A Matrix Usage for Load Balancing in Shortest Path Routing

The Open Shortest Path First (OSPF) protocol is a hierarchical interior gateway protocol (IGP) for routing in Internet Protocol. Traffic flows routed along shortest path and splits the load equally at nodes where a number of outgoing links on the shortest paths to the same destination IP address. Network operator defines shortest paths based on a link weights value assigned to each link in the network. The OSPF link weight-setting problem seeks a set of link weights to optimize a cost function and network performance, typically associated with a network congestion measure. This research highlight the importance of managing network resource and avoiding congested point in the current widely deployed shortest path routing. The previous Evenly Balancing Method (EBM) and Re-Improved Balancing Method (R-IBM) used demand matrix, which requires constant monitoring of routers with high time executions in the optimization process. The problems are to find another matrix that can replace or minimize the usage of demand matrix with low time executions process. A new proposed Matrix Usage Method (MUM) is developed. MUM selects the shortest path routing in order to provide a balancing load and optimized the usage of link in the network. The simulation results show that the routing performance of the new proposed method MUM is better than the routing performance of the previous Evenly Balancing Methods (EBM) and Re-Improved Balancing Method (R-IBM) due to providing counting selection technique in the shortest path routing. MUM times executions are also improved comparing with the previous work

Development and Alpha Testing of EzHifz Application: Al-Quran Memorization Tool

Learning to memorize the Quran presents a challenge. This paper reports the development and alpha testing of a mobile application called “EzHifz” for Quran memorization based on the VARK learning style. The application received positive feedback for user acceptance testing and heuristic testing. The Fleiss kappa coefficient (κ) results for user acceptance testing show a very good level of agreement (κ = 0.850). Heuristic testing results show that κ = 0.731 for content, manual guide, memorization activities, performance information, and tasmik assessment attributes, while κ = 0.727 for presentation design, interactivity, multimedia elements, attraction, and motivation attributes. These results show a good level of agreement, which indicates that the EzHifz application meets the requirements of design and development based on the attributes evaluated. A combination of memorizing techniques in the application helps strengthen the use of preferred VARK learning styles. The techniques support the use of multiple senses that could facilitate the process of memorizing the Quran independently. This study contributes to the novel design and evaluation of the Quran memorization application based on the Quran memorization model. The application supports the teaching and learning of Quran memorization where it allows students to select their preferred VARK learning style with the technique of memorizing the Quran. This mobile application learning approach based on VARK’s learning style has the potential to be implemented in the process of memorizing the Quran as well as retaining memory through the use of memory senses in support of the learning materials developed