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Failover for Multiple-Controller with Failure Detection Method in Software Defined Network on Distributed Switch Decision Ryan Lingga Wicaksono; Maman Abdurohman; Hilal Hudan Nuha
eProceedings of Engineering Vol 10, No 3 (2023): Juni 2023
Publisher : eProceedings of Engineering

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Abstract

Abstract-Hardware is important for the system to be used, with the increase in a lot of hardware such as switches and connected hosts can cause a decrease in quality on the network. Systems that have more than one core device can use two scenarios namely, load balancing and failover. Software Defined network allows the separation of the control plane and the data plane in the network. Based on it, it provides scalability and centralized control. In addition, by using these properties, clustering controllers consisting of 3 controllers are also applied. The clustering controller system uses ONOS with the aim of stabilizing and improving network performance. Communication between the data plane and the control plane is necessary to detect, calculate, and insert rules that create new paths. The system used to be able to achieve connectivity is scattered everywhere. System by utilizing a centralized controller, one can anticipate one point of failure. Service interruptions due to the failure of the communication network link that occurs are unavoidable circumstances. This study proposes a failover mechanism on the controller. Moving from a failed master controller to a slave controller results in a delay time from the average of each switch connected in the failed master controller. The average delay time in each different traffic background results in a different value from each traffic background. In the background traffic of 500mb/s has an average delay time of 0.159ms, the background traffic of 750mb/s has an average delay time of 0.194ms, and background traffic of 900mb/s has an average delay time of 0.309ms. The delay time value occurs in 1 master controller failure. Different results occurred in 2 controller failures, when using a background traffic of 500mb/s, the average delay time value was 0.203ms, the background traffic was 750mb/s the average delay time was 0.265ms and the background traffic was 900mb/s, the average delay time was 0.346ms. The failure handling mechanism on the master controller is intended to overcome delay time when the backup controller takes over the tasks of the new master controller. Keywords-software defined network; failover; controller; delay time
Performance and Efficiency Testing Analysis of Database Systems in Academic Information Systems: Analisis Pengujian Kinerja dan Efisiensi Sistem Basis Data dalam Sistem Informasi Akademik Kusuma Dewi, Utami; Lingga Wicaksono, Ryan; Dwifebri Purbolaksono, Mahendra; Satria, Villy
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.438

Abstract

This study examines the performance and efficiency of database systems within academic information systems, acknowledging the increasing demand for responsiveness and reliability in managing complex academic data. As educational institutions increasingly rely on digital systems, performance testing becomes essential to ensure that these systems continue to support the learning environment effectively. Guided by the ISO/IEC 25010 standard, the research focuses on evaluating three key aspects of performance efficiency: time behavior, resource utilization, and capacity. Using JMeter, a range of user load scenarios were simulated, and the results were examined through Control Quality Charts and Nelson Rules to detect underlying issues affecting system performance. The findings reveal that 82.5% of queries demonstrated good time behavior, and 80% performed well in resource usage. However, half of the tests related to capacity highlighted the need for further improvements. Some queries experienced delays and consumed excessive CPU and memory resources, indicating areas where optimization is required. These insights highlight the importance of refining queries and managing resources more effectively to ensure a seamless user experience. Future research should consider automated optimization, machine learning-based performance prediction, and system scalability, especially in more dynamic and distributed academic environments.