dgterzo
TRANSCRIPT
• 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
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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 [email protected]
Istituto Superiore Mario BoellaVia Pier Carlo Boggio, 61
10138 Torino, Italy
T. +39 011 2276855
MP. +39 331 670 6418
ISMB CONTACTSPietro Ruiu [email protected]
Istituto Superiore Mario BoellaVia Pier Carlo Boggio, 61
10138 Torino, Italy
T. +39 011 2276903
MP. +39 366 693 7444
QUESTIONS?