Telecommunications carriers have investigated automating network operations such as failure recovery over the years. When a failure occurs, a large number of alarms (ALMs) are generated from multiple sets of equipment. However, the issuing of ALMs and the number of ALMs change dynamically depending on the situation. Therefore, in order to identify a suspected failure, the scope of the investigation must be specified based on the network topology and the combination of ALMs as a preliminary step. The workflow of such network operations is difficult to define, and efficiently selecting the range to investigate the failure is necessary for rapid failure recovery. However, automating such processes is difficult because in existing search algorithms defining in advance the solution that will terminate a search or conditions under which the search will be completed is impossible. In this paper, we propose a network node search algorithm that uses a graph neural network (GNN) to determine repeatedly the necessity for investigating neighboring equipment to determine the scope of the failure investigation in the network.

Network and maintenance operations (NWOPs) are being automated. In recent years, there have been attempts to develop artificial intelligence (AI) using machine learning models such as neural networks in order to automate more sophisticated decisions among NWOPs. Equipment alarms (ALMs) are commonly used in many NWOP decisions. To handle ALMs as AI input requires vectorization as other AI inputs, though vectorization employing general one-hot encoding cause generalization performance problem. Its general performance tends to be low despite the large amount of computation. This paper proposes new vectorization method(ALM2Vec) that reflects ALM data and their relational information onto the network topology. ALM2Vec method is able to reduce the amount of calculation, and learning with a high level of generalization performance can be achieved.

In the course of network operations, telecommunications carriers must have accurate topology information of the network related to the failure to identify quickly causes of service failures and determine their impacts. However, telecommunications carrier networks are divided into multiple layers according to their roles, and each layer has a different management system, making it difficult to detect the topology information between different layers. Therefore, there is a need for a technology that can assure accurate topology information of the multiple layers network. We propose a new topology discovery method based on the consistency of the traffic of mutually connected interfaces. We verify the effectiveness of the proposed method using traffic data from network equipment in a commercial network and show that the proposed method is capable of detecting topologies with high accuracy.

Deploying artificial intelligence (AI) to network operations have long been an issue. AI has been expected to help automating network operations, especially in those requiring human decisions, such as handling failures which involve complex decisions. Since current AIs do not have enough parameters for individual failures cases, their accuracy is not enough to fully rely on their decisions. Thus, handing unusual fault cases are still dominated by skilled operators. This paper proposes an extended Business Process Model and Notation (BPMN) which uses Bayesian networks to represent operator's decisions, to connect AIs and workflow engines (WFE) which automates handing atypical failures.

As for the service assurance operation of telecommunications carriers, accurate information about a network topology which indicates the connection relationships between pieces of network equipment is necessary. However, the network of a telecommunications carrier has several hundreds of thousands of equipment, and its topology is frequently supplemented and modified due to daily construction work and troubleshooting. It is a therefore a problem when incorrect topology information is mixed into the overall topology information. In this paper, we propose a method that can discover the topology between equipment by using alarm information issued by those equipment during construction work or when a failure occurs. The proposed method was evaluated using alarm information generated under in certain commercial configurations (sections containing specific routers), and it was confirmed that the current topology could be discovered with 100% accuracy, even though only 1.7% of the total topology was evaluated in one day.

For the service assurance operation of telecommunications carriers, accurate information about a network topology that indicates the connection relationships between pieces of network equipment is necessary. However, the network of a telecommunications carrier has several hundreds of thousands of pieces of equipment. Furthermore, its topology is frequently supplemented and modified due to daily construction work and troubleshooting. As a result, this causes incorrect topology information to be mixed into the overall topology information. In this paper, we propose a method that can discover the topology between equipment by using alarm information issued by those equipment during construction work or when a failure occurs. The proposed method was evaluated using alarm information generated on a commercial network. The experimental results show that the proposed method discovers topology with higher accuracy than classical topology discovery approaches.