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HUE UNIVERSITY UNIVERSITY OF SCIENCES LE VAN HOA RESEARCH OF THE FAIRNESS CONTROL IN OPTICAL BURST SWITCHING NETWORKS MAJOR: COMPUTER SCIENCE CODE: 9480101 SUMMARY OF PHD THESIS Supervisors: Assoc Prof Dr Vo Viet Minh Nhat Dr Nguyen Hoang Son HUE, YEAR 2019 The thesis has been completed at: University of Sciences, Hue University Supervisor: Assoc Prof Dr Vo Viet Minh Nhat, Hue University Dr Nguyen Hoang Son, University of Sciences, Hue University Reviewer 1: Assoc Prof Dr Dang Van Duc Institute of Information Technology, VietNam Academy of Science and Techonology Reviewer 2: Assoc Prof Dr Truong Thi Dieu Linh Hanoi University of Science & Technology Reviewer 3: Assoc Prof Dr Huynh Xuan Hiep Can Tho University The thesis will be presented at the Committee of Hue University, to be held by Hue University at The thesis can be found at the follow libraries:  National Library of VietNam  Library Information Center University of Science, Hue University PREFACE The urgency of the topic The continuous development of Internet in recent decades, along with the explosion of communication services, has increased demands for bandwidth This poses a major challenge in finding suitable communication technologies to enhance the communication capabilities of new generation networks Optical networks, together with the technology of wavelength division multiplexing (WDM), have provided an effective solution to meet these requirements [24], [36] Optical communication, from its inception in early 1990s to the present, has undergone many generations of development: from the initial model of wavelength routing with end-to-end dedicated lightpaths to the model of optical packet switching [36], which has been proposed recently, with the idea taken from electronic packet switching networks However, with some technological limitations, such as not being able to produce optical buffers (similar to RAM in electronical networks) or the optical packet switches in nanosecond speed, optical packet switching has not yet been come true A compromise solution is the optical burst switching (OBS) model A typical feature of the communication in optical burst switching (OBS) networks is that the burst control packet (BCP) is separated from its data burst In other words, to transmit an optical burst, a BCP is formed and sent ahead an offset time that is sufficient to reserve resources and configure switches at intermediate nodes along the path that its burst will pass from source to destination In addition, the OBS network reserves some channels (wavelengths) for BCPs, while the remaining channels are used for burst transmission Thus, the BCP transmission is completely separate from its burst in terms of space (on separated transmission channels) and also in time (sent ahead an offset time) [65] With the transmission mode, it is clear that OBS networks not need optical buffers to temporarily store optical burst while waiting for the switching at core nodes, nor it require the switches in nanosecond However, this way of communication also places a pressure on how a control packet can promptly reserve resources and successfully configure switches at the core nodes for its following burst That is the task of operations such as resource reservation, scheduling, contention resolution In addition, another issue that is also interested by many researchers in the field of OBS networks is how to ensure the fairness of the different flows which share the same links within OBS networks In OBS networks, fairness is studied in three main directions: delay fairness [69], througphut fairness [53] and distance fairness [10] Ensuring the fairness of flows which share resources in OBS networks is very significate, on the one hand to ensure the commitment of service quality, and other hand to optimize the communication performance per flow and/or entire network (based on data loss rate, bandwidth utilization rate, end-to-end delay rate, etc.) Research motivation There have been a number of studies on fairness issues in OBS networks that can be classified into two main approaches which based on the location of implementation: - The fairness solutions at edge nodes and - The fairness solutions at core nodes With the first group, there are two main research directions: (1) delay fairness and (2) throughput fairness; while in the second group, the fairness problem is known mainly as distance fairness In OBS networks, the ingress node plays an important role in controlling fairly flows, because: The ingress node controls the traffic of flows (end-to-end connections) fairly before transfers them into the core network; Only the ingress nodes have buffers, so the fair control of delay, throughput is made easier; and The core node does not have buffer, so fair handling at the core node almost depends on the control operations of its ingress node Based on these characteristics, the thesis focuses on the study of fairness control at the ingress node, with two main activities: delay control and throughput control Research objectives  Studying and proposing some improvements in the burst assembly with delay reduction to reduce the communication delay over OBS networks;  Studying and proposing a solution of the burst assembly with delay fairness for delay-based QoS difference, delay reduction and delay fairness of different priority flows  Studying and proposing a solution with throughput fairness, which can be applied to different types of incoming flows with Poisson and nonPoisson distribution  Studying and proposing a solution of burst padding to improve bandwidth utilization and throughput fairness Contribution of thesis The main contributions of the thesis include:  Proposing two burst assembly models with delay fairness, named iBADR [CT2] and OBADR [CT3], in order to reduce the burst assembly delay  Proposing a burst assembly model with delay fairness, named BADF [CT5], that results in delay-based QoS difference, delay reduction and delay fairness of different priority flows  Proposing a bandwidth allocation model with throughput fairness, named TFBA [CT6], that is applicable to different types of incoming traffics  Proposing a burst padding solution, named QDBAP [CT7], in order to improve bandwidth utilization and throughput fairness CHAPTER 1: AN OVERVIEW OF THE FAIRNESS IN OPTICAL BURST SWITCHING NETWORKS 1.1 Switching models in optical communication Optical switches are divided into types: optical channel switching (OSC), optical packet switching (OPS) and optical burst switching (OBS), in which OBS has inherited the advantages from the two others, where no optical buffer and high-speed optical switche are needed 1.2 Communication principle of OBS networks In OBS networks, different types of incoming data are aggregated into data bursts before being sent (Figure 1.2a) At egress nodes, the bursts will be disassembled into original packets which are then delivered to their destination (Figure 1.2b) Figure 1.1 The process of assembling and disassembling at the edge OBS nodes 1.3 Operations within OBS networks 1.3.1 Assembling Burst assembling is a method of aggregating packets (such as IP packets, ATMs, etc.) from different access networks into larger burst at ingress nodes of the OBS network 1.3.2 Signaling There are two main types of signaling protocols in OBS networks: JIT and JET, in which JET is the signaling protocol implemented for most OBS networks because it makes better use of the bandwidth 1.3.3 Scheduling When a control packet arrives at a core node, a scheduling algorithm is called to schedule its following burst on an output link There are three main scheduling mechanisms: (1) scheduling without void filling; (2) scheduling with void filling and (3) group scheduling 1.3.4 Contention resolution As with any packet-switched network, contention also appears in OBS networks That is when two burst simultaneously dispute the same resource at the same link (wavelength) Possible solutions to contentions in OBS networks include: wavelength conversion, using FDL, deflection routing and combination of the above solutions 1.4 Fairness issues in OBS networks 1.4.1 The fairness concept in OBS networks According to Denda et al [38], fairness is known as the satisfaction of individuals in the process of resource allocation In the OBS network, fairness issues are distinguished by: delay fairness, throughput fairness and distance fairness, which are considered at edge and core nodes (Figure 1.7) FAIRNESS IN OBS NETWORKS EDGE NODES DELAY FAIRNESS CORE NODES THROUGHPUT FAIRNESS DISTANCE FAIRNESS Figure 1.7 Fairness classification based on consideration location 1.4.2 Delay fairness Fairness delay refers to establishing fairly a buffering delay (including assembling delay and offset time) for bursts of different QoS classes 1.4.3 Throughput fairness Throughput fairness refers to the fair allocation of bandwidth between flows which share the same link 1.5 Summary chapter This chapter has introduced an overview of OBS networks and its operations, in which the burst assembly at the ingress node is focused on analysis because it plays an important role in the fairness of entire network This chapter has also analyzed and evaluated the methods of fairness control that have been published so far That is the basis for the thesis to identify the research objectives, as well as propose the improved architecture of ingress node with additional functional modules to ensure the implementation of the fairness solutions proposed by the thesis 1.5.1 Distance fairness Distance fairness refers to dealing with contentions fairly (as measured by the data loss rate) based on the path length (number of hops) from source to destination 1.6 Research objectives The thesis focuses on the fairness issues at the ingress node to improve the efficiency of delay and throughput fairness, with four main objectives: Improving and proposing new solution of burst assembly for delay reduction that applies to each individual queue Improving and proposing new solution of delay fairness control that applies to multiple queues with different QoS levels Improving and proposing new solution of throughput fairness control that applies to flows of Poisson and Non-Poisson distribution Proposing new solutions of burst padding to improve communication performance CHAPTER 2: BURTS ASSEMBLY FOR DELAY REDUCTION AND DELAY FAIRNESS 2.1 Model of burst assembly for delay reduction 2.1.1 Issue of the burst assembly delay The end-to-end delay of a burst transmitted over an OBS network is mainly caused by four components: (1) the assembly delay at ingress node, (2) the offset time for resource reservation of the control packet, (3) burst switching delay at core nodes and (4) propagation delay over core network Two first delays are collectively formed a common element which há the name the buffering delay; two latters usually not change corresponding to a given implemented protocol Therefore, the proposals usually focus on reducing the buffering delay 2.1.2 Related works 2.1.2.1 Analysis of the published methodes of burst assembly for delay reduction There are published methodes of burst assembly for delay reduction as shown in Table 2.1 Table 2.1 Comparison of the published methodes of burst assembly for delay reduction IE-BADR POQA JK-BADR BADR-EAT MTBA-TP BASTP Timer-based Timer- Timer- Timer-based Hybrid Hybrid based based fixed fixed fixed fixed fixed addaptivei Estimation Based on the Based on Based on Based on the Based on the Based on method average speed the the density of arrival speed M last Burst assembly method Assembly threshold of packets length of estimated the last M of the last burst arriving in the the last error of the packets packet assemblies estimated M bursts previous To t1 + To – Ta To time period assembly Reduced delay To To To 2.1.2.2 Comparison and analysis of simulation results Simulation is done with the following objectives:  Comparing the average estimattion error rate (Formula 2.8) between the methodes of burst assembly for delay reduction  L M RE  j 1 j  Lej / L j M  (2.8) where M is the number of assembly times, Lej and Lj are the estimation size and the completed size of burst j  Comparing the number of redundant packets transferred for the next burst of 100 consecutive assembly times between the methodes of burst assembly for delay reduction  Analyzing the way to select the thresholds for BASTP, the best methode of burst assembly for delay reduction The simulations are installed on a PC with 2.4 GHz Intel Core CPU, 2G RAM Packets arriving at the ingress node have a Poisson distribution with exponentially random size in the range of [500, 1000] bytes Traffic loads ariving at the queues varies from 0.1 to 0.9 Simulations are performed in second) Data were extracted from NS2 [71] with support package obs-0.9a Other parameters include Ta = ms, To = ms a Comparison of the average estimation rate Figure 2.3 shows a comparison of the average estimation error between the methods of burst assembly for delay reduction The simulation results show that the estimation error of the statistical-based methods such as BASTP, BADR-EAT and POQA being lower than that of other methods Figure 2.3 Comparison of the average estimation error rate of IE-BADR, JK-BADR, POQA, BADR-EAT, MTBA-TP and BASTP with normalized load to 0.5 between iBADR and previous methods, where the number of redundant bursts of iBADR is significant Figure 2.10 Comparison of the number of redundant packets in 100 consecutive assembly times 2.1.3.5 Comments Based on simulation results, iBADR achieved a estimation error rate lower than that of BASTP, but it generates relatively many redundant packets as shown in Figure 2.10 The method of burst asembly for delay reduction iBADR has been published in [CT2] 2.1.4 Method of burst assembly for delay reduction OBADR 2.1.4.1 Description The OBADR (Optimal Burst Assembly for Delay Reduction) method is an improvement of iBADR, in addition to applying the burst length estimation method TW-EWMA with flexibly adjusted , the burst assembly process is a combination of the assembly stages:  Stages 1: When the first packet arrives at a queue, the timer of the queue is triggered The control packet is only sent to the core network when the timer reaches the threshold Tw, which is the size of the time window The estimated length ( Le ) is also calculated based on TWEWMA with flexibly adjusted   Stages 2: The burst assembly process continues, but now based on the estimated length threshold The burst is only completed when the number of packets arriving in the queue reaches the threshold Le 2.1.4.3 Comparison and analysis of simulation results The simulation parameters for this part are similar to Section 2.1.2.2 11 a Comparison of the average estimation error rate Figure 2.11 shows that the average estimation error rate ( RE ) of OBADR is lower than that of all previous methods Figure 2.11 Comparison of the average estimation error rate between OBADR and previous methods b Comparison of the number of redundant packets in 100 consecutive assembly times Figure 2.13 Comparison of the number of redundant packets in 100 consecutive assembly times As shown in Figure 2.13, OBADR does not generate redundant packets This is due to the estimated lengths used as the length threshold in burst assembly 2.1.5 Effect of the factor α on OBADR 2.1.5.1 Investigating the variation of α depending on the load changes With the normalized load changing from 0.1 to 0.9 and α changing from 0.1 to 0.9, the simulation results show that the estimation error is minimum when α has a distribution in the range of (0.4, 0.6) Thus, the fixed α setting is clearly not suitable when arriving load is varied 12 2.1.5.2 Comparing the burst assembly efficiency when α is fixed and varied Simulation results show that dynamic α results in a better average estimation error than fixed α (α = 0.5) when the assembly time is small (from 2.5 ms to 5.5 ms) 2.1.5.3 Comments Based on the simulation results, flexible adjustment of α values (such as Equation 2.12) depending on the incoming traffic rate has increased the efficiency of estimating the complete burst length This result also confirms the effect of flexible adjustment of α according to the incoming traffic rate The results of this study were published in [CT4] 2.1.6 Effect of OBADR on scheduling 2.1.6.1 Analyze the effect of OBADR on scheduling based on Engset method 2.1.6.2 Comparison of the analysis model and simulation result As shown in Figure 2.19, OBADR achieves a lower probability of burst loss compared to the traditional model in theory and simulation Figure 2.2 Comparison on the probability of burst loss between OBADR and traditional model 2.1.6.3 Comments Based on the analysis and simulation results, OBADR has proven to be the most efficient burst assembly method in term of low estimation error, reduced delay and minimized burst loss rate The method of burst assembly for delay reduction OBADR was published in [CT3] 2.2 Model of burst assembly for delay fairness 2.2.1 Related works The models of burst assembly for delay reduction share a common idea of sending the control packet early before the burst is completed Among these 13 models, only POQA [69] is associated with service differentiation Specifically, the authors in [69] have set different offset times for bursts with different priority classes and adjusted the assembly times so that the higher priority burst is, the shorter the buffering time As the example shown in Figure 2.21, the burst with the highest priority class class0 has the smallest assembly time Ta(0) and the largest offset time To(0), while the lowest priority class class2 has the longest assembly time Ta(2) and the smallest offset time To(2) class0 To(0) Ta(0) class1 To(1) Ta(1) class2 To(2) Ta(2) Figure 2.3 An example of time thresholds and offset times for classes 2.2.2 Method of burst assembly for delay fairness BADF 2.2.2.1 Introduction to the delay fairness in OBS networks With the concept of delay fairness proposed in [69], higher priority burst will have a shorter buffering time However, this interpretation has not yet shown the nature of the fair response to individuals in the notion of fairness Therefore, the thesis supplements the concept of delay fairness as follows: Delay fairness is the satisfaction on delay between different priority bursts, such that the average ratio of the end-to-end delay to their limited delay is approximative In addition, in order to meet the requirement for prioritizing delays in OBS networks, the following two constraints were added The higher the priority is, the lower the end-to-end delay is; and The end-to-end delay of a burst is not greater than its limited delay (Example: RTT of IP packets carried in a burst) Thus, the concept of "delay fairness" added by the thesis implies the concept of delay fairness proposed in [69] 2.2.2.2 Index of delay fairness Let D(i) be the average delay that packets must wait in queue i before being aggregated into a burst and Ta(i) is the assembly time of queue i, xi = D(i)/Ta(i) will reflect the delay of the packets in queue i The thesis proposes a formula to calculate the DFI delay fairness index for different priority bursts based on 14 Jain's formula in [39] as follows:  x i 1 i i n DFI   n i 1 ( i xi ) n (2.22) Fairness will increase as DFI approaches 1, and equals when  x1   x2    n xn , where i is the weight of xi, < i < and  n i 1 i  2.2.2.3 Method of 2-stage burst assemly The Burst Assembly for Delay Fairness (BADF) method is also based on the idea of sending control packets early (see Section 2.1.3 and 2.1.4), but adding new points in the 2-stage assembly model Stage is a estimated time threshold-based burst assembly and Stage is a estimated length thresholdbased burst assembly The 2-stage assembly model is in detail as follows: Stage 1: when the first packet arrives at queue i, the timer is triggered The control packet is only sent when the timer reaches the estimated time threshold Te(i) = Ta(i) – To(i) The estimated burst length is calculated based on the TWEWMA method [23]: (2.25) Le (i)  Ta (i)  (1   (i))  avg (i)   (i)  cur (i)  During this period, the value of α(i) is adjusted to increase/decrease depending on the rate of packet arrival at queue i, calculated by α(i) = cur(i)/(avg(i) + cur(i)), instead of being fixed as in [23] Stage 2: the burst assemly algorithm continues until either the length threshold Le (i ) or time threshold Ta(i) is reached 2.2.2.5 Comparison and analysis of simulation results Assuming that incoming packets belong to three priority classes (K = 3), have Poisson distribution and have exponential distribution sizes in the range [500, 1000] bytes; three priority queues 1, and are therefore used with the offset times set to 0.3, 0.2 and 0.1 (ms), respectively Simulation objectives include:  Comparing the DFI index between BADF and POQA;  Analyzing the impact of the delay fairness on the assembly time Ta(i) and the buffering delay; 15  Comparing the estimation error (Formula 2.8) between BADF and POQA a Comparison of the DFI index between BADF and POQA Figure 2.4 Comparison of the DFI index between BADF and POQA b Analyzing the impact of the delay fairness on the assembly time Ta(i) and the buffering delay As shown in Figure 2.25, the assembly time Ta(i) decreases as the incoming traffic of packets increases, with class0 during the simulation period [0.4, 0.6] and with class2 during the simulation period [0.7 , 0.9] Figure 2.5 Comparison of Ta(i) between priority classes with BADF c Comparison of the estimation error between BADF and POQA Figure 2.60 Comparison of the estimation error between BADF and POQA 16 Figure 2.30 shows a comparison of the estimation error rate (Formula 2.8) between BADF and POQA, where the estimation error of BADF is much smaller than that of POQA Figure 2.7 Comparison of the bandwidth wasting rate between BADF and POQA Figure 2.8 Comparison of re-send rate between BADF and POQA 2.2.2.6 Comments The BADF algorithm has been proven to effectively control the delay fairness between different QoS queues, based on DFI index, estimation error, and re-send rate One drawback of BADF, however, is the high bandwidth wastage rate about 12% on average (Figure 2.31) However, if compared with the re-send rate of POQA (about 30% on average), BADF is better The BADF algorithm and the above results have been published in [CT5] 2.3 Summary chapter This chapter presents two proposed models of delay reduction iBADR [CT2] and OBADR [CT3], and a model of delay fairness BADF [CT5] Based on simulation results, iBADR and OBADR achieve lower delay than previous proposals BADF also achieves the delay fairness which is almost optimal, while reducing the delay and minimizing the estimation error on the queues 17 CHAPTER 3: THROUGHPUT FAIRNESS BASED ON BANDWIDTH ALLOCATION AND BURST PADDING 3.1 Model of bandwidth allocation with throughput fairness 3.1.1 Introduction to fair bandwidth allocation Fair bandwidth allocation, also known as rate fairness, refers to allocating bandwidth for connections proportional to the provided bandwidth to the available bandwidth [53] 3.1.2 Related works So far, the models of fair bandwidth allocation in OBS networks are based on the max-min fair bandwidth allocation model in IP networks [16], such as MMFP and RFP 3.1.3 Bandwidth allocation with throughput fairness TFBA 3.1.3.1 Architecture of the ingress node supporting QoS Consider an ingress node with an architecture as shown in Figure 3.3 Figure 3.1 Architecture of the ingress node supporting QoS 3.1.3.2 Maximum bandwidth ratio for each link in OBS networks Table 3.1 Maximum throughput rate per link with different normalized loads Normalized load 0.5 0.6 0.7 0.8 0.9 1.0 Maximum throughput 0.48616 0.572186 0.67152 0.72123 0.7213 0.71945 3.1.3.3 Description of TFBA The idea of TFBA is to adjust actual throughput close to the fair allocation bandwidth to ensure fair bandwidth allocation between flows The process of allocating bandwidth with fairness throughput includes steps: Step 1: Determine the fairness rate Fi for each connection 18 If the incoming traffic of flow i varies significantly, the bandwidth is first divided among the connections, the ratio Fi of each connection is determined as the minimum of the actual throughput (Ai) and the fairly allocated bandwidth Connections with actual throughput less than the allocated bandwidth will not participate in the redundant bandwidth sharing process in the next time The allocation is continued until the allocated bandwidth (m) does not change from the previous time (m = mprev) or all connections are satisfied (m = 0) Step 2: Determine the fairly allocated bandwidth ABi for each connection Let Bw be the maximum output bandwidth, the fair allocation bandwidth for connection i is determined by Equation 3.4 (3.4) ABi  Fi  Bw where Fi is the fairness ratio determined in Step Step 3: Measure the actual throughput ATi of each connection Actual throughput is determined by Formula 3.5 ATi  p w (i ) / Tw (i ) (3.5) where pw(i) is the number of packets arriving in the time window Tw(i) Step 4: Handling burst contention The burst contention problem is resolved based on a comparison between ATi and ABi to determine whether the incoming burst is in an overrate (overloaded) flow If ATi > ABi, the incoming burst is in an overrate flow and will be dropped to reserve resources for the bursts in an underrate (unoverloaded) flow Conversely, if ATi < ABi, the arrival burst is in underrate flow and the ratio ATi /ABi will be considered If the value of ATi /ABi is smaller than that of ATj/ABj, the scheduled burst will be dropped Conversely, if the value of ATi /ABi is greater than ATj/ABj, the incoming burst will be dropped 3.1.3.4 Index of throughput fairness The fair bandwidth allocation approach proposed by the thesis also comes from the idea of max-min fairness, but based on the ratio between actual throughput and provided bandwidth, instead of probability of loss as in [67], [53] Specifically, set yi = ATi / ABi is the ratio between the actual throughput (ATi) and the fair allocation bandwidth (ABi) of flow i Based on Jain's formula in [39], the thesis proposes the throughput fairness index (TFI) as follows: 19 TFI    y i 1 i i n  (3.6) ni 1 ( i y i ) n where σi is the weight that represents the actual used bandwidth compared to the provided bandwidth between the flows, < σi < and  n i 1 i  3.1.3.6 Comparison and analysis of simulation results Simulation objectives include: - Comparing the byte loss rate between connections and the average byte loss rate among TFBA, MMFP and RFP - Comparing the fairness based on TFI among TFBA, MMFP and RFP Because the simulation objective only considers the byte loss rate of the connections which share the same (or group of) output link(s), the Dumbbell simulation network as shown in Figure 3.5 is sufficient to evaluate the effectiveness of the proposed algorithm Figure 3.2 Simulation network topology a Comparison of the byte loss rate between connections Figure 3.3: Comparison of the byte loss rate between connections with TBFA in cases: (1) The total load does not exceed the link capacity and (2) the load of connection spikes beyond the link capacity 20 Figure 3.4 Comparison of the average byte loss rate on connections of TFBA, RFP and MMFP b Comparison of the fairness based on TFI Figure 3.5 Comparison of the fairness based on TFI among TFBA, MMFP and RFP 3.1.4 Analyzing the impact of TFBA on scheduling at output link 3.1.4.1 Analytical model The Markov model [11] is used to analyze the impact of TFBA on scheduling at output link 3.1.4.2 Comparison of the effectiveness between analytical model and simulation results The simulation results in Figure 3.15 show that the probability of burst loss of the analysis model is similar to the simulation results (Figure 3.7) Figure 3.6 Comparison of the effectiveness between analytical model and simulation results with TFBA 3.1.5 Comments Based on analysis and simulation results, the TFBA method has proven to 21 be more efficient than previous methods, especially applicable to many types of incoming flows, instead of only Poisson flow as in [67], [53] The TFBA method has been published in [CT6] 3.2 Padding model for effective bandwidth utilisation and throughput fairness 3.2.1 Related works 3.2.2 Padding method 3.2.2.1 Introduction to burst padding The burst padding model QDBAP (QoS Differentiation Burst Assembly with Padding) proposed in the thesis is described as shown in Figure 3.17, in which the burst padding is done by picking packets from low QoS queues to add into higher QoS burst Figure 3.7 An example of a burst padding model with three layers: (a) before the burst padding and (b) after the burst padding 3.2.2.2 Padding policy The proposed padding policy is as follows: Only take packets from the low QoS queue to pad in the higher QoS burst Selecting a packet from a low QoS queue is on a first-come, first-served basis Low QoS packets are padded to the tail of the higher QoS burst Only take packets from the low QoS queue that its control packet has not yet been sent 3.2.2.4 Comparison and analysis of simulation results Simulation objectives include:  Comparing wasted bandwidth when using padded bytes and  Compare the throughput fairness based on TFI (Formula 3.8) a Comparison of wasted bandwidth 22 Figure 3.8 Comparison of the number of padding bytes between QDBAP and POQA b Comparison of the throughput fairness Figure 3.9 Comparison of the throughput fairness based on TFI between QDBAP and POQA Figure 3.10 Comparison of the throughput fairness (based on the ratio of the actual load and the bandwidth capacity) between POQA and QDBAP 3.2.3 Comments Based on the analysis and simulation results, the QDBAP method has proven to be effective in increasing bandwidth usage and throughput fairness for different QoS flows The results of this study were published in [CT7] 3.3 Summary chapter This chapter introduced a method of bandwidth allocation for throughput fairness TFBA, which can be applied to many types of incoming flows, greatly increasing the network performance and assuring throughput fairness (this result has been published in [CT6]) In addition, this chapter also presents a proposed burst padding method that increases the efficiency of bandwidth 23 utilization and throughput fairness between QoS classes (This result was published in [CT7]) CONCLUSION The OBS network is a promising solution for the next-generation Internet, because OBS overcomes the today's technology limitations of optical packet switching and exploits flexible bandwidth better than optical channel switching One of the issues of OBS network research is how to control the fairness between different service flows With that motivation, the thesis has focused on fairness control models and algorithms in OBS network with different approaches Achievements of the thesis include: Reviewing, analysing, evaluating and classifying of the fairness control methods in OBS networks Based on the points of existence of the previous published findings, the thesis proposes improvements and new solutions for better functional modules, fairness control models and algorithms Proposing models of burst assembly for delay reduction, iBADR [CT2] and OBADR [CT3], in order to reduce the buffering delay of assembly queues, in which OBADR achieves minimum error and accurate estimation of generated burst size Proposing a burst assembly model, BADF [CT5], that ensures the delay fairness which is better than a previously proposed method (POQA) At the same time, BADF is more effective than POQA in terms of delay, average error rate The dissertation also proposes a metric index (DFI) in order to measure the delay fairness efficiency of proposed methods Proposing a model of throughput-based fair bandwidth allocation, TFBA [CT6], that can be applied to priority flows with Poisson and non-Poisson distribution TFBA has a lower loss rate than the two previously proposed methods (MMFP and RFP), and increase the throughput fairness efficiency based on TFI, a metric index proposed by the dissertation Propose a burst padding method, QDBAP [CT7], in order to increase the efficiency of bandwidth utilization and throughput fairness In addition, QDBAP also reduces the bandwidth wastage rate in comparison with POQA 24 LIST OF RESULTS PULISHED BY AUTHOR CT1 Le Van Hoa, Vo Viet Minh Nhat, Nguyen Hoang Son (2016), “Analysis of the algorithms of burst assembly for delay reduction at the edge node of obs networks”, Hue University College of Sciences Journal of Science, ISSN 2354-0842, vol 6, no.1, pp 9-20 CT2 Le Van Hoa, Vo Viet Minh Nhat, Nguyen Hoang Son (2017), “An improved approach of burst assembly for delay reduction at edge OBS nodes”, Hue University Journal of Science, ISSN 1859-1388, vol 126, no 2A, pp.19-30 CT3 Vo Viet Minh Nhat, Le Van Hoa, Nguyen Hoang Son (2017), “A model of optimal burst assembly for delay reduction at ingress OBS nodes”, Turkish Journal of Electrical Engineering & Computer Sciences (SCIE), vol 25, no 5, pp 3970-3982 CT4 Le Van Hoa, Vo Viet Minh Nhat, Nguyen Hoang Son (2018), “Effects of data flow properties to the efficiency of burst assembly for delay reduction at ingress OBS nodes”, Proceedings of the 11th National Conference of Fundamental and Applied Information Technology Research (FAIR), pp: 57-64 CT5 Vo Viet Minh Nhat, Le Van Hoa, Le Manh Thanh (2018), “On the delay fairness through the burst assembly for service difference”, ETRI Journal (SCIE), vol 40, no 3, pp 347-354 CT6 Le Van Hoa, Vo Viet Minh Nhat, Le Manh Thanh (2018) “Throughput-based Fair Bandwidth Allocation in OBS Networks”, ETRI Journal (SCIE), vol 40, no 5, pp 624-633 CT7 Vo Viet Minh Nhat, Le Van Hoa, Nguyen Hoang Son, Le Manh Thanh (2018), “A Model of QoS Differentiation Burst Assembly with Padding for Improving the Performance of OBS Networks”, Turkish Journal of Electrical Engineering & Computer Sciences (SCIE), vol 26, no 4, pp 1783-1795 25 ... Le Manh Thanh (2018), “On the delay fairness through the burst assembly for service difference”, ETRI Journal (SCIE), vol 40, no 3, pp 347-354 CT6 Le Van Hoa, Vo Viet Minh Nhat, Le Manh Thanh... Journal (SCIE), vol 40, no 5, pp 624-633 CT7 Vo Viet Minh Nhat, Le Van Hoa, Nguyen Hoang Son, Le Manh Thanh (2018), “A Model of QoS Differentiation Burst Assembly with Padding for Improving the Performance... delay reduction 2.1.1 Issue of the burst assembly delay The end-to-end delay of a burst transmitted over an OBS network is mainly caused by four components: (1) the assembly delay at ingress