ATMOSPHERE was invited to be a speaker at Think Milano event, on 6th June from 14.30 to 17.30, to join a panel discussion called “L’infrastruttura cloud ready protagonista del future” on how cloud infrastructures are important for different market sectors.

1. Danilo Ardagna
Politecnico di Milano
Dipartimento di Elettronica Informazione e Bioingengeria
danilo.ardagna@polimi.it
Big Data @ PoliMi

2. Collaborative Innovation Center 2
In-memory DBP7 Cluster
Management Node
Internal hi-speed drives
Data Node 1 Data Node 2
Stg hadoop data Stg hadoop data
Internal hi-speed drives
Data Node 3 Data Node 4
HPC Node 1
Internal hi-speed drives
HPC Node 3
Internal hi-speed drives
HPC Node 2
Internal hi-speed drives
P8 Cluster based on HDP
with BLU
Acceleration
Stg hadoop data Stg hadoop data
Internal hi-speed drives
Grow cultural awareness, education and innovation on Analytics
Support the usage of Analytics in businesses, both start-up’s and corporate clients
Foster the establishment of new Analytics-related jobs

3. Ongoing research on Big Data
The DICE project (February 2015 - January 2018) is funded
by the European Union’s Horizon 2020 research and
innovation program under grant agreement No. 644869.
Want to rapidly enter
Cloud and Big Data
markets? To tackle skill
shortage and steep
learning curves? To
decrease costs to
develop and operate? To
enhance the quality and
reduce incidents?
Design
Simulation
OptimizationDeployment
Testing
DICE IDE
The focus of the DICE project is to define a quality-driven framework for
developing data-intensive applications that leverage Big Data
technologies hosted in private or public clouds. DICE will offer a novel
UML profile and tools for data-aware quality-driven development. The
methodology will excel for its quality assessment, architecture
enhancement, agile delivery and continuous testing and deployment,
relying on principles from the DevOps paradigm.
Visit the DICE website
www.dice-h2020.eu
Model the data-intensive application (DIA) through the DICE profile and
other complementary standard profiles (e.g. MARTE).
Perform simulation and verification of correctness on designed
models to assess quality characteristics of the DIA during design-
time.
Support iterative development. It will deliver quality issue
detection that will be automatically fed back to the developer via
accessible visualization mechanisms.
Automatic highly configurable TOSCA model building and
application deployment on both private and public Clouds.
Continuous application quality testing, configuration optimization,
scalability assessment and continuous integration.
Athens Technology Center
Flexiant Limited
Imperial College London
Institute e-Austria Timisoara
Netfective Technology
Politecnico di Milano
Prodevelop
Universidad de Zaragoza
XLAB
www.dice-h2020.eu
Project Coordinator:
Dr. Giuliano Casale
Imperial College of Science,
Technology and Medicine (UK)
Email: contact@dice-h2020.eu
At a glance
Solution
Use cases
NewsAsset will demonstrate how
the DIA designer will be able to
efficiently gather data from
heterogeneous nodes, utilize
services to aggregate them to relax
redundancy and compose services
to associate relevant data coming
from different social medias.
eGov Fraud Detection aims to
improve tax fraud detection for
public financial ministers, which
requires the timely processing of
complex requests across highly
duplicated, scattered, even diluted
data.
Maritime Operations aims at
providing a DICE-based geo-fencing
enabler with geo-located complex
event processing and streaming
capabilities for the maritime sector
using location awareness
technologies.
Developing Data-Intensive Cloud
Applications with Iterative Quality
Enhancements
3
The problem:
o Minimize costs and suggests the
optimal deployment architecture
which provides QoS guarantees
What does the tool do?
o Automatic analysis of multiple
candidate alternative
configurations
Innovation:
o Design space exploration has been
increasingly sought only in
traditional multi-tier applications
Impact & stakeholders:
o Designers and operators make
more informed decisions about
the technology to use
o Reduce costs of a shared cluster
running multiple DIAs
The DICE project (February 2015 - January 20
by the European Union’s Horizon 2020
innovation program under grant agreement No
Want to rapidly enter
Cloud and Big Data
markets? To tackle skill
shortage and steep
learning curves? To
decrease costs to
develop and operate? To
enhance the quality and
reduce incidents?
Design
Simulation
OptimizationDeployment
Testing
DICE IDE
The focus of the DICE project is to define a
developing data-intensive applications
technologies hosted in private or public c
UML profile and tools for data-aware qua
methodology will excel for its quali
enhancement, agile delivery and continu
relying on principles from the DevOps para
Visit the DICE website
www.dice-h2020.eu
Model the data-intensive application (D
other complementary standard profiles
Perform simulation and verificati
models to assess quality characte
time.
Support iterative development
detection that will be automatica
accessible visualization mechanism
Automatic highly configurable
application deployment on both pr
Continuous application quality testin
scalability assessment and continuous in
Athens Technology Center
Flexiant Limited
Imperial College London
Institute e-Austria Timisoara
Netfective Technology
Politecnico di Milano
Prodevelop
Universidad de Zaragoza
XLAB
www.dice-h2020.eu
P
D
I
T
E
Solution
NewsAsset will demonstrate how
the DIA designer will be able to
efficiently gather data from
heterogeneous nodes, utilize
services to aggregate them to relax
redundancy and compose services
to associate relevant data coming
from different social medias.
eGov Fraud Detection aims to
improve tax fraud detection for
public financial ministers, which
requires the timely processing of
complex requests across highly
duplicated, scattered, even diluted
data.
Ma
pro
en
ev
ca
us
tec
Developing Data-Intensive Cloud
Applications with Iterative Quality
Enhancements

4. Optimization Service
Machine
Learning
Model
Local Search Algorithms
Lundstrom
Performance Prediction
Service
(3)
(5)
Proactive/
Reactive Policies
Adaptation policies
module
Proactive/Reactive Rules
Monitoring
(MONASCA)
(7)
Broker API
(2)
Spark
YARN
(9)
Configuration & Contextualisation
Service
Application
Submission
(1)
(4)
OPT_JR OPT_IC Mesos
dagSim
DB
(6)
(8)
(2)
4
The problem:
o Identify minimum cost Spark
deployment to fulfil a deadline
o Re-balance cluster capacity in case
of heavy load
What does the tool do?
o Runtime analysis of multiple
candidate deployments
Innovation:
o No tools available to reconfigure a
Spark cluster @ runtime with
performance guarantees
Impact & stakeholders:
o System operators released by
resource reconfigurations
o Provide deadline guarantees and
increase cluster utilization
Ongoing research on Big Data

5. 5
The problem:
o Predict training time of deep
learning applications for image
classification by
o using GPU and multi GPU
systems
o relying on Spark clusters
(transfer learning)
What does the tool do?
o Build machine learning models to
regress application training time
o End-to-end and per-layer model
Innovation:
o No open source tool available to
predict network training time from
its structure
Impact & stakeholders:
o Cloud providers: deadline
guarantees and increase cluster
utilization
o End users: reduce image analysis
costs
Ongoing research on Big Data & AI
ATMOSPHERE (777154) is a Research and Innovation Activity funded by the European Commission under the Cooperation Programme, H2020
4ª Chamada Coordenada BR-UE em Tecnologias da Informação e Comunicação (TIC), Secretaria de Políticas de Informática (Sepin)
do Ministério de Ciência, Tecnologia e Inovação (MCTI) e a RNP, Brasil.
Ignacio Blanquer – iblanque@dsic.upv.es - (Universitat Politècnica de València)
Francisco Brasileiro. – fubica@computacao.ufcg.edu.br - (Universidade Federal de Campina Grande)
Use Case
• Trustworthiness of an application is assessed by means of individual separate tests
• A priori and dynamic a posterior evaluation of vulnerability, performance, integrity, robustness,
scalability, resource consumption, fairness, isolation, etc.
• Testing trustworthiness a priori and dynamically in a broader sense, which will enable creating self-
adaptive applications and tracing the degree of compliance of regulations such as the EU-GDPR
• Privacy protection, traceability, confidentiality warning, etc.
• Applications that cannot reach a defined level of certification can be still trustful for specific
operations with lower risks, such as pure anonymised data, best effort computations and long-
term research.
Interaction among the activities
ATMOSPHERE specific objectives
• ATMOSPHERE aims at the design and development of a framework and a platform
to implement trustworthy cloud services on top of an intercontinental hybrid and
federated resource pool.
• Considering a broad spectrum of trustworthiness properties and measures
• Security, Privacy, Coherence, Isolation, Stability, Fairness, Transparency & Dependability.
• Supporting the development, build, deployment, measurement and evolution of
trustworthy cloud resources, data management and processing services.
• ATMOSPHERE will provide:
• A definition of Trustworthiness Metrics.
• A platform to measure such metrics.
• A hybrid and federated container-based infrastructure.
• Performance modelling services for the applications.
• Trustworthy Data Management and Processing services.
• Automatic Deployment and Configuration of Complex
Infrastructures from standard and descriptive software configuration
recipes.
• Deployment of a Hybrid and federated infrastructure supporting LxD
Containers for a fast provisioning of resources and Virtual Machines.
• A set of metrics, rules, tests and procedures to evaluate mostly
automatically the trustworthiness dimensions.
• A Distributed Data Management Platforms which supports such
services as storage, retrieval, update and access of data in a cloud,
guaranteeing features such as confidentiality and revocation
associated to an AAA system.
• A Distributed Processing Service addressing real-time and
distributed analytics privacy and traceability and performance
optimization.
www.atmosphere-eubrazil.eu
Trustworthiness Assessment & Monitoring Framework
Cloud services managing federated and hybrid
resources
DistributedTrustworthy Data Management
Services
Trustworthy Data Processing Services
Pilot & Use Cases
CommunityEngagement,Communication&Impact
ExploitationandSustainability
atmosphere-eubrazil.eu @AtmosphereEUBR
Improving Trustworthiness of Data Analytics
blanque@dsic.upv.es - (Universitat Politècnica de València)
– fubica@computacao.ufcg.edu.br - (Universidade Federal de Campina Grande)
rm
nd
ity.
of
Automatic Deployment and Configuration of Complex
Infrastructures from standard and descriptive software configuration
recipes.
Deployment of a Hybrid and federated infrastructure supporting LxD
Containers for a fast provisioning of resources and Virtual Machines.
A set of metrics, rules, tests and procedures to evaluate mostly
automatically the trustworthiness dimensions.
A Distributed Data Management Platforms which supports such
services as storage, retrieval, update and access of data in a cloud,
guaranteeing features such as confidentiality and revocation
associated to an AAA system.
ness of Data Analytics
ATMOSPHERE (777154) is a Research and Innovation Activity funded by the European Commission under the Cooperation Programme, H2020
4ª Chamada Coordenada BR-UE em Tecnologias da Informação e Comunicação (TIC), Secretaria de Políticas de Informática (Sepin)
do Ministério de Ciência, Tecnologia e Inovação (MCTI) e a RNP, Brasil.
Ignacio Blanquer – iblanque@dsic.upv.es - (Universitat Politècnica de València)
Francisco Brasileiro. – fubica@computacao.ufcg.edu.br - (Universidade Federal de Campina Grande)
Use Case
• Trustworthiness of an application is assessed by means of individual separate tests
• A priori and dynamic a posterior evaluation of vulnerability, performance, integrity, robustness,
scalability, resource consumption, fairness, isolation, etc.
• Testing trustworthiness a priori and dynamically in a broader sense, which will enable creating self-
adaptive applications and tracing the degree of compliance of regulations such as the EU-GDPR
• Privacy protection, traceability, confidentiality warning, etc.
• Applications that cannot reach a defined level of certification can be still trustful for specific
operations with lower risks, such as pure anonymised data, best effort computations and long-
term research.
Interaction among the activities
ATMOSPHERE specific objectives
• ATMOSPHERE aims at the design and development of a framework and a platform
to implement trustworthy cloud services on top of an intercontinental hybrid and
federated resource pool.
• Considering a broad spectrum of trustworthiness properties and measures
• Security, Privacy, Coherence, Isolation, Stability, Fairness, Transparency & Dependability.
• Supporting the development, build, deployment, measurement and evolution of
trustworthy cloud resources, data management and processing services.
• ATMOSPHERE will provide:
• A definition of Trustworthiness Metrics.
• A platform to measure such metrics.
• A hybrid and federated container-based infrastructure.
• Performance modelling services for the applications.
• Trustworthy Data Management and Processing services.
• Automatic Deployment and Configuration of Complex
Infrastructures from standard and descriptive software configuration
recipes.
• Deployment of a Hybrid and federated infrastructure supporting LxD
Containers for a fast provisioning of resources and Virtual Machines.
• A set of metrics, rules, tests and procedures to evaluate mostly
automatically the trustworthiness dimensions.
• A Distributed Data Management Platforms which supports such
services as storage, retrieval, update and access of data in a cloud,
guaranteeing features such as confidentiality and revocation
associated to an AAA system.
• A Distributed Processing Service addressing real-time and
distributed analytics privacy and traceability and performance
optimization.
www.atmosphere-eubrazil.eu
Trustworthiness Assessment & Monitoring Framework
Cloud services managing federated and hybrid
resources
DistributedTrustworthy Data Management
Services
Trustworthy Data Processing Services
Pilot & Use Cases
CommunityEngagement,Communication&Impact
ExploitationandSustainability
atmosphere-eubrazil.eu @AtmosphereEUBR
Improving Trustworthiness of Data Analytics