References of "Gurbani, Vijay K"
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See detailQuery-able Kafka: An agile data analytics pipeline for mobile wireless networks
Falk, Eric UL; Gurbani, Vijay K.; State, Radu UL

in Proceedings of the 43rd International Conference on Very Large Data Bases 2017 (2017, August), 10

Due to their promise of delivering real-time network insights, today's streaming analytics platforms are increasingly being used in the communications networks where the impact of the insights go beyond ... [more ▼]

Due to their promise of delivering real-time network insights, today's streaming analytics platforms are increasingly being used in the communications networks where the impact of the insights go beyond sentiment and trend analysis to include real-time detection of security attacks and prediction of network state (i.e., is the network transitioning towards an outage). Current streaming analytics platforms operate under the assumption that arriving traffic is to the order of kilobytes produced at very high frequencies. However, communications networks, especially the telecommunication networks, challenge this assumption because some of the arriving traffic in these networks is to the order of gigabytes, but produced at medium to low velocities. Furthermore, these large datasets may need to be ingested in their entirety to render network insights in real-time. Our interest is to subject today's streaming analytics platforms --- constructed from state-of-the art software components (Kafka, Spark, HDFS, ElasticSearch) --- to traffic densities observed in such communications networks. We find that filtering on such large datasets is best done in a common upstream point instead of being pushed to, and repeated, in downstream components. To demonstrate the advantages of such an approach, we modify Apache Kafka to perform limited \emph{native} data transformation and filtering, relieving the downstream Spark application from doing this. Our approach outperforms four prevalent analytics pipeline architectures with negligible overhead compared to standard Kafka. [less ▲]

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See detailOn non-parametric models for detecting outages in the mobile network
Falk, Eric UL; Camino, Ramiro Daniel UL; State, Radu UL et al

in Integrated Network and Service Management 2017 (2017, May)

The wireless/cellular communications network is composed of a complex set of interconnected computation units that form the mobile core network. The mobile core network is engineered to be fault tolerant ... [more ▼]

The wireless/cellular communications network is composed of a complex set of interconnected computation units that form the mobile core network. The mobile core network is engineered to be fault tolerant and redundant; small errors that manifest themselves in the network are usually resolved automatically. However, some errors remain latent, and if discovered early enough can provide warnings to the network operator about a pending service outage. For mobile network operators, it is of high interest to detect these minor anomalies near real-time. In this work we use performance data from a 4G-LTE network carrier to train two parameter-free models. A first model relies on isolation forests, and the second is histogram based. The trained models represent the data characteristics for normal periods; new data is matched against the trained models to classify the new time period as being normal or abnormal. We show that the proposed methods can gauge the mobile network state with more subtlety than standard success/failure thresholds used in real-world networks today. [less ▲]

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See detailDetecting and predicting outages in mobile networks with log data.
Gurbani, Vijay K.; Kushnir, Dan; Mendiratta, Veena B. et al

in IEEE International Conference on Communications, ICC 2017 (2017, May)

Modern cellular networks are complex systems offering a wide range of services and present challenges in detecting anomalous events when they do occur. The networks are engineered for high reliability and ... [more ▼]

Modern cellular networks are complex systems offering a wide range of services and present challenges in detecting anomalous events when they do occur. The networks are engineered for high reliability and, hence, the data from these networks is predominantly normal with a small proportion being anomalous. From an operations perspective, it is important to detect these anomalies in a timely manner, to correct vulnerabilities in the network and preclude the occurrence of major failure events. The objective of our work is anomaly detection in cellular networks in near real-time to improve network performance and reliability. We use performance data from a 4G LTE network to develop a methodology for anomaly detection in such networks. Two rigorous prediction models are proposed: a non-parametric approach (Chi-Square test), and a parametric one (Gaussian Mixture Models). These models are trained to detect differences between distributions to classify a target distribution as belonging to a normal period or abnormal period with high accuracy. We discuss the merits between the approaches and show that both provide a more nuanced view of the network than simple thresh- olds of success/failure used by operators in production networks today. [less ▲]

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