enterprises and the ability to manage and monitor bank loans. In which, Credit terms are the strongest influencing factor with a Beta coefficient of 3.11. Because most small and medium enterprises find that the credit terms offered by banks such as: loan interest rates, collateral requirements, loan repayment period... are disadvantageous to the business. This has partly caused costs for the business and affected the business's production and business activities. On that basis, the author also makes some recommendations to improve credit efficiency for commercial banks in Kenya. (Leah Atieno Auma, 2017). [91]
Domestic research:
Nguyen Kim Anh (2004) has presented "basic theories on credit operations, from which he analyzed factors affecting the development of credit operations of Vietnamese commercial banks such as: loan appraisal, collateral assessment, credit risk management... these are also factors affecting the credit quality of banks". (Nguyen Kim Anh, 2004), [29]
Maybe you are interested!
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Factors Affecting Bank Credit Quality -
![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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Nguyen Van Tuan (2015) used descriptive statistics and secondary data analysis methods to clarify the current status of credit quality at Agribank Vietnam through a group of quantitative indicators such as: Group of indicators reflecting the scale of credit capital provision of commercial banks, Group of indicators reflecting the level of safety in credit activities of commercial banks, Group of indicators of commercial banks' profits; and qualitative indicators such as: Bank credit granting process, Internal credit rating system to assess the debt repayment capacity of enterprises, Indicators reflecting the capacity to develop credit products and customer care policies. At the same time, combined with the use of primary data analysis methods through Cronbach's Alpha reliability coefficient analysis, EFA (Exploratory Factor Analysis), ANOVA variance analysis and regression analysis, the study quantified the level of influence of factors on the credit quality of Agribank Vietnam including: Bank credit policy; Bank credit procedures and regulations; Organizational work; Bank human resource quality; Management capacity; Banking technology equipment; Credit information; Internal inspection and control work; Capital mobilization work. In which, the factor "Bank credit procedures and regulations" has the strongest influence with a coefficient of 0.265; followed by the factor "Bank credit policy" with a coefficient of 0.257 and the lowest influence is the factor "Bank organization work" with a coefficient of 0.069".
Credit policy
Internal control and inspection work
Quality of human resources
CREDIT QUALITY
Capital mobilization
Organizational work
Management capacity
Credit information
Credit process
Technology equipment
Source: Nguyen Van Tuan (2015), [35]
Figure 2.2. Model of factors affecting credit quality at Agribank Vietnam
2.3.3. Studies on bank credit for supporting industries
Foreign research:
Zaghum Umar et al. (2019). The study examines the conditional correlation and optimal hedge ratio results between credit default swap (CDS) spreads of the US metal mining industry of copper, platinum, silver and gold used during 2017-2018. The volatility and conditional correlation of CDS and metal prices are compared using multivariate GARCH models, differentiating features of financial time series. The study uses estimation techniques and constructs forecasts of optimal hedge ratios. The results of the study show that copper prices are a better substitute for other metal prices in the US and that copper mining industries have lower credit risk than other metal mining (Zaghum Umar et al. 2019). [121]
LingxiaoTang et al. (2019). With the rapid growth of credit card business in China's energy industry, credit risks are gradually exposed. This study aims to scientifically measure the credit risk of credit cards used in China 's energy industry and lay a foundation for comprehensive credit risk management. Based on the analysis of influencing factors of credit risk, this study applies a random algorithm and monthly data of credit cards used by energy industry customers in a branch of the Postal Savings Bank of China from April 2014 to June 2015.
2017 to construct an effective credit risk assessment model and scientifically measure credit risk in China's energy industry. The results show that credit card features such as overdraft ratio and credit card expense amount within a month have a significant impact on credit risk, the comprehensive prediction accuracy of our model is as high as 91.5%, and its stability is very satisfactory. These findings can provide valuable information to help banks improve credit risk management (LingxiaoTang et al. 2019). [92]
ShiqiOu ZhenhongLin et al. (2019). The Chinese government introduced a new energy vehicle credit limit in 2017 to encourage fuel-saving and electrification technologies in the Chinese passenger car market. This study summarizes the dual credit policy and develops a Credit Model for the Energy and Oil Consumption Industry to quantify the impact of this policy on consumer choice and profitability of the clean energy industry, in which the capital subsidy is used to represent the industry's response to the policy. The key findings from the model results include: (1) Enterprise Average Fuel Consumption rules alone can stimulate more sales of electric vehicles (PEVs) than the dual credit policy; however, (2) the dual credit policy can stimulate more battery electric vehicles (BEVs) in the market, compared to other policy scenarios; (3) the industry may lose about $2122/vehicle in 2020 under the dual credit policy; (4) battery electric vehicles with ranges greater than 250km and plug-in hybrid SUVs may become popular under the dual credit policy; (5) credit allocation to BEVs under the dual credit policy may affect PEV production; and (6) the reduction in fuel-saving technology costs helps to minimize the profit loss affected by the preferential credit policy. (ShiqiOu ZhenhongLin et al. 2019). [110]
Alfredo JuanGrau (2018). Analyzing the impact of trade credit on profitability determinants during the European crisis. The study uses panel data for a total of more than 24,000 European agri-food companies from 2010 to 2014. Among the main contributions of the study is that separating industry effects and country effects by separating national policies and trade credit provision, the impact of trade credit on profitability depends on the country and the characteristics of the size, specificity, market power or reputation of the European agri-food company. (Alfredo JuanGrau, 2018), [70]
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Studies on information technology to expand the analysis of information in the industry, thereby promoting a deeper understanding of the processes, operations, policy making and rational planning of the industry (Li et al., 2015), [89]. However, due to the lack of micro-level or complete data on supporting industry enterprises, few studies have fully analyzed the spatial distribution of supporting industry industries in China at multiple scales, from the perspective of time change, incorporating all types of supporting industry enterprises (Watkins, 2014), [117]. The Administration for Industry and Commerce (AIC) of China is funded to register, supervise and manage supporting industry enterprises and protect the rights and interests of consumers (AIC, 2016). Regional offices record detailed operational information for each enterprise. The registration data of supporting industry enterprises, collected from the China AIC office, can enable and support the spatiotemporal analysis of supporting industry sectors. A typical registration file contains information of a supporting industry enterprise, including name, address, registration date, supporting industry category, business scope, postal code, legal representative, and registered capital. Typically, these records are recorded manually and entered into the system at local AIC locations. During this process, important information is over-looked or overlooked and thus frequently missing from the database. In the study by FaLi et al. (2018) [81], 43.64% of the data did not have supporting industry category values. However, this information is required when conducting spatial distribution analysis of supporting industry types and industries. Thus, the lack of customer information will affect the accuracy of the bank's credit decisions (FaLi et al., 2018), [81]. In addition to customer information that is a supporting industry enterprise, information about the operating plan of the supporting industry enterprise is also valuable information for the bank to decide to grant credit. An effective operating plan ensures that the supporting industry enterprise operates well (Luengo et al. 2012). Moreover, the application of Science and Technology in the operations of supporting industry enterprises is extremely necessary in the supporting industry.
The analysis of the capacity of supporting industry enterprises, the financial capacity of supporting industry enterprises is emphasized in demonstrating the competitiveness of enterprises and especially the capital policies of supporting industry enterprises are demonstrated through the studies of Combes and Associates (2010), [75]. By analyzing the financial capacity of supporting industry enterprises and the capital structure in supporting industry enterprises, many authors have tried to explain the capital structures of enterprises such as Giuliano and Associates.
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1991; Liu et al. (2016), [90]. Studies on clustering of supporting industry enterprises and participation in supporting industry clusters by Duranton et al. (2005), [77], when supporting industry enterprises participate in industry clusters, it will ensure that the enterprise's operations are more efficient and safe, increase the enterprise's internal capacity, and help enterprises access credit sources more easily (M. Colledani et al. 2010), [96]. A research model by Bernard et al. (2011), [72] also shows that the use of information by supporting industry enterprises when participating in the Multinational Corporation network is useful information for the industry in general and the supporting industry in particular. If an enterprise participates in the Multinational Corporation network, it proves that the enterprise is developing and has prestige in the domestic and international markets. Domenech et al. (2011), [78] concluded that the use of enterprise-related information such as financial capacity, level of participation in global networks will be important data to optimize information to assess the development of enterprises and certainly this will be an important source of information for banks to decide whether to provide capital or not? Information on the type of CNHT enterprise, the duration of operation of the enterprise, the enterprise's business portfolio, geographical information... these data are taken from many different sources. In China, they are taken from the AIC office, this data source is easier to apply to researchers, planners and decision makers (FaLi et al., 2018), [81]
Domestic research:
Phuong Chi (2011), "Credit creates conditions for the development of supporting industries". The author's research results show that the Government has identified that in the coming time, supporting industries are one of the areas that need to prioritize capital allocation for development and the banking sector needs to continue to implement monetary and credit solutions to fully and promptly meet the borrowing needs of businesses. Banks need to continue to improve the process of assessing customers' creditworthiness and business activities to improve appraisal efficiency, thereby increasing the ability to lend without collateral; Develop credit programs and packages with reasonable interest rates for businesses, diversify banking products and services; Simplify administrative procedures to increase businesses' access to capital. On the part of supporting industries businesses, they must improve themselves, strengthen their management capacity, seek markets, and provide transparent information to enhance their reputation with banks. Enterprises need to participate in production and business activities according to the product value chain to create
50
conditions for banks to control cash flow and financial situation of enterprises during the borrowing process. (Phuong Chi, 2011), [50]
Thach Hue (2017), "Credit solutions for supporting industry enterprises". The article analyzed the current situation of Vietnam's supporting industry in recent times and showed that financial resources are still limited, enterprises in Vietnam's supporting industry are facing many difficulties and challenges to innovate and develop. The author believes that "essential solutions are needed for capital sources, preferential finance, infrastructure and factory space to help supporting industry enterprises increase investment in production and technology. At the same time, improve the quality of human resources and orient the search for markets as well as product output". Through a survey of enterprises, it is shown that many commercial banks have paid attention and provided certain support to supporting technology industries and fields. Specifically, such as Vietnam Development Bank (VDB), Vietnam Joint Stock Commercial Bank for Industry and Trade, Tien Phong Bank... However, the author also stated that "in practice, accessing preferential credit sources for supporting industry enterprises is not as easy and convenient as desired. Difficulties in complicated loan procedures; lack of collateral, small scale of production and business, poor financial management skills and, in addition, inadequate and non-transparent information and finance... are barriers that make it difficult for small and medium-sized enterprises in the supporting industry to borrow credit when needed." In the solution section, the author proposed that "Relevant levels and sectors should soon study credit support solutions with preferential interest rates, flexible loan terms and loan limits suitable to the conditions of enterprises. At the same time, loosen regulations on mortgaged assets... so that enterprises can easily access capital when needed, instead of having to borrow from informal channels in the market. In addition, it is also necessary to establish a financial fund specifically for supporting industry enterprises and an open fund to attract all domestic and international funding sources. Private enterprises in the supporting technology and supporting technology for high technology should be allowed to access ODA loans to invest in purchasing equipment, machinery, and technology from foreign countries such as Japan, Korea, and some advanced technology countries. From there, increase production capacity and join the global production chain. Even allow and support Vietnamese supporting industry enterprises to invest in acquiring enterprises in Japan that are producing supporting technology components. Because these Japanese enterprises are facing difficulties in the problem of aging population and lack of successor generations,
51
are in need of technology transfer to Vietnamese enterprises". (Thach Hue, 2017), [62]
2.3.4. Research gaps and research directions of the thesis
After reviewing domestic and foreign research related to the topic, the author found that previous research has the following main directions:
- When researching the credit quality of commercial banks, the authors all approach it from the perspective of credit for enterprises in general or for production households at a commercial bank or a group of commercial banks, but do not specifically research credit for enterprises in the supporting industry.
- For studies on factors affecting credit quality, the authors have used many different models and methods (GMM dynamic panel data regression method, VAR model, multiple linear regression model, SEM linear structure model) to measure the level of influence of internal and external factors on credit quality, however, the authors only approached from the perspective of credit for enterprises in general and did not delve into credit research for enterprises in the supporting industry.
- There are many studies on the field of supporting industries, but they only focus on the development of supporting industries, the role of supporting industries in economic development, or the impact of supporting industries on attracting foreign direct investment (FDI), but do not delve into credit activities for supporting industries.
- There are very few studies on credit for supporting industry enterprises in Vietnam at present. However, in the world, there are quite a few studies on credit for each supporting industry such as automobile manufacturing, metal mining, etc. and studies on assessing factors affecting credit risk for supporting industry enterprises at commercial banks.
Lending activities of commercial banks are the main activities that bring a lot of profit to banks but always have many potential risks, so commercial banks must find measures to improve credit quality. On the other hand, the field of supporting industry, although it was born a long time ago in the world, has only recently received attention in Vietnam. Enterprises operating in the supporting industry are usually small and medium enterprises, although they have borrowed capital from banks to maintain their operations, but the operating efficiency is not high due to the fragmentation in the process of organizing production and business and not receiving appropriate support from the State. Therefore
These enterprises have contributed to increasing bad debts for the banking system. Currently, the development of the supporting industry is mainly from attracting foreign direct investment (FDI), while access to credit for small and medium enterprises operating in the supporting industry is facing many difficulties.
Therefore, the author realizes that the research on credit activities for the supporting industry in Vietnam is currently very new when the supporting industry has just begun to appear in Vietnam but has not yet been deeply integrated. Based on that perception, the author directs his research to analyze the current status of credit quality for the supporting industry and the factors affecting credit quality for the supporting industry at Vietnamese commercial banks to find solutions to improve credit quality for the supporting industry at Vietnamese commercial banks in the coming time.