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IBM Think Milano

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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.

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.

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IBM Think Milano

  1. 1. Danilo Ardagna Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingengeria danilo.ardagna@polimi.it Big Data @ PoliMi
  2. 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. 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. 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. 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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