KICK OFF MEETINGUR3 ACTIVITIES

Olivier Terzo, Pietro Ruiu

ISMB

• Cloud computing Infrastructure implementation

• Share data/algorithms and HD resources

• Improve applications/data portability in Cloud

• Data accessibility for different teams, communities

• Computational resources availability for analysis

UR3: OBJECTIVES

GANTT: UR3 TASKSDeliverable

DEMOGRAPE1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 INGV ISMB POLITO CRAAM SANSA

3.0 UR3 (ISMB)

3.1 USERS, RESOURCES, STORAGE REQUIREMENTS X X X X X

3.2 CLOUD INFRASTRUCTURE DESIGN x x x

3.3 DATA STORAGE DESIGN x x x x x

3.4 CLOUD INFRASTRUCTURE IMPLEMENTATION x x x

3.5 DATA STORAGE IMPLEMENTATION x x x

3.6 APPLICATIONS CLOUD INTEGRATION x x x

3.7 CLOUD INFRASTRUCTURE TESTING x x x x x

2014 2015 2016

Partners InvolvedYear 1: (Start: 06/2014) Year 2 (End: 05/2016)

DURATION

PERIOD

MANAGEMENT

USE CASES DEFINITION

RECEIVERS INSTALLATION

PROTOTYPE DELIVERY

DEMOGRAPE: CLOUD GENERAL CONCEPT

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Cloud non Cloud• Automatically add Virtual Nodes, suitably sized (num. of CPU

and RAM) depending on the workload.

• Virtualization enables the optimization of resources and simplifies infrastructure management.

• The real advantage of this model, is the flexibility of the use of the hardware

NON cloud

Resources available

Real needs

IT capacities

Time

OVER CAPACITIES

ON cloudUNDER CAPACITIES

ESTIMATED NEEDS

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CLOUD TERMINOLOGY

Horizontal scalability: • dynamic allocation in upscaling and downscaling of more virtual

machines• dynamic allocation of storage for data management

Vertical scalability:

• capability to change RAM memory and core allocation in a single virtual machine

DEMOGRAPE: CLOUD MOTIVATIONS

Reduce IT costs for HW infrastructureservers usage optimization

«Pay per Use»«On demand resources»On E-Science, demand of computational resources and storage are increasing constantly

Reduce the risk of fragmentation, isolation from existing infrastructure

Full compatibility and flexibility on using existing algorithms / applications independently of the SW

Dynamic and flexible use of

hardware capacitiesCollaborative

Infrastructures, Horizontal and

vertical scalability, computational infrastructure

platforms

CLOUD TECHNOLOGICAL LAYERSVirtualization

Infrastructure

Hybrid Cloud:Private and public Cloud

Cloud services

Open Source Plaforms

Dataset

International Cloud Research Infrastructure

ResourcesSensors Upload

Upload/Download

WEB PLATFORMSharing data

Sharing resourcesDeploy applications

INGV: Istituto Nazionale di Geofisica e Vulcanologia

SANSA: South Africa National Space Agency

CRAAM-INPE: Centro De Radio Astronomia E AstrofisicaMACKENZIE

Brazil

South Africa

Italy

DEMOGRAPE: ARCHITECTURE COMPONENTS

Resources orchestration

User & Admin console

ManagementApplication

orchestrationCOMPATIBLE API

Applications

CLOUD MANAGEMENT

VirtualizationCPU RAM Network Storage

Resources and storage (IaaS)

Resources Virtualization

Cloud Platform

servicesCloud Services

(PaaS)

Users (SaaS)

Public Cloud

Providers

Constraints:

1. Moving data (time transfers, network link limitations)

2. Datasets are growing constantly

3. Data Management

Data as a Services (DaaS) is an emerging service on cloud for large users communities

Proposed approach:

1. Decoupling resources sharing and data processing

2. Federation of infrastructures

3. Moving applications NON data

DAAS: DATA AS A SERVICES

• Scientifics disciplines are growing • Communities are growing • Large scale experiments

• Moving from datasets/resources isolation to datasets/resources share services model for:

• Data location services• Data sharing services• Processing services

Decoupling data Location and data Processing

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Need new paradigms for facilitate co-operation , co-ordination

DaaS

Services discovery

2

2Finding Dataset

3 Dataset location

4 Sending application

DAAS SERVICES: CONCEPT

Datasets catalog

1

Datasets declaration

Data formatData delivery

Data qualityData availibility

Dataset

…

Metadata declaration

Cloud Infrastructure

UR 3: OPEN POINTSApplications:

1. Applications integration on cloud

2. Data acquisition from Antartica to Cloud to be analyzed

3. Application replication over all sites

Data storage:

1. Data estimation grow

2. Data sharing / replication over all sites (centralized/decentralized approach)

Cloud Infrastructure:

1. Cloud model to be applied

2. Resources availibility

3. Implementation timeline

Oliver Terzo terzo@ismb.it

Istituto Superiore Mario BoellaVia Pier Carlo Boggio, 61

10138 Torino, Italy

T. +39 011 2276855

MP. +39 331 670 6418

ISMB CONTACTSPietro Ruiu ruiu@ismb.it

Istituto Superiore Mario BoellaVia Pier Carlo Boggio, 61

10138 Torino, Italy

T. +39 011 2276903

MP. +39 366 693 7444

QUESTIONS?