973 research outputs found
A multi-objective particle swarm optimized fuzzy logic congestion detection and dual explicit notification mechanism for IP networks.
Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2006.The Internet has experienced a tremendous growth over the past two decades and with that growth have come severe congestion problems. Research efforts to alleviate the congestion problem can broadly be classified into three groups: Cl) Router based congestion detection; (2) Generation and transmission of congestion notification signal to the traffic sources; (3) End-to-end algorithms which control the flow of traffic between the end hosts. This dissertation has largely addressed the first two groups which are basically router initiated. Router based congestion detection mechanisms, commonly known as Active Queue Management (AQM), can be classified into two groups: conventional mathematical analytical techniques and fuzzy logic based techniques. Research has shown that fuzzy logic techniques are more effective and robust compared to the conventional techniques because they do not rely on the availability of a precise mathematical model of Internet. They use linguistic knowledge and are, therefore, better placed to handle the complexities associated with the non-linearity and dynamics of the Internet. In spite of all these developments, there still exists ample room for improvement because, practically, there has been a slow deployment of AQM mechanisms. In the first part of this dissertation, we study the major AQM schemes in both the conventional and the fuzzy logic domain in order to uncover the problems that have hampered their deployment in practical implementations. Based on the findings from this study, we model the Internet congestion problem as a multi-objective problem. We propose a Fuzzy Logic Congestion Detection (FLCD) which synergistically combines the good characteristics of the fuzzy approaches with those of the conventional approaches. We design the membership functions (MFs) of the FLCD algorithm automatically by using Multi-objective Particle Swarm Optimization (MOPSO), a population based stochastic optimization algorithm. This enables the FLCD algorithm to achieve optimal performance on all the major objectives of Internet congestion control. The FLCD algorithm is compared with the basic Fuzzy Logic AQM and the Random Explicit Marking (REM) algorithms on a best effort network. Simulation results show that the FLCD algorithm provides high link utilization whilst maintaining lower jitter and packet loss. It also exhibits higher fairness and stability compared to its basic variant and REM. We extend this concept to Proportional Differentiated Services network environment where the FLCD algorithm outperforms the traditional Weighted RED algorithm. We also propose self learning and organization structures which enable the FLCD algorithm to achieve a more stable queue, lower packet losses and UDP traffic delay in dynamic traffic environments on both wired and wireless networks. In the second part of this dissertation, we present the congestion notification mechanisms which have been proposed for wired and satellite networks. We propose an FLCD based dual explicit congestion notification algorithm which combines the merits of the Explicit Congestion Notification (ECN) and the Backward Explicit Congestion Notification (BECN) mechanisms. In this proposal, the ECN mechanism is invoked based on the packet marking probability while the BECN mechanism is invoked based on the BECN parameter which helps to ensure that BECN is invoked only when congestion is severe. Motivated by the fact that TCP reacts to tbe congestion notification signal only once during a round trip time (RTT), we propose an RTT based BECN decay function. This reduces the invocation of the BECN mechanism and resultantly the generation of reverse traffic during an RTT. Compared to the traditional explicit notification mechanisms, simulation results show that the new approach exhibits lower packet loss rates and higher queue stability on wired networks. It also exhibits lower packet loss rates, higher good-put and link utilization on satellite networks. We also observe that the BECN decay function reduces reverse traffic significantly on both wired and satellite networks while ensuring that performance remains virtually the same as in the algorithm without BECN traffic reduction.Print copy complete; page numbering of 105-108 incorrect
Congestion Control for Adaptive Satellite Communication Systems with Intelligent Systems
With the advent of life critical and real-time services such as remote operations over satellite, e-health etc, providing the guaranteed minimum level of services at every ground terminal of the satellite communication system has gained utmost priority. Ground terminals and the hub are not equipped with the required intelligence to predict and react to inclement and dynamic weather conditions on its own. The focus of this thesis is to develop intelligent algorithms that would aid in adaptive management of the quality of service at the ground terminal and the gateway level. This is done to adapt both the ground terminal and gateway to changing weather conditions and to attempt to maintain a steady throughput level and Quality of Service (QoS) requirements on queue delay, jitter, and probability of loss of packets.
The existing satellite system employs the First-In-First-Out routing algorithm to control congestion in their networks. This mechanism is not equipped with adequate ability to contend with changing link capacities, a common result due to bad weather and faults and to provide different levels of prioritized service to the customers that satisfies QoS requirements. This research proposes to use the reported strength of fuzzy logic in controlling highly non-linear and complex system such as the satellite communication network. The proposed fuzzy based model when integrated into the satellite gateway provides the needed robustness to the ground terminals to comprehend with varying levels of traffic and dynamic impacts of weather
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Intelligent based Packet Scheduling Scheme using Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) Technology for 5G. Design and Investigation of Bandwidth Management Technique for Service-Aware Traffic Engineering using Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) for 5G
Multi-Protocol Label Switching (MPLS) makes use of traffic engineering (TE)
techniques and a variety of protocols to establish pre-determined highly
efficient routes in Wide Area Network (WAN). Unlike IP networks in which
routing decision has to be made through header analysis on a hop-by-hop
basis, MPLS makes use of a short bit sequence that indicates the forwarding
equivalence class (FEC) of a packet and utilises a predefined routing table to
handle packets of a specific FEC type. Thus header analysis of packets is not
required, resulting in lower latency. In addition, packets of similar
characteristics can be routed in a consistent manner. For example, packets
carrying real-time information can be routed to low latency paths across the
networks. Thus the key success to MPLS is to efficiently control and distribute
the bandwidth available between applications across the networks.
A lot of research effort on bandwidth management in MPLS networks has
already been devoted in the past. However, with the imminent roll out of 5G,
MPLS is seen as a key technology for mobile backhaul. To cope with the 5G
demands of rich, context aware and multimedia-based user applications, more
efficient bandwidth management solutions need to be derived.
This thesis focuses on the design of bandwidth management algorithms, more
specifically QoS scheduling, in MPLS network for 5G mobile backhaul. The
aim is to ensure the reliability and the speed of packet transfer across the
network. As 5G is expected to greatly improve the user experience with
innovative and high quality services, users’ perceived quality of service (QoS)
needs to be taken into account when deriving such bandwidth management
solutions. QoS expectation from users are often subjective and vague. Thus
this thesis proposes the use of fuzzy logic based solution to provide service aware and user-centric bandwidth management in order to satisfy
requirements imposed by the network and users.
Unfortunately, the disadvantage of fuzzy logic is scalability since dependable
fuzzy rules and membership functions increase when the complexity of being
modelled increases. To resolve this issue, this thesis proposes the use of neuro-fuzzy to solicit interpretable IF-THEN rules.The algorithms are
implemented and tested through NS2 and Matlab simulations. The
performance of the algorithms are evaluated and compared with other
conventional algorithms in terms of average throughput, delay, reliability, cost,
packet loss ratio, and utilization rate.
Simulation results show that the neuro-fuzzy based algorithm perform better
than fuzzy and other conventional packet scheduling algorithms using IP and
IP over MPLS technologies.Tertiary Education Trust Fund (TETFUND
Chronology of the development of Active Queue Management algorithms of RED family. Part 1: from 1993 up to 2005
This work is the first part of a large bibliographic review of active queue management algorithms of the Random Early Detection (RED) family, presented in the scientific press from 1993 to 2023. The first part will provide data on algorithms published from 1993 to 2005
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