Trusted Big Data Sharing;
Researching alliances and infrastructure models across
multiple autonomous organizations.
Leon Gommans, Ameneh Deljoo, Ralph Koning, Ben de Graaff,
Tristan Suerink, Gerben van Malenstein, Axel Berg, Erik
Huizer, Rob Meijer, Tom van Engers, Cees de Laat.
This effort researches the concerns many organizations
have that prevents them from sharing their Big Data Assets
considering the associated risks. We show how some of
these concerns can be addressed by creating an alliance
organizing and maintaining trust amongst members of a
group that see a particular common benefit. We also
consider a number of Big Data Sharing infrastructure
models, implementing alliance rules using a common digital
marketplace to administer and enforce them.
See: ABM Animation.
Planet Analytics Over 100 Gbps WANs (also in booth 2611)
Gauravdeep Shami, Olivier Simard, Marc Lyonnais, Rodney
Supported by Ciena research labs, iCAIR at Northwestern
University, the Electronic Visualization Laboratory at the
University of Illinois at Chicago, this demo will showcase
the advanced measurement and analytics capabilities that
could be enabled by Ciena’s Blue Planet Analytics.
Autonomous Response Networks (SARNET).
Ralph Koning, Ben de Graaff, Paola Grosso, Robert Meijer,
Cees de Laat.
This interactive demo will showcase Ciena’s joint research
efforts with the Dutch Research Foundation, University of
Amsterdam and other partners that explore how Software
Defined Networking (SDN) and Network Function Virtualization
(NFV) can help alleviate cyber-attacks and program networks
to provide enhanced cyber-terror detection and defense.
Attendees can see how the network is able to identify the
attack origin, the type of attack and use advanced software
capabilities to respond and defend autonomously.
SARNET, Secure Autonomous Response NETworks, is a project
funded by the Dutch Research Foundation . The University
of Amsterdam, TNO, KLM, and Ciena conduct research on
automated methods against attacks on computer network
infrastructures. By using the latest techniques in Software
Defined Networking and Network Function Virtualization, a
SARNET can use
advanced methods to defend against cyber-attacks and return
the network to its normal state. The research goal of SARNET
is to obtain the knowledge to create ICT systems that: 1)
model the system’s state based on the emerging behavior of
its components; 2) discover by observations and reasoning if
and how an attack is developing and calculate the associated
risks; 3) have the knowledge to calculate the effect of
countermeasures on states and their risks; 4) choose and
execute the most effective countermeasure.
Similar to the SC15 demonstration,, we showed an
interactive touch table based demonstration that controls a
Software Defined Network running on the ExoGENI
In the SC16 demo  the visitor selects the attack type and
its origin, the system will respond and defend against this
attack autonomously. The response includes the use of
security VNF's that are deployed using ExoGeni
infrastructure when required for analysis or mitigation, the
underlying Software Defined Network routes the attack
traffic to the VNF for analysis or mitigation. The demo
showed how Network Function Virtualization and Software
Defined Networks can be useful in attack mitigation and how
they can be used effectively in setting up autonomous
responses to higher layer attacks.
 SARNET project page: http://www.delaat.net/sarnet
 SARNET demonstration at SC15 – http://sc.delaat.net/sc15/SARNET.html
 R. Koning et al., “Interactive analysis of sdn-driven
defence against distributed denial of service attacks,” in
“2016 IEEE NetSoft Conference and Workshops (NetSoft),”
(IEEE, 2016), pp. 483–488.
 SARNET demonstration at SC16 – http://sc.delaat.net/
See: demo video.
See: demo StarNet version.
transformations on a 3D mesh.
Casper van Leeuwen, Hans Trompert, Ronald van der Pol,
Migiel de Vos, Gerben van Malenstein.
During this demonstration we will show real-time
transformations on a 3D mesh, initiated from the depth data
of a Kinect sensor pointed towards the user in the SURF
booth, in VR. In our setup the data from this sensor may
flow through zero or more virtual Network Functions (NFV)
and the user will experience a real-time texture
transformation when applying various effects. Network
functions are controlled by a LeapMotion sensor the user is
wearing. Under the hood we’re using the Network Service
Header to pass traffic through the Network Functions.
See: demo video.
Bring your own container.
Lukasz Makowski, Daniel Cabaca Romao, Cees de Laat, Paola
Our research on container-based remote data processing
investigates the applicability of container technologies for
sharing of (scientific) data. We focus in particular on the
analysis of the challenges and requirements posed to the
overlay networks interconnecting the containers.
Scientific datasets are usually made publicly available,
however, the data cannot always leave the organization
premises. Moreover, on-site data processing can be
challenging because of incompatibility of systems, lack of
manpower or the volume of the dataset itself.
We develop a proof-of-concept employing containers
performing data retrieval and computation networked with
VXLAN overlay. The user is given the ability to create
containers equipped with the chosen set of functions. Where
each function is capable of returning a different subset of
information. Next, the copies of the container are
concurrently executed at the different locations holding
diverse datasets. The output of such execution is the data
found by a particular function. Finally, the multiple
results are correlated and returned to the user.
Our SC16 demo is a gamification of the remote dataset
processing architecture. The selection of container
functions is constrained by the budget i.e. each function
costs a certain amount of money. Additionally, the ability
to run the created container at a selected location also
requires a fee. The user picks different search functions,
represented as tools, to find animals in the remote
datasets. Lastly, correlating found animals according to the
correlation method of choice.
See: demo video.