Intelligent real-time reactive Network Management

EC2ND 2005 ◽  
2007 ◽  
pp. 61-72
Author(s):  
Abhishek Jain ◽  
Guillaume Andreys ◽  
G. Sivakumar
Keyword(s):  
2013 ◽  
Vol 347-350 ◽  
pp. 2915-2918
Author(s):  
Mei Gen Huang ◽  
Zhi Lei Wang

The current data collection of a network device in the network management system exists low real-time and long polling cycle, this paper proposes a subnet broadcast algorithm based on SNMP. The algorithm introduces the idea of Subnet division and broadcasting, when polling, the algorithm polled network devices by sending a SNMP data broadcast packet to each subnet, which reduced the polling packets, shortened the polling cycle and lightened the burden of management station, thus the proposed algorithm improves real-time and work efficiency for the large-scale network management system.


2015 ◽  
Vol 2528 (1) ◽  
pp. 106-115 ◽  
Author(s):  
Hossein Hashemi ◽  
Khaled Abdelghany

This paper presents an integrated method for online calibration of realtime traffic network simulation models. The method integrates a time-dependent demand adjustment module and a link-based traffic flow propagation model calibration module. These modules use available realtime traffic observations to minimize inconsistency between the model estimation results and real-world observations. The modules are integrated into a real-time traffic network management system that was developed for the US-75 corridor in Dallas, Texas. Results illustrate that the online calibration method is effective in enhancing the model's consistency in the different operational conditions.


2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
B. Mokhtar ◽  
M. Azab ◽  
N. Shehata ◽  
M. Rizk

This paper presents a comprehensive water quality monitoring system that employs a smart network management, nano-enriched sensing framework, and intelligent and efficient data analysis and forwarding protocols for smart and system-aware decision making. The presented system comprises two main subsystems, a data sensing and forwarding subsystem (DSFS), and Operation Management Subsystem (OMS). The OMS operates based on real-time learned patterns and rules of system operations projected from the DSFS to manage the entire network of sensors. The main tasks of OMS are to enable real-time data visualization, managed system control, and secure system operation. The DSFS employs a Hybrid Intelligence (HI) scheme which is proposed through integrating an association rule learning algorithm withfuzzylogic and weighted decision trees. The DSFS operation is based on profiling and registering raw data readings, generated from a set of optical nanosensors, as profiles of attribute-value pairs. As a case study, we evaluate our implemented test bed via simulation scenarios in a water quality monitoring framework. The monitoring processes are simulated based on measuring the percentage of dissolved oxygen and potential hydrogen (PH) in fresh water. Simulation results show the efficiency of the proposed HI-based methodology at learning different water quality classes.


2021 ◽  
Author(s):  
Chiara Ormando ◽  
Ugo Ianniruberto ◽  
Paolo Clemente ◽  
Sonia Giovinazzi ◽  
Maurizio Pollino ◽  
...  

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