Mahmoud Baklizi

Multi-constrained routing using genetic algorithm

Different multimedia conferencing and telephony systems are available worldwide. The development of a method that can process more than one quality of service routing parameter and can meet communication system requirements is essential to support these systems within high-speed networks. This paper briefly presents a significant method that solves network routing protocol problems, such as finding the shortest and the most feasible path based on the genetic algorithm.

Evaluation of E-Learning websites in Jordan universities based on ISO/IEC 9126 standard

The aim of this paper is to evaluate E-Learning website of Jordan universities based on ISO/IEC 9126 standard which uses six main characteristics to evaluate software, and each characteristic includes its own sub-characteristics. The results show that the average of quality in E-Leaning websites is 65.45 %. The results reflect the students opinion about the website. The result might be used to improve the quality of E-Learning website for those universities.

Genetic-based routing algorithm with priority constraints

This paper presents an algorithm for finding an efficient routing path in mobile ad hoc network on the basis of multi-constraints with priority by using genetic algorithm that encoding the paths in chromosomes to help the algorithm to select an efficient path among the feasible paths. The mutation operation keeps the genetic variety of the chromosomes and also provides the ability of searching that obtains good result and increases the rate of convergence. The objective of this research work is to control the selection path to select the most efficient path on the basis of the priority percentage that redirects the algorithm to preferred constraints. The algorithm selects a path according to the important requirements that improve the performance of applications. On the basis of the multi-constraints observed when considering cost priority, the results showed that low cost path selected do not maximise the cost value. In addition, the algorithm with priority constraints presents more efficient path which presents a lower cost compared with no priority constraints.

Intelligent Routing Algorithm Using Genetic Algorithm (IRAGA)

In this study, Intelligent Routing Algorithm using Genetic Algorithm (IRAGA) is proposed to find the feasible path based on multi-constraint by using GA which presented the paths in chromosomes to help the algorithm to find the efficient path among the available paths. The proposed crossover-refining operation keeps the genetic variety of the chromosomes and also improve the ability of searching which obtains the good result and increases the rate of convergence between the chromosomes. The objective of this research work is to decrease the time of route selection and select the most efficient path on the basis of fitness value. IRAGA offers a better solution in a few iteration compared to other genetic algorithm-based routing protocols, such as genetic load balancing routing, adaptive routing method based on GA with QoS, and multi-constraint QoS Unicast Routing using GA, in terms of the time taken to find the feasible path.

A Survey in Active Queue Management Methods According to Performance Measures

Congestion generally occurs when the amount of packets arriving at the router buffer exceeds the available resources. This causes several problems, such as, increase in the probability of high queuing delay in the buffer, and increase in the probability of losing packets of the buffer. Congestion control method is one of the key that keeps any network efficient and reliable for the users. Many researchers were proposed in the literature over theses years for the efficient control of congestion that occur in the network. The congestion is monitored and controlled at an early stage before the router overflows, using a set of parameters. These methods are formally referred to as Active Queue Management (AQM) methods, which were proposed to overcome the congestion. These methods depend on maintaining the router buffer dynamic. When the packets arrival increases, the amount of packets dropped increases to prevent the accumulation of packets and to maintain the stability of the buffer. This procedure is implemented by assigning a fixed value at which the aql should be maintained. Packet dropping begins before the buffer overflows in reference to the fixed value and the current value of aql. Although such methods perform well in steady buffer, they do not adapt well when aql changes over time. In this paper, In this paper an comprehensive survey is made on the AQM methods that are proposed and the values and short tumbles is existing.

Performance Analysis of the Proposed Adaptive Gentle Random Early Detection Method under NonCongestion and Congestion Situations

The developments in computer networks in recent days such as the internet have increased rapidly. Connections of these networks necessitate resources in order to send their data to their prospective destinations. Further, the connections require high speed router buffers which they route data in high speed. Congestion is one of the main issues that occur at the router buffer cause deterioration of the network performance, i.e. increasing average waiting time, decreasing throughput, etc. Gentle Random Early Detection (GRED) is one of the known congestion control algorithms proposed to detect congestion before the router buffer overflows. In fact, GRED improves the setting of the parameters for the maximum threshold position (max threshold) at the router buffer and the maximum value for the packet dropping probability (D max ). This paper proposes an Adaptive GRED algorithm that detects congestion at router buffers in an preliminary stage, and enhances the parameters setting of the max threshold and the D max . During congestion, the simulation results reveal that the Adaptive GRED drops fewer packets than GRED, and it marginally offers better performance results than that of GRED.