SeLINA: A Self-Learning Insightful Network Analyzer

Apiletti, Daniele; Baralis, Elena; Cerquitelli, Tania; Garza, Paolo; Giordano, Danilo; Mellia, Marco; Venturini, Luca

doi:10.1109/tnsm.2016.2597443

Cited by 23 publications

(19 citation statements)

References 39 publications

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“…Deep packet inspection (DPI), relies on the availability of a training set, cannot provide a real-time identification for encrypted data traffic, and needs an expensive retraining phase. Big data analysis extracts information from raw data, but it often requires ML algorithms [72]. To resolve these issues, self-tuning, simple tools are proposed to extract knowledge from network traffic, including different data analytics techniques.…”

Section: ) Network Traffic Classification (Tc)mentioning

confidence: 99%

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

et al. 2019

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The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualization, activation, and customization. Software-defined networking (SDN) technology breaks the tight coupling between the control plane and the data plane in the traditional network architecture, making the controllability, security, and economy of network resources into a reality. As one of the important actualization methods of artificial intelligence (AI), machine learning (ML), combined with SDN architecture will have great potential in areas, such as network resource management, route planning, traffic scheduling, fault diagnosis, and network security. This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications. From the perspective of ML algorithms, this paper focuses on the applications of classical ML algorithms in SDN-based networks, after a characteristic analysis of algorithms. From the other perspective, after classifying the existing network applications based on the SDN architecture, the related ML solutions are introduced. Finally, the future development of the ML algorithms and SDN concepts is discussed and analyzed. This paper occupies the intersection of the AI, big data, computer networking, and other disciplines; the AI itself is a new and complex interdisciplinary field, which causes the researchers in this field to often have different professional backgrounds and, sometimes, divergent research purposes. This paper is necessary and helpful for researchers from different fields to accurately master the key issues.INDEX TERMS Artificial intelligence, machine learning, network management, software-defined networking.

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Section: ) Network Traffic Classification (Tc)mentioning

confidence: 99%

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

et al. 2019

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“…However, they also tackle the problem when no labels are present, by exploiting powerful exploratory techniques such as rule mining. Similar combinations of such techniques have also been successfully applied in other domains, e.g., for network data characterization [20,21].…”

Section: General-purpose and Data-driven Solutionsmentioning

confidence: 99%

Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine

et al. 2019

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Predicting power demand of building heating systems is a challenging task due to the high variability of their energy profiles. Power demand is characterized by different heating cycles including sequences of various transient and steady-state phases. To effectively perform the predictive task by exploiting the huge amount of fine-grained energy-related data collected through Internet of Things (IoT) devices, innovative and scalable solutions should be devised. This paper presents PHi-CiB, a scalable full-stack distributed engine, addressing all tasks from energy-related data collection, to their integration, storage, analysis, and modeling. Heterogeneous data measurements (e.g., power consumption in buildings, meteorological conditions) are collected through multiple hardware (e.g., IoT devices) and software (e.g., web services) entities. Such data are integrated and analyzed to predict the average power demand of each building for different time horizons. First, the transient and steady-state phases characterizing the heating cycle of each building are automatically identified; then the power-level forecasting is performed for each phase. To this aim, PHi-CiB relies on a pipeline of three algorithms: the Exponentially Weighted Moving Average, the Multivariate Adaptive Regression Spline, and the Linear Regression with Stochastic Gradient Descent. PHi-CiB’s current implementation exploits Apache Spark and MongoDB and supports parallel and scalable processing and analytical tasks. Experimental results, performed on energy-related data collected in a real-world system show the effectiveness of PHi-CiB in predicting heating power consumption of buildings with a limited prediction error and an optimal horizontal scalability.

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“…All the current implementations are based on Hadoop MapReduce and has been exploited to support different data warehousing applications [6,7]. MLlib [8], instead, is the Machine Learning library developed on Spark, and it is rapidly growing both in development and adoption (e.g., network traffic analysis [9], social networks [10]).…”

Section: Distributed Frameworkmentioning

confidence: 99%

Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings

2019

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The pervasive and increasing deployment of smart meters allows collecting a huge amount of fine-grained energy data in different urban scenarios. The analysis of such data is challenging and opening up a variety of interesting and new research issues across energy and computer science research areas. The key role of computer scientists is providing energy researchers and practitioners with cutting-edge and scalable analytics engines to effectively support their daily research activities, hence fostering and leveraging data-driven approaches. This paper presents SPEC, a scalable and distributed engine to predict building-specific power consumption. SPEC addresses the full analytic stack and exploits a data stream approach over sliding time windows to train a prediction model tailored to each building. The model allows us to predict the upcoming power consumption at a time instant in the near future. SPEC integrates different machine learning approaches, specifically ridge regression, artificial neural networks, and random forest regression, to predict fine-grained values of power consumption, and a classification model, the random forest classifier, to forecast a coarse consumption level. SPEC exploits state-of-the-art distributed computing frameworks to address the big data challenges in harvesting energy data: the current implementation runs on Apache Spark, the most widespread high-performance data-processing platform, and can natively scale to huge datasets. As a case study, SPEC has been tested on real data of an heating distribution network and power consumption data collected in a major Italian city. Experimental results demonstrate the effectiveness of SPEC to forecast both fine-grained values and coarse levels of power consumption of buildings.

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SeLINA: A Self-Learning Insightful Network Analyzer

Cited by 23 publications

References 39 publications

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine

Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings

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