As a result, the fractional CCR model (3.1) can be transformed into the following linear programming model, known as the multiplier form of the CCR model:
$
max,; f 9 (µ) = ^ µ 6 y 69
6Z[
Provided:
$
#
^ v T x T9 = 1
TZ[
#
(3.2)
^ µ 6 y 6` − ^ v T x T` ≤ 0, j = 1, … . , n
6Z[ TZ[
µ 6 ≥ ε, r = 1, … . . , s
v T ≥ ε, i = 1, … . . , m
The dual model of model (3.2) is widely known as the “envelope model form” of the CCR model, which has the following form:
$#
min g 9 (θ jj k , s " , s l) = θ 9 jj k − ε m^ s "+ ^ s l o
b cc d ,e,$ f ,$ g
Provided:
q
6
6Z[
T
TZ[
T
^ λ ` x T ` + s l= θ 9 jj k x T 9 , i = 1, … , m
`Z[
q
(3.3)
6
^ λ ` y 6` − s " = y 69
, r = 1, … , s
`Z[
λ `≥ 0, j = 1, … , ns " , s l≥ 0
6 T
0 < ε ≤ 1
The scalar θ 9 jjk , corresponds to the bank's TE o's efficiency score . For inefficient banks, the value of θ 9 jjk <1 represents the proportion of inputs that the bank could use to produce the current level of output, so 1- θ 9 jjk corresponds to the BANK's level of technical inefficiency o's. The value of θ 9 jjk is bounded by 0< θ 9 jjk ≤ 1 . Every
A non-zero value of λ ` indicates that an efficient bank is in the reference set of bank 'o'.
With the VRS assumption, the CCR model (3.3) becomes BCC:
$#
min g 9 (θ tj j , s " , s l ) = θ tj j − ε m^ s "+ ^ s l o
b sc c ,e,$ f ,$ g
Provided:
q
9 6
6Z[
T
TZ[
^ λ ` x T ` + s l= θ tj j x
T9 T9
`Z[
∑
q
`Z[
λ ` y 6` − s " = y 69
6
q
^ λ `` = 1
`Z[
(3.4)
λ ` , s l , s "≥ 0
T 6
`Z[
The difference in the BCC model compared to the CCR is the convex constraint ∑ q λ ` = 1 about
essentially ensuring that an inefficient bank is only 'scored' against other banks.
`Z[
similar sized goods. Since the BCC model imposes an additional constraint ∑ q λ ` = 1 , the region
The feasibility of the BCC model is a subset of the CCR model. The relationship between the optimal objective values of the CCR and BCC models is θ ∗ tjj ≥ θ ∗ jjk . Therefore, a bank is
9 9
It is assumed that the efficiency under the CCR model will also be efficient under the BCC model. The SE (Efficiency to Scale) measure for the 'o' bank can be calculated as the ratio of the measured efficiency
according to the CCR-I model with the efficiency measured according to the BCC-I model, i.e. θ ∗ jjk /θ ∗ tjj .
9 9
3.5.2. Regression method
According to Gujarati (2004), if the correlation coefficient between independent variables exceeds 0.8, there is a possibility of high multicollinearity in the model. Then the sign of the regression coefficient in the model may change, leading to incorrect research results. Therefore, before running the regression model, the thesis checks the correlation coefficient between independent variables in the model, and also checks whether there is multicollinearity by using the variance inflation factor (VIF). If the VIF coefficient of the variables is less than 10, multicollinearity is not a serious problem affecting the estimated results of the model (Gujarati 2004).
This study performed regression models using panel data estimation methods such as fixed effects and random effects. Then, the author used Hausman test to determine the appropriate estimation method. Heteroscedasticity and autocorrelation tests were also performed with the selected model.
In case these phenomena exist, the author will continue to estimate the model using the two-step System GMM (SGMM) method of Arellano & Bover (1995) and Blundell & Bond (1998). This method is commonly used in estimating linear dynamic panel data or panel data with heteroscedasticity and autocorrelation.
The estimates of the GMM method will be suitable for use in the following cases:
• Panel data with small T, large N (lots of observations with few time points)
• There is a linear relationship between the dependent variable and the explanatory variables.
• Dynamic models with one or both sides of the equation containing a delay variable
• The independent variables are not strictly exogenous, meaning that they may be correlated with the residuals (current or previous) or have endogenous variables in the model.
• There is a problem of heteroscedasticity or autocorrelation in measurement errors (idiosyncratic disturbances)
• Existence of individual fixed effects
• Heteroscedasticity and autocorrelation exist within each subject (but not between subjects)
This research model is a dynamic model with a lagged dependent variable in the equation, which will cause endogeneity problems. In addition, due to the simultaneity in the relationship between the independent variable and the dependent variable (specifically between the non-traditional banking activity variable and the efficiency variable), the research model has an endogeneity problem. The endogeneity problem can cause biased estimates in the analysis. In addition, non-traditional banking activities can be affected by past, present and vice versa business performance. In other words, the causal relationship can be two-way and the non-traditional banking activity variables and business performance can be correlated with the error terms. Time-invariant bank-specific characteristics (fixed effects) can also be correlated with the errors. The presence of a lagged dependent variable in the model (spontaneous characteristics) can also cause autocorrelation. Furthermore, the study's data set covers a short time period (9 years) and relatively longer banking units (13 banks). Therefore, the study using the two-step SGMM of Arellano & Bover (1995) and Blundell & Bond (1998) is appropriate in this case.
SGMM is an efficient research method in previous studies. Blundell and Bond (1998) demonstrated that SGMM has smaller variance and is more efficient, thus improving the accuracy in the estimator. SGMM consists of a system of two equations according to Gurbuz (2013)
In which: Y: dependent variable X: independent variable δ: Unobservable factors
v: error
SGMM is used to address the endogeneity of some explanatory variables through instrumental variables. The efficiency of the estimation depends on the suitability of the instrumental variables. The Sargan test or Hansen test for over-identifying property allows to check the suitability of the instrumental variables. In theory, the Hansen test in 2-step estimation is considered more efficient than the Sargan test in 1-step estimation (Roodman, 2009). Therefore, the Hansen test is used to test the over-identification of the instrumental variables. This test determines whether there is a correlation between the instrumental variables and the residuals in the model or not by testing the hypothesis H 0 : the instrumental variables are suitable (satisfy the over-identifying condition). When accepting the hypothesis H 0 (p-value
> alpha) means that the instrumental variables used in the model are appropriate.
In addition, the quadratic autocorrelation (AR2) test is also an important test of the quadratic correlation of the residuals in the model. In the AR2 test, the hypothesis H 0 is tested : there is no quadratic correlation of the residuals. When the p-value is greater than alpha, we accept H 0 : the residuals of the model do not have the phenomenon of quadratic autocorrelation, meaning that the model meets the requirements.
Chapter 3 Summary
Based on the research of Akhigbe & Stevenson (2010) and previous studies, the model of non-traditional banking operations affecting banking efficiency at Vietnamese commercial banks is built with 7 research hypotheses. The model is as follows:
EFF it = α 0 + α 1 EFE it - 1 + β 1 (1)
In which, the dependent variable EFF measures bank efficiency.
With the aim of studying the factors affecting the performance of commercial banks, inheriting the research of Rogers & Sinkey (1999) and previous studies, this study builds a research model on the factors affecting the performance of commercial banks in Vietnam with 6 research hypotheses. The model is as follows:
NII it = α 0 + α 1 NII it-1 + β 1 NIM it + β 2 DEP it + β 3 ETA it + β 4 LLP it + β 5 BRANCH it + u it (2)
The study uses secondary data, which are audited financial statements of 13 Vietnamese commercial banks in the 9 years from 2011 to 2019, to calculate internal variables within the bank. Data for calculating external factors in the macro environment are collected from the International Monetary Fund, the World Bank and the General Statistics Office of Vietnam. The study uses the two-step SGMM (SystemGMM) of Arellano & Bover (1995).
and Blundell & Bond (1998) for complete estimates.
CHAPTER 4: EXPERIMENTAL RESEARCH RESULTS AND DISCUSSION
4.1. General assessment of banking efficiency and non-traditional banking activities of listed commercial banks in Vietnam in the period 2011 - 2019
4.1.1. General assessment of effectiveness
The input of the DEA model is the input resources of a commercial bank such as: mobilized capital, labor, facilities, technical equipment quantified by 3 cost variables including: Interest payment cost (X1): includes interest payment cost and equivalents representing the capital factor in the input of joint stock commercial banking activities; Salary cost (X2): is the cost paid to employees representing the labor factor in the input of commercial banking activities; Other costs (X3): are non-interest costs excluding employee costs representing the equipment factor, technical facilities, etc.
The output of the DEA model includes 02 variables reflecting the business performance of a commercial bank: Interest income (Y1): is income from credit activities and equivalents; Non-interest income (Y2): includes service income and other operating income.
The DEA analysis results of the commercial banks in this study are presented in Table 4.1.
Table 4.1. DEA analysis results on technical efficiency of commercial banks
2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | Medium | |
ACB | 0.9061 | 0.9168 | 0.8673 | 0.8052 | 0.8413 | 0.8787 | 0.9401 | 0.8869 | 0.8502 | 0.8770 |
BIDV | 0.8657 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9851 |
CTG | 0.9475 | 0.9302 | 0.9377 | 0.9274 | 0.9475 | 0.8841 | 0.9516 | 0.9231 | 1.0000 | 0.9388 |
EIB | 0.9503 | 1.0000 | 0.9507 | 0.8580 | 0.8591 | 0.8661 | 0.8328 | 0.7784 | 0.8275 | 0.8803 |
HDB | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
MBB | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9953 | 0.9557 | 1.0000 | 1.0000 | 0.9946 |
NCB | 0.8443 | 0.8281 | 0.8890 | 0.9293 | 1.0000 | 0.8853 | 0.9395 | 0.8019 | 0.8986 | 0.8907 |
SHB | 0.8867 | 0.9221 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9787 |
STB | 0.9019 | 0.9185 | 0.9107 | 0.9121 | 0.8371 | 0.7423 | 0.8483 | 0.7801 | 0.7626 | 0.8460 |
TCB | 0.9615 | 0.9157 | 0.8510 | 0.9048 | 0.9925 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9584 |
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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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Using Traditional Formulas to Understand the Question Pattern
TPB
1.0000 | 1.0000 | 1.0000 | 0.9668 | 0.9983 | 0.9189 | 0.9537 | 0.8799 | 0.9522 | 0.9633 | |
VCB | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9767 | 0.9957 | 1.0000 | 1.0000 | 0.9969 |
VPB | 0.8335 | 0.9295 | 0.8675 | 0.8729 | 0.9314 | 1.0000 | 0.9356 | 1.0000 | 1.0000 | 0.9301 |
Source: analysis results from STATA 16 software
Figure 4.1. Technical efficiency of Vietnamese commercial banks over the years
1.0000
0.9500
0.9000
0.8500
0.8000
0.7500
0.7000
2011
ACB SHB
2012 2013
BIDV STB
2014 2015
EIB TPB
2016
HDB VCB
2017
MBB VPB
2018
2019
CTG
TCB
NCB
Source: analysis results from STATA 16 software
It can be seen that in the period 2011 - 2019, the performance of Vietnamese commercial banks fluctuated continuously over the years. HDB's average TE is the highest among commercial banks in Vietnam, reaching a maximum of 1. In contrast, STB's average TE is the lowest, at 0.8460.
HDB's average SE is the highest among commercial banks in Vietnam, reaching a maximum of 1. In contrast, CTG's average SE is the lowest, at 0.9403.
Table 4.2. DEA statistical analysis results of Vietnamese commercial banks
Bank
Technical efficiency (TE) | Scale efficiency (SE) | |||||
Mean | Max | Min | Mean | Max | Min | |
ACB | 0.8770 | 0.9401 | 0.8052 | 0.9725 | 0.9999 | 0.9108 |
BIDV | 0.9851 | 1.0000 | 0.8657 | 0.9851 | 1.0000 | 0.8657 |
CTG | 0.9388 | 1.0000 | 0.8841 | 0.9403 | 1.0000 | 0.8841 |
EIB | 0.8803 | 1.0000 | 0.7784 | 0.9532 | 1.0000 | 0.8624 |
HDB | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
MBB | 0.9946 | 1.0000 | 0.9557 | 0.9986 | 1.0000 | 0.9895 |
NCB | 0.8907 | 1.0000 | 0.8019 | 0.8967 | 1.0000 | 0.8019 |
SHB | 0.9787 | 1.0000 | 0.8867 | 0.9911 | 1.0000 | 0.9221 |
STB | 0.8460 | 0.9185 | 0.7423 | 0.9915 | 0.9985 | 0.9771 |
TCB | 0.9584 | 1.0000 | 0.8510 | 0.9911 | 1.0000 | 0.9615 |
TPB | 0.9633 | 1.0000 | 0.8799 | 0.9633 | 1.0000 | 0.8799 |
VCB
0.9969 | 1.0000 | 0.9767 | 0.9969 | 1.0000 | 0.9767 | |
VPB | 0.9301 | 1.0000 | 0.8335 | 0.9975 | 1.0000 | 0.9913 |
Source: analysis results from STATA 16 software
4.1.2. General assessment of non-traditional banking activities
Non-traditional banking activities at Vietnamese commercial banks have developed quite diversely. In general, all banks fully implement the four basic groups of non-traditional banking activities. The first is the group of fee-based services including: payment, treasury, trust services, agency, consulting services, guarantees, cooperation services, insurance agencies, custody services, safekeeping services, other services such as services related to opening and managing accounts, deposit-related services, balance confirmation services, information retrieval, electronic banking, document copying, etc. It can be seen that this is the most diverse non-traditional activity in the group. The remaining groups include foreign exchange trading, securities trading and other activities.
It is noteworthy that the scale of NHPTT activities has been continuously improved over the years:
Figure 4.2. Proportion of non-interest income to total operating income of commercial banks from 2011 - 2019
100%
80%
60%
40%
20%
0%
-20%
-40%
-60%
-80%
2019
2018
2017
2016
2015
2014
2013
2012
2011
VCB
BIDV
CTG
TCB
VPB
MBB
EIB
HDB
TPB
STB
ACB
SHB
NCB
Source: Author's own synthesis from financial statements of banks Looking at the chart, we see that the group of leading banks VCB, BIDV and CTG have
stable growth but not much breakthrough. Meanwhile, banks such as TCB, VPB, MBB, IEB, STB and SHB all showed a clear growth trend in the 3 years 2017-2019. However, there are also banks that go against the general trend such as HDB with very good growth in the period 2012-2014 but then showed signs of decline. Similar to HDB is NCB, which grew well in 2013, 2014, 2017, 2018 but declined in 2019.