Lab sessions
Lab Session 1: Map-Reduce and Hadoop
Jérôme François, INRIA Grand Est Nancy, France
Date: Tuesday June 23 2015, 14:00 - 17:00
Location: Oude Infirmerie (2nd floor)
Abstract This tutorial introduces Hadoop and how it can be applied to different
challenges today’s community is facing in network management. Data analytics
is, thus, the focus of this tutorial as networks are producing tons of various
logs, for example network traffic measures, firewall alerts, or SNMP messages.
They form the basis of many management functions, which may necessitate basic
processing like accounting or more complex calculations in particular for providing
predictions on the future for (a) configuration purposes, (b) detecting security
anomalies, or (c) supporting fault management.
This lab session introduces the Map-Reduce paradigm before explaining how to
implement a program for Hadoop. Common programming patterns (join, filter,
aggregation) are presented using short examples. Usual problems are discussed
also, for example sorting or optimizing and chaining multiple tasks. Finally, the
lab session presents Hadoop extensions like Pig for writing requests without any
programming needs.
Lab Session 2: Deploying Network Function Virtualization Experiments on the Virtual Wall Test-bed
Niels Bouten, Ghent University, iMinds, Belgium Rashid Mijumbi, Universitat Politècnica de Catalunya, Spain
Date: Wednesday June 24 2015, 13:30 - 16:30
Location: Oude Infirmerie (2nd floor)
Abstract Network Function Virtualization (NFV) takes advantage of IT virtualization
technologies and network programming to virtualize physical network
functions (e.g., firewall, NAT, and DHCP) and interconnect them to create new
communication services. This allows service providers to create new communication
services on top of existing network and datacenter infrastructure enabling
shorter time-to-market at lower cost. Combining IT virtualization and Softwaredefined
Networking (SDN) technologies allows NFV to increase greatly the network
management flexibility by decoupling network functions from physical machines
and by decoupling the control plane from traffic forwarding in network
equipment.
The goal of this hands-on tutorial is to familiarize all participants with the concept
of NFV in general and possible benefits of combining it with SDN. This will
be accomplished by deploying several network functions on the Virtual Wall and
interconnecting them using OpenFlow. This allows for the creation of individual
Service Function Chains (SFC) for different users.
These experiments will be run in a live network setting, facilitated by the Virtual
Wall test-bed. The Virtual Wall is a test-bed facility for setting up large-scale network
topologies. Its nodes can be assigned different functionality and organized
in arbitrary network topologies on the fly. As such, it is a generic experimental
environment for advanced network, distributed software and service evaluation,
and supports scalability research. The facility has been made available to the research
community through different FP7 FIRE projects. This tutorial will provide,
too, a brief theoretical introduction about the Virtual Wall’s capabilities in
preparation of the hands-on part. By using the jFed framework for test-bed federation,
experiments on the Virtual Wall will be set-up.
The accounts used in the tutorial can be accessed through http://users.ugent.be/~nbouten/aimsaccounts.
Lab Session 3: Powering Monitoring Analytics with ELK Stack
Abdelkader Lahmadi, University of Lorraine, France Frederick Beck, INRIA Nancy, France
Date: Thursday June 25 2015, 09:30 - 12:30
Location: Oude Infirmerie (2nd floor)
Abstract Machine-generated data, including logs and network flows, are considerably
growing and their collection, searching, and visualization is a challenging
task for (a) daily administrator activities and (b) researchers aiming to better
find out analytics and insights from monitoring data regarding their research
goals, including amongst others security or modeling of network and systems.
This lab session introduces the open source ELK stack and its components, including
Elasticsearch for deep search and data analytics, Logstash for centralized
logging, log enrichment, and parsing, and Kibana for powerful and beautiful data
visualizations. ELK enables the analysis and visualization of monitoring data,
such as logs and netflows. A first step details these individual components and
the second step provides guidelines for their deployment and configuration. In the
third step participants will perform hands-on practical work for collecting, processing,
and enriching logs and netflows, combined with the creation of associated
visualization and dashboards aspects.
|