关键任务呼叫处理中的异常检测
Anomaly Detection Within Mission-Critical Call Processing
AI总结：
本文提出一种基于机器学习的新方法，通过服务器端关键性能指标检测关键任务系统中客户端性能异常，实验证明Random Forest模型在未训练场景下表现最佳，F1分数达0.980。
摘要：
随着电信网络日益庞大和复杂，对改进监控和可靠性的需求日益增加。当处理需要稳定运行以满足精确设计和客户要求，同时保持高可用性的关键任务系统时，这些要求会进一步提高。本文提出了一种新颖的方法，用于开发机器学习模型，通过异常检测来协助维持关键任务系统中客户端-服务器通信的可用性。为此，我们验证了基于根据客户端性能分类的数据来训练模型的方法。所提出的方法评估了使用机器学习对加载了模拟网络流量（使用SIPp）和媒体呼叫的单个虚拟化服务器执行异常检测的情况。为确定训练后的模型能否仅使用服务器上可用的关键性能指标检测到异常的客户端性能，所收集的模型数据根据客户端经历的往返时间性能进行了分类。我们通过测试不同的已训练和未训练测试压力场景，比较了七种不同机器学习模型的性能。在比较中，有五个模型在已训练测试场景中的F1分数达到了0.99以上。Random Forest是唯一能在所有未训练测试场景中F1分数达到0.9以上的模型，其最低分为0.980。结果表明，生成准确的异常检测来评估客户端性能下降是可能的。
英文摘要：
With increasingly larger and more complex telecommunication networks, there is a need for improved monitoring and reliability. Requirements increase further when working with mission-critical systems requiring stable operations to meet precise design and client requirements while maintaining high availability. This paper proposes a novel methodology for developing a machine learning model that can assist in maintaining availability (through anomaly detection) for client-server communications in mission-critical systems. To that end, we validate our methodology for training models based on data classified according to client performance. The proposed methodology evaluates the use of machine learning to perform anomaly detection of a single virtualized server loaded with simulated network traffic (using SIPp) with media calls. The collected data for the models are classified based on the round trip time performance experienced on the client side to determine if the trained models can detect anomalous client side performance only using key performance indicators available on the server. We compared the performance of seven different machine learning models by testing different trained and untrained test stressor scenarios. In the comparison, five models achieved an F1-score above 0.99 for the trained test scenarios. Random Forest was the only model able to attain an F1-score above 0.9 for all untrained test scenarios with the lowest being 0.980. The results suggest that it is possible to generate accurate anomaly detection to evaluate degraded client-side performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
