Table 2.
2 times
average lending and deposit rates of the units
Commercial Bank
Vietnam 2011 2019
Unit: %
Target
2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 201 7 | 201 8 | 2019 | |
|
average | 11.44 | 9.01 | 7.00 | 5.55 | 4.77 | 4.56 | 5.9 | 6.5 | 6.9 |
3. Average loan interest rate | 18.14 | 15.75 | 12.03 | 10.46 | 8.85 | 8.74 | 10.4 | 10.7 | 11.2 |
Maybe you are interested!
-
Current Status of Interest Rate Risk Management at Military Commercial Joint Stock Bank -
Current Status of Credit Risk Control Measures -
![Qos Assurance Methods for Multimedia Communications
zt2i3t4l5ee
zt2a3gs
zt2a3ge
zc2o3n4t5e6n7ts
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
div.maincontent .s1 { color: black; font-family:Times New Roman, serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 15pt; }
div.maincontent .s2 { color: black; font-family:Times New Roman, serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 15pt; }
div.maincontent .p { color: black; font-family:Times New Roman, serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; }
div.maincontent p { color: black; font-family:Times New Roman, serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; }
div.maincontent .s3 { color: black; font-family:Times New Roman, serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; }
div.maincontent .s4 { color: black; font-family:Times New Roman, serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; }
div.maincontent .s5 { color: black; font-family:Times New Roman, serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; }
div.maincontent .s6 { color: black; font-family:Times New Roman, serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; }
div.maincontent .s7 { color: black; font-family:Wingdings; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; }
div.maincontent .s8 { color: black; font-family:Arial, sans-serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 15pt; }
div.maincontent .s9 { color: black; font-family:Times New Roman, serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; }
div.maincontent .s10 { color: black; font-family:Times New Roman, serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 6pt; }
div.maincontent .s11 { color: black; font-family:Times New Roman, serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; }
div.maincontent .s12 { color: black; font-family:Times New Roman, serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; }
div.maincontent .s13 { color: black; font-family:Times New Roman, serif; font-style: normal; font-weight: normal; text-d](https://tailieuthamkhao.com/uploads/2022/05/15/danh-gia-hieu-qua-dam-bao-qos-cho-truyen-thong-da-phuong-tien-cua-chien-6-1-120x90.jpg)
Qos Assurance Methods for Multimedia Communications
zt2i3t4l5ee
zt2a3gs
zt2a3ge
zc2o3n4t5e6n7ts
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
div.maincontent .s1 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 15pt; }
div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 15pt; }
div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; }
div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; }
div.maincontent .s3 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; }
div.maincontent .s4 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; }
div.maincontent .s5 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; }
div.maincontent .s6 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; }
div.maincontent .s7 { color: black; font-family:Wingdings; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; }
div.maincontent .s8 { color: black; font-family:Arial, sans-serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 15pt; }
div.maincontent .s9 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; }
div.maincontent .s10 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 6pt; }
div.maincontent .s11 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; }
div.maincontent .s12 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; }
div.maincontent .s13 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-d -
Current Status of Credit Risk Management at Vietnam Bank for Agriculture and Rural Development -
Current status of credit risk at Saigon Thuong Tin Commercial Joint Stock Bank, Lam Dong branch - 1
Table 2.2 shows a decrease in both deposit and lending interest rates in the period 2011-2016, and an increasing trend in 2017 and 2019.
Table 2.3 Average lending and deposit interest rates of
Vietinbank 2011 – 2019
Unit: %
Source: Vietinbank Annual Report 20112019
Target
2011 | 2012 | 2013 | 2014 | 201 5 | 201 6 | 2017 | 2018 | 2019 | |
1. Listed average mobilization interest rate listed | 11.0 2 | 8.88 | 6.89 | 5.27 | 4.27 | 4.22 | 5.1 | 5.8 | 6.9 |
2. Actual mobilization interest rate (taking into account the utilization coefficient) capital) | 12.6 1 | 9.92 | 7.25 | 5.62 | 4.82 | 8.33 | 7.12 | 7.25 | 7.02 |
3. Average loan interest rate | 18.0 2 | 15.6 1 | 11.9 1 | 10.3 2 | 8.78 | 8.68 | 10 | 10.2 | 11.2 |
4. The difference between the lending interest rate and the average deposit interest rate is calculated with a coefficient. use of capital | 5.41 | 5.69 | 4.66 | 4.70 | 3.96 | 3.02 | 4.69 | 3.89 | 3.48 |
Table 2.3 shows that the difference between deposit and lending interest rates has decreased from 2011 to 2019, specifically in 2011 the difference was 5.41%, in 2014 the difference was 4.7% and in 2018 the difference was 3.89% and in 2019 the difference was 3.48%. With this data showing the great competitive pressure between banks, Vietinbank had to adjust the difference between deposit and lending, which also means reducing profits to attract customers.
Through practical research at the bank, the bank has recognized the risk through fluctuations in deposit and lending interest rates. The bank has made timely adjustments in managing deposit and lending interest rates, however, this recognition was very slow after there were signs of a decrease in actual interest rates in the market.
2.2.2.2. Current status of interest rate risk measurement
To measure RRLS, in theory, we have 3 basic models for measurement: maturity model; revaluation model and duration model. Each model has its own advantages and disadvantages. In fact, Vietnamese commercial banks currently mainly apply the revaluation model. There are 2 types of RRLS, which are income risk and asset value reduction risk. Income risk is measured by the revaluation model, while asset value reduction risk is measured by the duration model. For Vietinbank, the bank only uses the revaluation model to measure income risk. The staff of the planning department and the support department of the TSC Asset Management Committee measure RRLS according to the revaluation model with the following content:
One is to measure the interest rate sensitivity gap across pricing maturity ranges.
again, including: no term, under 1 month, from 13 months, from 36 months, from 612
months. From 15 years, over 5 years is not sensitive to interest rates because the repricing period is usually 1 year.
Second, the bank uses the interest rate repricing period as the remaining period from the date of preparing the financial statement to the nearest interest rate repricing period of the assets and capital items.
Third, when analyzing the real interest rate repricing period of assets and capital sources, the bank makes assumptions and conditions for classifying which assets
insensitive to interest rates, or arrangement of
assets
small
strange
equivalent
with FTP term.
In general, risk measurement is carried out at banks, but the measurement according to the above model does not fully reflect interest rate risk.
2.2.2.3. Current status of control, supervision and reporting of interest rate risk
The control and delivery of goods are carried out as follows:
sat, bao
high
rui
interest rates are calculated by the departments in the bank.
Officer in charge of QTRRLS: is responsible for regularly measuring, monitoring and promptly reporting to the leaders of the Market Risk Management Department on the implementation of interest rate sensitive asset gap limits, limits on changes in net interest income, limits on changes in net present value when market interest rates change, and limits on values subject to RRLS.
Periodically (in accordance with the operating mechanism of the TSN TSC Management Committee), the Market Risk Management Department prepares reports on compliance with the interest rate sensitive asset gap limit, the limit on changes in net interest income, the limit on changes in net present value when market interest rates change, and the limit on the value subject to RRLS to submit to the approval levels (leaders of the Market Risk Management and Operations Department, Deputy General Director in charge of risk management) to report to the TSN - TSC Management Committee.
At Vietinbank, RRLS management and supervision is carried out through
The limits have been approved by the TSN TSC Management Committee. Limits
Commonly used is the cumulative sensitivity gap/Total Assets limit. The basis for establishing the limit is based on the previous year's limit, business and profit plan, market conditions, risk appetite, compliance test results. The GAP limit approval period is usually monthly or when there is a
major market fluctuations as required by the TSN TSC Management Committee. The limits are as follows:
Table 2.4 Table of limits for cumulative sensitive TSC TSN difference ratio/Total
asset
Deadline
Limit | |
Up to 3 months | ± 25% |
Up to 6 months | ± 20% |
Up to 9 months | ±15% |
Up to 12 months | ±10% |
Source: Vietinbank's financial report
Table 2.5 Accumulated sensitive assets/Total assets difference ratio of Vietnam Joint Stock Commercial Bank for Industry and Trade
Duration
Implementation of the years | Limit | Result | |||||||||
2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |||
Up to 3 month | 3.29% | 5.07% | 5.37% | 5.37% | 7.41% | 5.37% | 5.37% | 5.37% | 6.12% | ±25% | Follow |
Up to 6 month | NA | 7.29% | 7.99% | 7.92% | 7.86% | 7.89% | 7.99% | 8.09% | 8.32% | ±20% | Follow |
Up to 12 month | 8.16% | 2.30% | 4.31% | 5.01% | 5.37% | 5.39% | 5.61% | 5.62% | 5.92% | ±10% | Follow |
Source: Author's calculation based on Vietinbank annual report 2011-2019
Thus, looking at the table above, we see that in fact, in the years 2011-2019, the cumulative sensitive gap ratio/Total assets of the bank all complied with the limit set by the bank (In 2011, in the term range up to 6 months, the author did not have the conditions to get data because the bank's report divided the term range from 312 months, there was no data for the term range from 612 months).
In addition, the results of interest rate risk management are also reflected in the volatility of the bank's net interest income (NII).
In fact, because the interest rates of assets and liabilities do not fluctuate at the same rate, the calculation of the fluctuation of net interest income is based on the fluctuation of interest rates of each asset and interest rates of liabilities.
We have the following formula:
∆NIIt = RSAt1 * ∆RAt RSLt1* ∆RLt
In which: RSA, RSL are shown in the following table 2.13:
Table 2.6: Rate sensitive assets and liabilities table for the years 2011-2019
Unit: Billion VND
Liabilities and Assets
sensitive
interest rate
December 31, 2011 | December 31, 2012 | December 31, 2013 | December 31, 2014 | December 31, 2015 | December 31, 2016 | December 31, 2017 | December 31, 2018 | December 31, 2019 821,501,129 | |
Interest sensitive assets RSA | 353,967,044 | 452,966,415 | 542,903,410 | 592,039,233 | 622,837,232 | 662,378,232 | 704,429,504 | 736,128,831 | |
Interest sensitive liabilities | 421,069,155 | 454,008,640 | 494,098,406 | 554,387,408 | 569,998,208 | 596,668,201 | 624,586.0725 | 652,692,446 | 701,644,379 |
capacity(RSL)
Interest rate sensitive gap (GAP) | 67,102,111 | 1,042,225 | 48,805,004 | 37,651,825 | 52,839,024 | 65,710,031 | 79,843,431 | 83,436,386 | 119,856,750 |
Sensitivity rate Interest Rate Sensitivity (RSA/RSL) | 0.841 | 0.998 | 1,099 | 1,068 | 1,093 | 1,110 | 1,128 | 1.13 | 1,171 |
Source: author's calculation from Vietinbank annual report 2011-2019
To calculate the change in net income when market interest rates change
change, it is necessary to calculate the change in the average return of Assets (∆ RA ) and
change in average rate of return of the
drill
Debt (∆ RL). Data in the following tables
shows the change in the average rate of return of TSN and TSC.
Table 2.7. Changes
average yield in Taiwan
jump
Orange
return rate (∆ RA)
Unit: %
Year
Beginning interest rate of assets (
| End-of-period interest rate of assets ( | The average interest rate change of interest rate sensitive assets ( | |
2011 | 17.20 | 14.12 | 3.08 |
2012 | 15.56 | 13.01 | 2.55 |
2013 | 14.12 | 12.50 | 1.62 |
2014 | 14.02 | 12.47 | 1.55 |
)
)
)2015
12.50 | 11.25 | 1.25 | |
2016 | 12.46 | 11.04 | 1.42 |
2017 | 12.71 | 11.37 | 1.34 |
2018 | 12.92 | 11.74 | 1.18 |
2019 | 14.46 | 13.15 | 1.31 |
Source: Author's calculation from Vietinbank annual report 2011-2019
Substitute the loan figures into the following formula to calculate ∆ RA:
i=1 Who Who CK i=1 Who Who DK
∆ RA = RACK RAĐK = ∑n (W * R ) ∑n (W * R )
In fact, the bank's interest-sensitive assets and liabilities include both domestic and foreign currencies. The interest rates of these currencies are different. Therefore, to provide a uniform interest rate change, the author has overcome this problem by: at the beginning and end of the period, collecting data on total assets and interest-sensitive liabilities of domestic and foreign currencies according to the maturity ranges as prescribed by the bank, then converting the interest-sensitive assets and liabilities of foreign currencies (including principal and interest at the corresponding interest rate of foreign currencies) in each maturity range at the unified exchange rate at the time of conversion (assuming a stable exchange rate), then calculating the interest income and interest expenses converted to domestic currency, from which calculating the average lending and mobilization interest rates of assets and liabilities of foreign currencies corresponding to the domestic currency. After calculating the lending interest rate, the average mobilization interest rate of the domestic currency and the corresponding converted foreign currency, using the weighted average method to calculate the average interest rate of the Assets and Debts sensitive to interest rates, from which the interest rate fluctuation can be calculated.