See Figure 2.2).
7. Needs
1. Core resources and key attractions
2. Tourism services
Main activities
and
5. Destination Management
6. Tourism policy, planning and development
resources



4. Supporting factors and conditions
3. Common infrastructure
Support activities
and resources

Figure 2.2. Model for assessing the competitiveness and sustainability of tourism destination competitiveness - Goffi (2012) Source: Goffi G. (2012) Determinants of tourism destination competitiveness: A theoretical model and empirical evidence
The model also pointed out a very important factor, which is the role of tourism management organizations. In order to effectively use tourism resources in the long term, tourism management organizations and tourism management levels plan and develop tourism. The study also affirms that a sustainable tourism policy and tourism management are useful for preserving the ecological environment, minimizing negative impacts on culture and society, and strongly impacting on improving the competitiveness of tourism destinations. However, according to tourism experts, this study collected data from 610 small and medium-sized destinations in Italy.
However, geographically and in practice, different tourism destinations may have different results and conclusions about the impacts of factors on the competitiveness of tourism destinations. Different tourism destinations and countries will have different perceptions of tourism development and enhancing the competitiveness of tourism destinations. In addition, experts believe that this study did not ask tourists for their opinions on the competitiveness policies of tourism destinations, so the objective meaning will be limited. [84]
2.2.1.5. Indicators for assessing the competitiveness of tourist destinations
* WTTC and WEF tourism destination competitiveness assessment indexes
In 2004, WTTC used 8 indicators to evaluate the competitiveness of 212 countries and territories in the world [107]. However, after several years of use, the WTTC's tourism competitiveness assessment indicators revealed certain limitations. Therefore, WTTC and WEF have rebuilt new tourism competitiveness assessment indicators to help governments and the tourism industry more accurately assess the competitiveness as well as the tourism development potential of their countries on a global scale. In 2007, WEF published a study on the competitiveness of travel and tourism of 124 countries and territories in the world with 13 large index sets with more than 70 specific indicators to evaluate the competitiveness of destinations and from there publish these reports annually. WEF's ranking report on destination competitiveness of countries according to criteria groups for each index is measured using data provided by international organizations and by WEF experts in each country.
These indexes include: (1) Laws and policies on tourism, including 5 indexes (Legal regulations and policies; Environmental regulations; Safety and security; Health and hygiene; Prioritizing tourism development). (2) Infrastructure and tourism business environment, including 5 indexes (air transport infrastructure; road transport infrastructure; tourism infrastructure; information and communication technology infrastructure; price competitiveness). (3) Natural resources, culture and human resources, including 3 indexes (human resources index, national tourism awareness index, natural resources and culture).
WEF affirms: “The goal of the Tourism Competitiveness Index is to provide a comprehensive strategic tool to assess the factors and policies that make tourism attractive in different countries and to improve the competitiveness of the industry in national economies, as it contributes to national growth and prosperity”.
It can be seen that the WEF indexes [120] have many advantages, helping governments and the tourism industry assess the potential and prospects of the tourism industry in the world; they are useful tools for businesses as well as policy makers participating in tourism development at destinations. These indexes help raise awareness of the importance of tourism in national and global socio-economic activities.
* Tourism competitiveness assessment indexes of the Organization for Economic Cooperation and Development (OECD)
In April 2013, the OECD Tourism Committee released a set of indicators to assess tourism competitiveness through a survey of 30 member experts from 30 countries [96].
The OECD's objective is to identify a set of indicators that are useful and meaningful for governments to assess and measure their country's tourism competitiveness over time and to guide them in choosing appropriate policies.
The framework for measuring tourism competitiveness is established with three groups of indicators: core indicators, supplementary indicators, and future development indicators. These groups of indicators are divided into four areas: (1) Indicators measuring the effectiveness and impacts of tourism; (2) Indicators assessing the ability of a destination to provide competitive and quality tourism services; (3) Indicators assessing the attractiveness of a tourism destination; (4) Indicators reflecting economic opportunities and policy coordination [103].
From the OECD index, it can be concluded that: The objective of the OECD is to identify a set of indicators that are applied in an overall framework to assess national competitiveness in the tourism industry. The OECD approach is to create a limited system of indicators that are useful and meaningful for governments to assess and measure their tourism competitiveness over time and to guide them in choosing appropriate development policies. However, the most difficult thing about assessing tourism competitiveness in this model is the lack of some factors that governments can use to measure the success and competitiveness in tourism, leading to the current competitiveness in the tourism industry not being fully measured and monitored, one of the reasons being the difficulty in identifying some key factors to measure. In addition, this index has too many assessment indicators (79 indicators), so it is difficult to apply all the indicators in this model to each specific tourism destination.
* The Travel and Tourism Competitiveness Index (TTCI)
Most recently, Jennifer Blanke and Thea Chiesa (2014), the Travel and Tourism Competitiveness Index (TTCI) was developed using an index method with the aim of measuring key factors for the development of the Travel and Tourism industry in different countries. The index was developed in close cooperation with a number of partners such as the International Air Transport Association (IATA), the International Union for Conservation of Nature (IUCN), the World Travel and Tourism Council (WTTC), etc. The TTCI is integrated into 3 main groups: Group A. Travel and Tourism legal framework includes relevant policy factors under the supervision of the Government; Group B. Travel and Tourism business environment and infrastructure includes hard factors of each country's business environment and infrastructure; Group C. Natural resources, cultural resources and human resources include soft factors of human, natural resources and cultural resources of each country. [118]
This set of criteria is considered easy to understand and apply in practice, but many experts also say that the TTCI criteria set is still lacking many variables and some variables overlap with the global NLCT assessment criteria set.
GCI. Should the World Economic Forum WEF make adjustments or integrate these two sets of criteria into one?
In addition to the above research models, the thesis also refers to the research model of Vu Van Hung (2016). Based on the criteria for evaluating the competitiveness of the service industry by M. Porter; competitiveness in tourism by Crouch and Ritchie; competitiveness in tourism by Dwyer and Kim, and the criteria for evaluating the competitiveness of the tourism industry by WEF, the study has formed a set of evaluation criteria suitable for the sea and island tourism of Khanh Hoa province. The set of criteria includes 5 main groups of factors and 44 evaluation indicators: (1) Input factors or also known as service supply conditions, including: accommodation system; restaurant system, food court; public transport system; entertainment infrastructure; shopping centers, souvenirs; (2) Demand conditions, including: tourist market; sea and island tourism products; (3) Supporting and related services, including: the availability and quality of supporting services; the availability of supporting and related industries; (4) Industry competitive strategy; (5) Tourism environment and the role of local government. [14]
All of the above criteria are assessed using a Likert scale from 1 to 5 points, in which: (1- Far below the average; 2- Slightly below the average; 3- Equal to the average of the compared island tourism destinations; 4- Slightly above the average; 5- Far above the average) (See Appendix 9). This set of criteria is a good reference source but requires the collection of sufficient secondary and primary data sources to serve the analysis of the current status of the island tourism potential.
In summary, each model and set of indicators above has a different approach to assessing the competitiveness of tourism destinations. These research models have been developed and tested in many spaces and times. And in reality, each tourism destination has a different geographical location and characteristics, so the competitiveness model applied in this tourism destination may not be applicable to another tourism destination and may not give suitable results (Kozak and Remmington, 1999) and there is not a complete model for studying the competitiveness of tourism destinations because the proposed models have not provided a comprehensive assessment framework, different aspects of each tourism destination (Gomezalej and Mehalic, 2008; Crouch, 2011; Mazuek, 2014; Gupta and Singh, 2015).
Therefore, for the topic of the thesis, to build a research framework for the topic, the author will inherit and select appropriate evaluation standards and criteria; at the same time, ignore inappropriate standards and criteria in terms of scale, space and time. This proposed research framework is also based on the foundation of the elements constituting the NLCT of the DDDL identified in the study.
2.2.2. Factors that make up the competitiveness of a tourist destination
Through a literature review and especially the results of in-depth interviews with 15 experts in this study, the elements constituting the NLCT of the DDDL were identified. Specifically:
The factors constituting the tourism potential of Ha Long, Quang Ninh - Vietnam were unanimously agreed upon by 15 experts (100%), namely: Tourism resources; Tourism human resources;
SPDL; Infrastructure and tourism infrastructure; Tourism management . The image of the tourism destination was unanimously agreed by 14 experts (93%). The two elements of Convenience of accessing the tourism destination and Community participation in tourism were unanimously agreed by 13 experts (87%). Tourism enterprises and Price are two elements that were added by 14 experts (93%) to the system of elements constituting the competitive capacity of the Ha Long tourism destination. Experts believe that tourism enterprises play an important role in constituting the competitive capacity of the tourism destination, especially for Ha Long - a tourism destination that is in great need of the business capacity of tourism enterprises. In addition, Price is also an indispensable element that needs to be added when price advantage is still considered a competitive strength of the tourism destination, especially for developing tourism destinations like Ha Long.
Thus, there are 8 elements constituting the proposed NLCT of the DDD plus 2 elements of DNDL and Price added by experts. (See Table 1.1; 2.2 and Appendix 3).
Table 2.2. Elements constituting the NLCT of the DDDL
Elements of capacity
competitiveness of tourist destinations
References | |
1. Tourism resources | Crouch and Ritchie (1999); Dwyer and Kim (2003); Enright and Newton (2004); M. Kozak (2004); Lee CF and King B. (2006); WEF (2007); Cracolici and Nijkamp (2008); Mechinda P. (2010); Pike & Mason (2010); Zhang et al. (2011); Goffi G. (2012); OECD (2013); Katerina Ryglovaa et al. (2015); Amaya Molinar et al. (2017); Nguyen Van Manh (2004); Pham Trung Luong (2011); Nguyen Minh Tue (2014); Thai Thi Kim Oanh (2015); Nguyen Thach Vuong (2015); Vu Van Hung (2016); Bui Thi Tam et al. (2017); Le Thi Ngoc Anh (2017); Ý expert opinion |
2. Tourism human resources | Dwyer & Kim (2003); Enright and Newton (2004); Mike and Caster (2007); M.Porter (2008); Craigwell and More (2008); Zhang et al. (2011); Katerina Ryglovaa et al. (2015); Amaya Molinar et al. (2017); Nguyen Van Manh (2004); Pham Trung Luong (2011); Nguyen Minh Tue (2014); Thai Thi Kim Oanh (2015); Nguyen Thach Vuong (2015); Le Thi Ngoc Anh (2017); Y expert opinion |
3. Tourism products | Richie and Crounch (2000); Candea et al., (2009); Nguyen Van Manh (2004); Pham Trung Luong (2011); Thai Thi Kim Oanh (2015); Nguyen Thach Vuong (2015); Vu Van Hung (2016); Expert opinion |
4. Tourism infrastructure and technical facilities | Crouch and Ritchie (1999); Hassan (2000); Dwyer and Kim (2003); Enright and Newton (2004); Mike and Caster (2007); Craigwell (2007); Cracolici and Nijkamp (2008); Barbosa et al. (2010); Zhang et al. (2011); Goffi G. (2012); Katerina Ryglovaa et al. (2015); Amaya Molinar et al. (2017); Pham Trung Luong (2011); Nguyen Minh Tue (2014); Thai |
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Assessing people's awareness of tourism impacts and people's support for tourism development in Vinh Long province - 14 -
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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Planning marketing strategy for Ha Long tourism industry until 2020 - 16 -
Sustainable tourism development in Ha Long Bay, Quang Ninh province - 16 -
Research on sustainable tourism development in Ha Long Bay area - 9

Elements of capacity
competitiveness of tourist destinations
References | |
Thi Kim Oanh (2015); Nguyen Thach Vuong (2015); Vu Van Hung (2016); Bui Thi Tam and colleagues (2017); Le Thi Ngoc Anh (2017); Nguyen Thanh Sang and colleagues (2018); Expert opinion | |
5. Tourism destination management | Crouch and Ritchie (1999); Dwyer and Kim (2003); Enright and Newton (2004); Lee CF and King B. (2006); Mechinda P. (2010); Goffi G. (2012); Mottironi C. and Corigliano MA (2012); Amaya Molinar et al. (2017); Pham Trung Luong (2011); Nguyen Minh Tue (2014); Thai Thi Kim Oanh (2015); Vu Van Hung (2016); Bui Thi Tam et al. (2017); Le Thi Ngoc Anh (2017); Nguyen Thanh Sang et al. (2018); Expert opinion |
6. Tourist destination images | Zimer and Golden, (1988; Chon (1990); Echtner and Ritchie (2003); Lin et al. (2007; Chen and Tsai (2007); Martin and del Bosque (2008); Katerina Ryglovaa et al. (2015); Amaya Molinar et al. (2017); Thai Thi Kim Oanh (2015); Le Thi Ngoc Anh (2017); Nguyen Thanh Sang and colleagues (2018); Expert opinion |
7. Tourism business | Additional experts |
8. Convenience of accessing tourist destinations | Crouch and Ritchie (2003); Mike and Caster (2007); Barbosa et al. (2010); Pham Trung Luong (2011); Nguyen Minh Tue (2014); Expert opinion |
9. Price | Additional experts |
10. Participation of local communities in tourism | Dwyer and Kim (2003); M. Kozak (2004); Craigwell and More (2008); Zamani-Farahani and Musa (2008); Cracolici and Nijkamp (2008); Thai Thi Kim Oanh (2015); Bui Thi Tam et al. (2017); Expert opinion |
Source: Author's synthesis
In summary, the factors constituting the competitiveness of tourism destinations suitable for the thesis topic are determined to include 10 factors: (1) Tourism resources; (2) Tourism human resources; (3) Tourism products; (4) Infrastructure and tourism physical facilities; (5) Tourism destination management; (6) Tourism destination image; (7) Tourism destination's appearance.
convenient access to tourism resources; (8) tourism enterprises; (9) Prices; (10) Participation of local communities in tourism.
2.2.2.1. Tourism resources
The tourism resource system becomes a resource factor, an important basic attribute that creates the attraction or appeal of the tourist destination to tourists. According to the Vietnam Tourism Law, 2017: “Tourism resources are natural landscapes, natural elements, historical - cultural relics, creative works of human labor and other human values that can be used to meet the needs of tourists.
Tourism, the basic factors for forming tourist areas, tourist spots, tourist routes, tourist cities" [42]. Tourism resources of tourist destinations include natural tourism resources and cultural tourism resources. Tourism resources are the main reason for tourists to decide to choose tourist destinations (Crouch and Ritchie, 1999). In particular, one of the factors of tourism resources that creates attractiveness, plays a decisive role, and increases the competitiveness of tourist destinations is World Heritage . World Heritage is a comparative advantage, considered a superior advantage of tourist destinations compared to other competitors in the tourism market. Accordingly, the attractiveness of tourism resources is an important criterion, decisive in tourists' choice of tourist destinations. Crouch and Ritchie (2000), natural beauty and climate are important factors in determining globally attractive destinations. The attractiveness of natural resources is created by the quantity and quality of natural tourism resources; the richness, popularity, uniqueness, class, novelty of these resources and the ability to develop tourism types. A tourist destination with high potential is a place with natural beauty and favorable weather and climate for tourism; is a place where a significant number of world heritages, natural wonders, national parks, national forests converge; rich flora and fauna, beautiful beaches, etc. The attractiveness of cultural resources is reflected in the quantity and quality of cultural resources, the impression, uniqueness, class, and novelty of cultural resources and especially world cultural heritages. That increases the attractiveness for tourists whose main motivation for the trip is to learn and appreciate the tangible and intangible cultural values of the tourist destination.
2.2.2.2. Tourism human resources
Tourism human resources are the workforce working or looking for jobs in the tourism sector, including direct and indirect human resources.
Tourism human resources are considered valuable assets, directly affecting the business efficiency of enterprises as well as the sustainable development of the tourism industry. For a tourism destination, tourism human resources are considered an important resource, determining the quality of the destination. With the current market trend, domestic and foreign tourists have very high demands from tourism services. Therefore, to improve the competitiveness of tourism destinations, it requires abundant tourism human resources; ensuring both quantity, structure and quality. In reality, a tourism destination, although its tourism resources are very rich and unique; convenient to access, but without a high-quality human resource team, cannot manage, exploit resources and create attractive tourism products and attract tourists.
2.2.2.3. Tourism products
According to the Vietnam Tourism Law, 2017: "SPDL is a set of services based on exploiting the value of tourism resources to satisfy the needs of tourists".
Tourism products create the value and attractiveness that a tourist destination provides to tourists. The tourism products of a tourist destination are the overall product that is accepted and satisfied by the market; satisfy the needs and give tourists more special impressions and emotions about this destination than other destinations in the world. The basic product in tourism is the experience of the tourist destination (Richie and Crounch, 2000).
The structure of a destination's attractive and competitive tourism products includes tourism programs, package tourism products (including goods or service products. The more diverse, unique and different this tourism product structure is, the more advantageous and competitive the destination will be compared to its competitors in the tourism market. Thus, the key and important point here is that the destination's specific tourism products must be built on the "core" values of the destination's tourism resources (Pham Trung Luong, 2011). Accordingly, the destination's specific tourism products are products with attractive, unique, original and representative elements of natural and cultural tourism resources for a tourist destination. The specificity of tourism products not only satisfies the needs of tourists but also contributes to creating and developing the image and enhancing the competitiveness of the tourist destination.
2.2.2.4. Tourism infrastructure and technical facilities
Infrastructure and tourism resources are considered important components of tourism competitiveness (Crouch and Ritchie, 1999; Hassan, 2000; Dwyer and Kim, 2003; Enright and Newton, 2004; Mike and Caster, 2007; Craigwell, 2007; Cracolici and Nijkamp, 2008; Barbosa et al., 2010; Amaya Molinar et al., 2017; Pham Trung Luong, 2011; Nguyen Minh Tue, 2014; Thai Thi Kim Oanh, 2015; Vu Van Hung, 2016; Nguyen Thanh Sang et al., 2018).
Infrastructure includes the transportation system, electricity, water, and communication systems of the tourist destination. Well-developed and well-maintained infrastructure will provide a solid foundation for the tourism industry to operate and develop strongly.
Tourist infrastructure is all technical and material aspects involved in the process of serving tourists; meeting the needs of rest, food, travel, meetings and other needs during tourists' stay and visit; including the system of accommodation, food, entertainment, shopping centers, etc.
Thus, infrastructure and tourism services contribute to effectively exploiting tourism resources, satisfying tourists' needs and promoting tourism development. Synchronous infrastructure and tourism services will create favorable conditions for tourism destinations to improve their competitiveness; conversely, it will slow down the development and reduce the competitiveness of tourism destinations.
2.2.2.5. Tourism destination management
The term “Destination Management” is generally understood as the process of coordinating actions to benefit local communities, businesses, tourists and at the same time resolving the relationships between them.


![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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