Dear Colleagues,

Network-monitoring systems have proven to be a fundamental tool for helping network managers in their task of tracking network operation. Network-monitoring systems are useful to detect, and eventually solve, issues with slow or failing components, as they provide managers with both dashboards (with multiple time series of relevant metrics) and rich sets of measurements useful for determining the root causes of any incident. In addition, monitoring systems usually provide managers with mechanisms and algorithms to automatically identify abnormal behaviors and traffic anomalies in time series, flows or packet payloads, which trigger alarms in both active and proactive ways.

However, diverse factors are making the task of network monitoring harder than ever: the heterogeneity of the services and infrastructure of the Internet; the ever-increasing demand for both bandwidth and low latency from users; the advent of new paradigms, such as the Internet of Things, which calls for the -deployment of network probes around the world; the externalization of management tasks; and the balance between the costs and capacity of probes are some of the most significant challenges today.

This scenario opens up the opportunity for the next generation of monitoring systems that combine the most efficient capture modules, the application of both new data aggregation mechanisms and novel analysis approaches (especially those based on cutting-edge approaches such as machine learning), the exploitation of concepts such as software-defined networking and network-function virtualization, the support of cloud infrastructure to provide real monitoring as a service in public/private clouds, and the development of  detailed and useful dashboards without ignoring the costs of the probes and other hardware expenses.

To this end, this Special Issue is soliciting conceptual, theoretical, and experimental contributions to addressing a set of current challenges in the area of systems for network monitoring. The topics of interest include but are not limited to:

• Novel visualization approaches and dashboards for networks metrics.
• Improved capture and storage mechanisms for network measurements.
• Techniques and algorithms for the automatic analysis of network measurements.
• Algorithms and novel approaches to identify the root causes of network anomalies.
• Data reduction for forensic analysis.
• Use of software defined networking (SDN) and network function.
• Virtualization (NFV) concepts for the development of monitoring systems.
• The exploitation of machine learning techniques, such as neural networks, to improve monitoring systems.
• The hardware/software optimization of monitoring systems for high-speed networks (>10 Gb/s).
• The hardware/software optimization of monitoring systems for low-cost probes (<USD 1000).
• Hybrid monitoring systems in public/private clouds.
• Monitoring as a service in the cloud.
• The monitoring of traffic at the application level, and the classification and identification of classes of traffic.
• Experiences, deployments and testing of network-monitoring systems.

Guest Editor

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