Tóm tắt Luận án Research improved control mechanisms at the network nodes

Internet is a global networking system which ensures a continuity between computer systems and equipment on a large scale. Internet is growing not only in the terms of connection but also diversity of the application layers. Therefore, Internet congestion is inevitable. In order that transmission lines may be smooth, congestion control at the network nodes plays a very important role for the Internet operational efficiency and reliability for users. To study and improve congestion control mechanism at the network nodes, the introduction of the thesis comes from general research of domestic and international control situation of congestion at the network nodes in order to show the scientific and necessary nature of the thesis. Thence, the thesis may set out the study motivation and study goals. Next, the thesis has proposed study methodology and study subjects, in order to perform the study goals and finally the introduction presents the layout and contributions of the thesis.

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HUE UNIVERSITY COLLEGE OF SCIENCES NGUYEN KIM QUOC RESEARCH IMPROVED CONTROL MECHANISMS AT THE NETWORK NODES MAJOR: COMPUTER SCIENCE CODE: 62.48.01.01 ABSTRACT OF THE THESIS NGƯỜI HƯỚNG DẪN KHOA HỌC: 1. GS. TS. NGUYỄN THÚC HẢI HUE, 2015 The thesis had implemented at College of Sciences, Hue University Academic Supervisor: Prof. Dr. Nguyen Thuc Hai Assoc. Prof. Dr. Vo Thanh Tu Reviewer 1: ............................................................................................................ .............................................................................................................. Reviewer 2: ............................................................................................................ .................................................................................................................................. Reviewer 3: ............................................................................................................ .................................................................................................................................. This thesis will be reported at Hue University Date & Time ./ ././. The thesis can be found at: 1. National Library of Vietnam, Hanoi 2. Learning Resource Centers - Hue University 3. Center for Information and Library, College of Sciences, Hue University 1 PREFACE Internet is a global networking system which ensures a continuity between computer systems and equipment on a large scale. Internet is growing not only in the terms of connection but also diversity of the application layers. Therefore, Internet congestion is inevitable. In order that transmission lines may be smooth, congestion control at the network nodes plays a very important role for the Internet operational efficiency and reliability for users. To study and improve congestion control mechanism at the network nodes, the introduction of the thesis comes from general research of domestic and international control situation of congestion at the network nodes in order to show the scientific and necessary nature of the thesis. Thence, the thesis may set out the study motivation and study goals. Next, the thesis has proposed study methodology and study subjects, in order to perform the study goals and finally the introduction presents the layout and contributions of the thesis. 1. Scientific and necessary nature of the thesis There are two common options for congestion control which consists of improving the capacity of hardware devices and using software techniques. Improving the capacity of hardware devices is necessary, but it is so costly, hard to be sync and the efficiency is still not high. However, using software techniques to control congestion is highly efficient. In this technique, there are two methods of interest and development, including: improving the communication controlling protocols and advancing AQM: Active Queue Management at the network nodes [17] [28] [55]. The increase of TCP performance through variations has been deployed on the Internet and has brought enormous efficiency. However, due to the multi-standard of network types, the abundance of connecting devices and the complexity of communication applications, so it is important to have mechanisms to (AQM) active queue management at the network nodes to support regulate traffic on the network, in order to avoid and resolve congestion [7][10][51]. Active queue management (AQM) at the network nodes in order to control the number of data packets in the queue of the network nodes, by actively removing the packets until the queue is full or congestion notification when the network is in the "embryo" period of congestion to regulate traffic on the network. The stability of the queue length will make some performance parameters of TCP/IP network, such as packet loss ratio, transmission efficiency, average latency and latency variability fluctuated in a reasonable range. This will both ensure no congestion on the network and facilitate to provide and maintain the best quality of network services [7] [39] [62]. There are three approaches to solve the active queue management problem, including: Queue management based on queue length (typically RED mechanism) [22][25] [67], queue management based on packet flow to - also called traffic load (represented as BLUE mechanism) [24] [73] and queue management based on the combination of the queue length and packet flow to (typically REM mechanism) [57] [65]. In recent years, in order to improve the performance of the active queue management, in addition to the typical three mechanisms mentioned above, there are many other mechanisms announced. The works revolve around the improvement of RED, BLUE and REM mechanisms[18][26][54]. The obtained results have partially met the requirements of the problem of active queue management [54][66]. However, active queue management mechanisms is still some inherent disadvantages, such as using the linear function to determine the level of congestion and probabilistic marking/dropping the packet; and difficult to install the parameters for mechanisms to suit each different network environment [39] [59] [76]. 2 Soft Computing (SC) includes tools: fuzzy logic, neural networks, probabilistic reasoning, evolutionary computation. The objective of Soft Computing is to solve problems of approximation, approximation which is a new trend, allowing a specific problem to be exploited with the goal that the system is easy to design, low cost while ensuring accuracy and intelligence in the implementation process with an acceptable error threshold. The successful applications of Soft Computing show Soft Computing is growing strongly and playing an important role in various fields of science and engineering [36][45]. In Soft Computing Technique, fuzzy logic is considered as the best tool to achieve human knowledge, thanks to the membership functions and fuzzy systems. Therefore, fuzzy logic is used widely in many fields, especially in the field of automatic control [5] [8]. Besides, fuzzy logic, with strengths in updating knowledge through the training process to neural networks is widely and commonly used, especially in the field of computer science [53] [68]. Because of the superiority of Soft Computing so that scientists have used soft computing tools to improve software active queue management mechanisms at the network nodes in recent years [23][32][50][78]. However, there should be a combination of Soft Computing tools to promote its advantages and reduce its disadvantages for the tools to build active queue management mechanisms. Thus, these mechanisms still need to be improved so that they are simpler, flexible to control, adaptive to the network environment when implementing, ensuring fairness in obtaining or removing packets for with the arrival flow streams and maintaining its average queue length in terms of changeable network. Thus, studying to improve queue active queue management mechanisms, by combining the Soft Computing skills and modern control methods to supplement processing capabilities, the ability to make smart decisions for active queue management system at network nodes is essential and urgent. 2. Motivation of study  First, linearity of the control function of the mechanism cannot be grasped for effective control of the network and nonlinear dependence of the mechanism on static parameters cannot be adapted to changeable network status. This issue is solved by fuzzy control method in the thesis.  Second, most active queue management mechanisms have not referred to the impact of the elements in the network on the congestion controlling process, so the control of mechanisms cannot be well adaptive to the network environment. Therefore, the thesis uses adaptive fuzzy control technique to overcome such problems.  Third, recently some mechanisms of active queue management have used fuzzy reasoning to join the queue management but fuzzy control system of these mechanisms depend heavily on experts and its parameters are not addressed Update in response to each different network conditions. Therefore, the thesis applies optimized fuzzy controlling method by training the system, keeps the system following the network’s changing environment so that the mechanism can work more effectively. 3. Thesis goals First is studying and evaluation of active queue management mechanisms to find out the advantages and disadvantages of each mechanism, in order to classify and evaluate application performance for mechanisms and using fuzzy logic to improve active queue management mechanism. The results of the first goals is to perform study motivation first and will be the foundation for the theory and simulation improvements of the thesis. Second is basing on the analysis and evaluation of active queue management mechanisms in the first goal, combining theory of dynamical system control, fuzzy control and adaptive 3 control techniques to build adaptive fuzzy control, to improve active queue management mechanisms. The results of this goal is to resolve existing problems in the second study motivation of the thesis. Third is combining fuzzy reasoning and neural networks to build fuzzy neural system in order to better improve queue active management mechanisms improved in the second goal, namely that building FUZZY NEURAL NETWORK (FNN) to enhance the performance of the mechanisms improved from AFC. The outcome of this goal is to implement the third study motivation of the thesis. 4. Study method To achieve these goals, the study method in the thesis is closely combined between theoretical research and proven simulation settings. This method using the study objects consisting of the typical active queue management mechanisms, control theory, Soft Computing skills and two simulated software, Matlab and NS2 [40] which are trusted by scientific researchers. 5. Thesis layout With the goals and methods of the study mentioned above, the thesis content is outlined into three chapters. Chapter 1: Congestion control in TCP/IP network upon active queue management at network nodes – The chapter’s beginning section will present the TCP congestion control and its variants over TCP/IP network. Thence, clarifying the importance of active queue management mechanisms in congestion control in TCP/IP network. The next section of the chapter will update, analyze, evaluate and classify application of typical active queue management mechanisms and fuzzy control applied to improve these mechanisms. Thereby, the thesis gives out the existing issues in the existing active queue management mechanisms and proposes the ideas of building adaptive fuzzy control model to the problem improving active queue management mechanisms in the network nodes at the end of the chapter. Chapter 2: Improvement of active queue management mechanisms upon adaptive fuzzy control – The chapter’s beginning section presents the mathematical foundations of fuzzy logic, the next of the chapter is to survey, to assess active queue management mechanisms with the application of fuzzy control. Thence, the thesis models (AFC) adaptive fuzzy control to overcome the limitations of previous proposals. Basing on theoretical models, the thesis carries out the construction and simulation installation of improved FLRED and FLREM mechanisms. In particular, FIRED mechanism is improved from RED mechanism, FLREM mechanism improved from REM mechanism. The next part of the program is the simulation evaluation of the proposed mechanism compared to existing mechanisms. The last part of the chapter is the conclusion of the AFC significance in improving mechanism of active queue management, while pointing out the limitations of AFC and setting out the need to use neural networks to adjust the parameters of AFC. Chapter 3: Integrated fuzzy reasoning and neural networks to enhance performance of active queue management - The first section of the chapter presents the mathematical basis of neural networks. Thence, the thesis models fuzzy neural network (FNN) by integrating fuzzy control with neural networks in order to improve active queue management mechanisms. During network training, to have good academic results, the thesis proposes to use Improved Back Propagation (IBP). Basing on the theoretical model, the thesis builds innovative mechanisms of FNNRED, FNNREM. In particular, FNNRED mechanism is improved from FLRED mechanism and FNNREM is improved from FLREM mechanism. 4 The next part of the chapter is the simulation and evaluation of the proposed mechanisms compared to the mechanisms using adaptive fuzzy control AFC and mechanisms using fuzzy controller. The last section of the chapter confirms the role of fuzzy neural network (FNN) to enhance performance of active queue management. Finally, the conclusion, summary of the author's new proposals to implement the goals of the thesis. In addition, the author also offers the expected studying areas and results in the future. 6. Thesis’s contributions From the study results on the theory and demonstration through simulation, the thesis has made some specific contributions as follows:  Making application layers for existing active queue management mechanisms, and using ECN technology to improve active queue management mechanisms, the result has been published in the works [CT1][CT2]. Using the fuzzy controller to improve active queue management mechanisms, the result has been published in the works [CT3] [CT5] [CT6].  Building (AFC) adaptive fuzzy control model to improve the active queue management mechanisms at the network nodes, the result has been published in the works [CT8].  Building (RFN) fuzzy neural network model to improve the efficiency of the active queue management mechanisms at the network nodes, the result has been published in the works [CT4] [CT7]. From the above results, the thesis shows the improvement role of queue management mechanisms in the network nodes and the potential application of soft computing technique to solve the larger problems in TCP/IP. CHAPTER 1. CONGESTION CONTROL IN TCP/IP NETWORK UPON ACTIVE QUEUE MANAGEMENT AT NETWORK NODES 1.1. Congestion control in TCP/IP network 1.1.1. Operating model of TCP/IP network 1.1.1.1. Communication model in TCP/IP network 1.1.1.2. Mathematical model of TCP/IP network 1.1.2. Congestion in TCP/IP network 1.1.2.1. Congestion Causes 1.1.2.2. Principles of congestion control 1.1.2.3. Congestion control techniques 1.1.3. Congestion control of TCP 1.1.4. Congestion control with queue management 1.1.5. Active queue management The most important goal of active queue management mechanism is to prevent congestion before it actually happens, maintain stable queue length in order to reduce the loss of the packets to achieve a high data transmission flow and a low latency queue [10] [17] [18]. 5 1.1.5.1. Architecture of network nodes 1.1.5.2. Congestion control with active queue management 1.1.5.3. Advantages of active queue management 1.1.6. Explicit congestion notification technique ECN: Explicit Congestion Notification is a technique that allows a network node to provide clear feedback to the sender of congestion. 1.2. Analysis and evaluation of active queue management mechanisms 1.2.1. Queue management mechanism based on queue length In the queue management mechanism based on queue length, the congestion phenomenon is based on the instantaneous or average length of queue. 1.2.1.1. RED mechanism In 1993, Sally Floyd and et al proposed RED mechanism [25][42] for early detection of congestion, RED controls congestion at network nodes by checking the average length of the queue when packets arrive and make decisions to receive the packets, mark or reject the packets. 1.2.1.2. FRED mechanism In 1997, Dong Lin and et al proposed FRED mechanism [21] to improve RED mechanism to reduce the unfair impact in queue. 1.2.2. Management mechanism based on traffic load Active queue management mechanisms based on traffic load to predict linked the usability of transmission lines, identify congestion and provide remedies. This mechanism’s purpose is to regulate packet in network nodes to stabilize the arrival packets, in order to maintain network stability. The typical mechanisms for this group are: BLUE and SFB. 1.2.2.1. BLUE mechanism In 2002, Wu-Chang Feng et al proposed BLUE mechanism [27][81]. The main idea of BLUE is to use a probability variable 𝑝𝑚 to mark the packets as they enter the queue. This probability increases/decreases linearly depending on the dropping ratio of packet or use of transmission line. 1.2.2.2. SFB mechanism In 2001, Wu-Chang Feng et proposed SFB mechanism [72]. SFB divided queue into computing boxes, each box maintain a probability marking the packet 𝑝𝑚 similar BLUE. The boxes are organized into 𝐿 levels, each level has 𝑁 box. In addition, SFB uses 𝐿 independent hash functions, each function corresponds to one level. Each hash function reflects a stream into one of the boxes in such level. 1.2.3. Management mechanism based on queue length and traffic load The active queue management mechanisms based on the queue length control and packet flow to the network nodes, to estimate the level of use of resources (queues and bandwidth), to determine the congestion state at the network nodes. Typical mechanisms for this group like REM and GREEN mechanisms [11] [57][71]. 1.2.3.1. REM mechanism In 2001, Sanjeewa Athuraliya et al proposed REM mechanism [57] [75]. The idea of REM is to stabilize input load and link capacity of the queue, regardless of the number of users to 6 link share. 1.2.3.2. GREEN mechanism In 2002, Apu Kapadia et al proposed GREEN mechanism [6][71]. GREEN mechanism applied knowledge of the stable behaviors of TCP connections in the network nodes to the fall (or mark) packets. 1.2.4. Performance evaluation and application classifier of AQM mechanism 1.2.4.1. Performance evaluation of AQM mechanism Table 1.2. Performance evaluation of active queue management mechanisms Mechanism RED BLUE REM GREEN Throughput medium high high high Packet loss ratio high low medium low Buffer space great small medium small 1.2.4.2. Application classifier of AQM mechanisms 1.3. Application classifier of active queue management mechanism Mechanism RED BLUE REM GREEN Classifier Based on queue length     Based on traffic load     Based on transmission line efficiency     Based on the information flow     Flow control Adaption     Unadaptive Strong     Weak     1.3. Fuzzy logic application status in active queue management The purpose of the application of fuzzy logic is to simplify the design of AQM algorithm based on a degree of tolerance. The application of fuzzy logic in active queue management mechanisms has been researched by many scientists in recent years. 1.3.1. RED Mechanisms using improved fuzzy logic 1.3.1.1. FEM Mechanism In 2006, C. Chrysostomou et al proposed FEM mechanism [12][13]. FEM is built by introducing fuzzy logic into RED mechanism. 1.3.1.2. FCRED Mechanism In 2007, Jinsheng Sun et at proposed FCRED mechanism [34]. FCRED using a fuzzy controller to adjust the maximum dropping probability 𝑚𝑎𝑥𝑝 of RED, to increase the stability of the average queue length in the reference queue length range QT. 1.3.2. BLUE mechanism using improved fuzzy logic 1.3.2.1. Fuzzy BLUE
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