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IBM Think Milano

ATMOSPHERE .
ATMOSPHERE .
ATMOSPHERE .Adaptive, Trustworthy, Manageable, Orchestrated,Secure, Privacy-assuring, Hybrid Ecosystem for REsilient Cloud Computing

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.

IBM Think Milano

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

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IBM Think Milano

  • 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