The Experts below are selected from a list of 24342 Experts worldwide ranked by ideXlab platform
Pyeongsoo Mah - One of the best experts on this subject based on the ideXlab platform.
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nanomon an adaptable sensor Network Monitoring Software
International Symposium on Consumer Electronics, 2007Co-Authors: Haeyong Kim, Pyeongsoo MahAbstract:In this paper, we present a sensor Network Monitoring Software, named NanoMon, which has a flexible architecture and supports for various user requirements of sensor Network applications in an adaptive manner. With NanoMon, users can specify custom GUI plug-ins and internal module parameters by using a simply describable configuration file; and it can be automatically integrated to NanoMon framework to support user-specific sensor Network applications. NanoMon employs a widely used database MySQL, to concurrently and correctly manage sensing data and node information of several types of sensor Network applications. To show flexibility and adaptability of NanoMon, we implemented two WSN applications- home Monitoring and parking lot Monitoring systems. By selecting a WSN application name specified in the configuration file, users can easily and dynamically change GUI and internal module parameters such as sensor types and database locations of NanoMon used to display the status of WSN.
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nanomon a flexible sensor Network Monitoring Software
International Conference on Advanced Communication Technology, 2007Co-Authors: Junkeun Song, Jinwon Kim, Keeyoung Shin, Pyeongsoo MahAbstract:In this paper, we present a sensor Network Monitoring Software, named NanoMon, which has a highly flexible architecture and is able to support various user requirements arising in their individual wireless sensor Network applications in an adaptive manner. With NanoMon, users can specify their own sensor types and custom GUI components by using a simply describable configuration file; and it can be automatically integrated to NanoMon GUI framework to support user-specific sensor Network applications. NanoMon employs a widely used database, MySQL, to manage sensing data and node information of several types of sensor Network applications and also to provide sensor data history and conditional sensor data look up functions. NanoMon provides well defined packet form at and transmission procedure to communicate with various sensor Network platforms with no dependency on any specific WSN platforms; and it can publish sensing data received from sensor Networks to other external Monitoring devices via Internet using well defined XML packet format and transmission procedure. For platform independency, NanoMon is implemented in Java language.
Mohd Mahdzir, Mohd Firdaus - One of the best experts on this subject based on the ideXlab platform.
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Performance analysis of Network Monitoring Software in wireless Network / Mohd Firdaus Mohd Mahdzir
Universiti Teknologi Mara Perlis, 2019Co-Authors: Mohd Mahdzir, Mohd FirdausAbstract:Network Monitoring Software is a tools that are able to monitor the behaviours and provide alertness for any failing component of computer Network. Network Monitoring Software usually deals with common issues such as hard to be configured and challenges in determined the best Network Monitoring that suite with requirements due to dumping of Network Monitoring Software. The objectives of this research are to identify the Network Monitoring Software that are easy to be configured and to analyse the performance of Network Monitoring Software in terms of response time and packet loss. Network Monitoring Software that were involved in this research are PRTG, OpManager, Zabbix and LibreNMS. Two experiments were conducted for the second objectives which are launching TCP attacks for response time and UDP attacks for packet loss. Both attacks consists of four scenario which are 15 threads, 30 threads, 45 threads and 60 threads. The result for the first objective belongs to PRTG. PRTG provides an attractive of Graphical User Interface (GUI) which all configuration can be done through the interface only. For Monitoring website, users need to add the device which was the targeted website and choose HTTP sensor for response time and PING for packet loss compared to others Network Monitoring Software there were a lot of configuration need to be done. For response time and packet loss, both of it belong to Zabbix. The evaluation was based on lowest average response time. The first scenario, Zabbix detected as second lowest response time with a value 1785 msec while for second scenario and third scenario, Zabbix detected the lowest average response time with a values of 2372 msec and 5889 msec. When fourth scenario was launched, the result were same with others Network Monitoring Software which is the average response time was able to be detected because of the website was down. In terms of packet loss, Zabbix show the consistent reading of packet loss from the 15 threads until 60 threads. The first scenario show challenges between Zabbix and PRTG which are 7.43 % and 5 % while LibreNMS was far away with 14.43 %. For the second scenario, the range between Zabbix, PRTG and LibreNMS was closed with the values 51.39 %, 48 % and 45.07 %. Same goes to third scenario, the range was between 64.44 %, 60 % and 66.6 % while the last scenario only show the challenges between Zabbix and LibreNMS only with a value 80 % and 86.72 %. Thus, Zabbix show consistency of detection percentage average packet loss
Mohd Firdaus - One of the best experts on this subject based on the ideXlab platform.
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performance analysis of Network Monitoring Software in wireless Network mohd firdaus mohd mahdzir
2019Co-Authors: Mohd Mahdzir, Mohd FirdausAbstract:Network Monitoring Software is a tools that are able to monitor the behaviours and provide alertness for any failing component of computer Network. Network Monitoring Software usually deals with common issues such as hard to be configured and challenges in determined the best Network Monitoring that suite with requirements due to dumping of Network Monitoring Software. The objectives of this research are to identify the Network Monitoring Software that are easy to be configured and to analyse the performance of Network Monitoring Software in terms of response time and packet loss. Network Monitoring Software that were involved in this research are PRTG, OpManager, Zabbix and LibreNMS. Two experiments were conducted for the second objectives which are launching TCP attacks for response time and UDP attacks for packet loss. Both attacks consists of four scenario which are 15 threads, 30 threads, 45 threads and 60 threads. The result for the first objective belongs to PRTG. PRTG provides an attractive of Graphical User Interface (GUI) which all configuration can be done through the interface only. For Monitoring website, users need to add the device which was the targeted website and choose HTTP sensor for response time and PING for packet loss compared to others Network Monitoring Software there were a lot of configuration need to be done. For response time and packet loss, both of it belong to Zabbix. The evaluation was based on lowest average response time. The first scenario, Zabbix detected as second lowest response time with a value 1785 msec while for second scenario and third scenario, Zabbix detected the lowest average response time with a values of 2372 msec and 5889 msec. When fourth scenario was launched, the result were same with others Network Monitoring Software which is the average response time was able to be detected because of the website was down. In terms of packet loss, Zabbix show the consistent reading of packet loss from the 15 threads until 60 threads. The first scenario show challenges between Zabbix and PRTG which are 7.43 % and 5 % while LibreNMS was far away with 14.43 %. For the second scenario, the range between Zabbix, PRTG and LibreNMS was closed with the values 51.39 %, 48 % and 45.07 %. Same goes to third scenario, the range was between 64.44 %, 60 % and 66.6 % while the last scenario only show the challenges between Zabbix and LibreNMS only with a value 80 % and 86.72 %. Thus, Zabbix show consistency of detection percentage average packet loss.
Mohammad Abdollahi Azgomi - One of the best experts on this subject based on the ideXlab platform.
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a scalable multi core aware Software architecture for high performance Network Monitoring
Security of Information and Networks, 2009Co-Authors: Mahdi Dashtbozorgi, Mohammad Abdollahi AzgomiAbstract:This paper proposes a high-performance Network Monitoring Software architecture. The proposed architecture, named DashNMon, is able to employ multi-core CPUs in an efficient and scalable manner. Multi-core awareness is a distinguished property of this architecture. In spite of most existing cluster-based solutions, DashNMon can be used with common-off-the-shelf (COTS) multi-core CPUs. DashNMon is based on DashCap high-performance packet capture and transmission Software solution, which we have recently introduced. Using the proposed architecture, it is possible to design and implement high-performance multi-threaded NIDSs or application-layer firewalls, completely in the user space and with better utilization of computational resources of multi-processor/multi-core systems. In this paper, after a brief overview of DashCap, we introduce the scalable Software architecture of DashNMon and the results of the experiments carried out using a prototype web filter to benchmark its performance and scalability.
Mohd Mahdzir - One of the best experts on this subject based on the ideXlab platform.
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performance analysis of Network Monitoring Software in wireless Network mohd firdaus mohd mahdzir
2019Co-Authors: Mohd Mahdzir, Mohd FirdausAbstract:Network Monitoring Software is a tools that are able to monitor the behaviours and provide alertness for any failing component of computer Network. Network Monitoring Software usually deals with common issues such as hard to be configured and challenges in determined the best Network Monitoring that suite with requirements due to dumping of Network Monitoring Software. The objectives of this research are to identify the Network Monitoring Software that are easy to be configured and to analyse the performance of Network Monitoring Software in terms of response time and packet loss. Network Monitoring Software that were involved in this research are PRTG, OpManager, Zabbix and LibreNMS. Two experiments were conducted for the second objectives which are launching TCP attacks for response time and UDP attacks for packet loss. Both attacks consists of four scenario which are 15 threads, 30 threads, 45 threads and 60 threads. The result for the first objective belongs to PRTG. PRTG provides an attractive of Graphical User Interface (GUI) which all configuration can be done through the interface only. For Monitoring website, users need to add the device which was the targeted website and choose HTTP sensor for response time and PING for packet loss compared to others Network Monitoring Software there were a lot of configuration need to be done. For response time and packet loss, both of it belong to Zabbix. The evaluation was based on lowest average response time. The first scenario, Zabbix detected as second lowest response time with a value 1785 msec while for second scenario and third scenario, Zabbix detected the lowest average response time with a values of 2372 msec and 5889 msec. When fourth scenario was launched, the result were same with others Network Monitoring Software which is the average response time was able to be detected because of the website was down. In terms of packet loss, Zabbix show the consistent reading of packet loss from the 15 threads until 60 threads. The first scenario show challenges between Zabbix and PRTG which are 7.43 % and 5 % while LibreNMS was far away with 14.43 %. For the second scenario, the range between Zabbix, PRTG and LibreNMS was closed with the values 51.39 %, 48 % and 45.07 %. Same goes to third scenario, the range was between 64.44 %, 60 % and 66.6 % while the last scenario only show the challenges between Zabbix and LibreNMS only with a value 80 % and 86.72 %. Thus, Zabbix show consistency of detection percentage average packet loss.