Reliable Network Management Research Articles

Attention mechanism enhanced LSTM networks for latency prediction in deterministic MEC networks

In deterministic mobile edge computing (MEC) networks, accurately predicting latency is critical for optimizing resource allocation and enhancing quality of service (QoS). This paper introduces a novel approach leveraging attention mechanism enhanced long short-term memory (LSTM) networks to predict latency in MEC networks. The proposed model integrates attention mechanisms into LSTM networks to capture temporal dependency and emphasize relevant features in the input data, thereby improving the prediction accuracy. T extensive experiments are conducted by using practical MEC network data, demonstrating that the proposed approach significantly outperforms traditional LSTM and other baseline models in terms of prediction accuracy and computational efficiency. Additionally, we analyze the impact of various configurations in the attention mechanism and LSTM on the model performance, providing insights into the optimal settings. The findings of this study contribute to the advancement of latency prediction techniques in deterministic MEC networks, facilitating more efficient and reliable network management.

Method and Algorithm for Topology Automatic Discovery in Complicated Passive Optical Network Architecture

Abstract Passive Optical Network (PON) plays an important role in optical access network due to its characteristics of resources saving and network protocol transparency. In the process of running and operating a PON system, a reliable network management system (NMS) is necessary. In order to acquisition the management object, NMS should perform topology automatic discovery process with the help of embedded software. Aiming at various types of the convergence network’s topology and the complicated architecture, this article proposes a topology automatic discovery algorithm to promote the network management. We have designed and verified the automatic discovery method on our simulation platform with SNMP-Agent-enabled OLTs.

Weighted Round Robin Load Balancer to Enhance Web Server Cluster in OpenFlow Networks

Web server clusters require a reliable network management for increasing the quality of service (QoS). A load balancer system installed in a software-defined network (SDN) is one method that can improve the performance and availability of web server services. SDN is a dynamic and a programmable network management approach, and one protocol that supports it is OpenFlow. This research aims to design and analyse a model of a load balancer on OpenFlow networks, implementing a Weighted Round Robin (WRR) algorithm. The analysis process is conducted by measuring the value of a QoS web server performance parameters, such as response time, throughput, HTTP success, and loss connection. The results showed the WRR algorithm can be implemented for balancing a network system with dynamic resource allocation. The weight workload of each service can be obtained from the needs and existing network resources. The performance of a load balancer on an OpenFlow network is 57% better than in a traditional one for testing of response time conducted in a high connection. However, the throughput and HTTP success connection decreased by 2% and 10%, respectively, while HTTP loss connection increased by 49%.

Testing network management software

A fourth wave of computer-based network management systems is here. Experienced network managers worry about the reliability of software. This discussion provides ways of testing software to improve software and system reliability, and it proposes a “reliability footprint” in-process metric to monitor the effectiveness of a test program. Systematic ways of moving from debugging to assuring reliable network management systems are emphasized.