Olivier Terzo presentation

1. KICK OFF MEETING
UR3 ACTIVITIES
Olivier Terzo, Pietro Ruiu
ISMB

2. UR3: OBJECTIVES
• 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

3. GANTT: UR3 TASKS
Deliverable
DEMOGRAPE
Year 1: (Start: 06/2014) Year 2 (End: 05/2016) Partners Involved
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
2014 2015 2016
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
DURATION
PERIOD
MANAGEMENT
USE CASES DEFINITION
RECEIVERS INSTALLATION
PROTOTYPE DELIVERY
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

4. DEMOGRAPE: CLOUD GENERAL CONCEPT

5. 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
IT capacities
UNDER CAPACITIES
OVER CAPACITIES
NON cloud
ON cloud
Resources available
Real
needs
Time
ESTIMATED NEEDS
27/10/2014 ISMB – Copyright 2013 5

6. 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
27/10/2014 ISMB – Copyright 2013 6

7. DEMOGRAPE: CLOUD MOTIVATIONS
Reduce IT costs for HW
infrastructure
servers usage optimization
«Pay per Use»
«On demand resources»
On E-Science, demand of computational
resources and storage are increasing
constantly
Dynamic and flexible use of hardware capacities
Reduce the risk of
fragmentation, isolation from
existing infrastructure
Full compatibility and flexibility
on using existing algorithms /
applications independently of
the SW
Collaborative Infrastructures,
Horizontal and vertical scalability, computational infrastructure platforms

8. CLOUD TECHNOLOGICAL LAYERS
Virtualization
Infrastructure
Hybrid Cloud:
Private and public
Cloud
Cloud services
Open Source Plaforms

9. Dataset
International Cloud Research Infrastructure
Resources
Sensors Upload
Upload/Download
WEB PLATFORM
Sharing data
Sharing resources
Deploy applications
INGV: Istituto Nazionale
di Geofisica e
Vulcanologia
SANSA:
South Africa National
Space Agency
CRAAM-INPE: Centro De
Radio Astronomia E
Astrofisica
MACKENZIE
Brazil
South Africa
Italy

10. DEMOGRAPE: ARCHITECTURE COMPONENTS
Resources
orchestration
User & Admin
console
Management
Application
orchestration
C
O
M
P
A
T
I
B
L
E
A
P
I
Applications
CLOUD MANAGEMENT
CPU RAM Network Storage
Virtualization
Users (SaaS)
Cloud
Platform
Resources
Virtualization
Resources and
storage (IaaS)
services
Cloud Services
(PaaS)
Public
Cloud
Providers

11. DAAS: DATA AS A SERVICES
Data as a Services (DaaS) is an emerging
service on cloud for large users communities
Constraints:
1. Moving data (time transfers, network link limitations)
2. Datasets are growing constantly
3. Data Management
Proposed approach:
1. Decoupling resources sharing and data processing
2. Federation of infrastructures
3. Moving applications NON data

12. Decoupling data Location and data Processing
• Scientifics disciplines are growing
• Communities are growing
• Large scale experiments
Need new paradigms for facilitate
co-operation , co-ordination
• Moving from datasets/resources isolation to
datasets/resources share services model for:
• Data location services
• Data sharing services
• Processing services
ISMB – Copyright 2013 12

13. DaaS
Services
discovery
DAAS SERVICES: CONCEPT
2
2
Finding Dataset
Datasets catalog
3 Dataset location
4 Sending application
Metadata declaration
Dataset
Data format
Data delivery
Data quality
Data availibility
1
Datasets
declaration
…
Cloud Infrastructure

14. UR 3: OPEN POINTS
Applications:
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

15. ISMB CONTACTS
Pietro Ruiu ruiu@ismb.it
Istituto Superiore Mario Boella
Via Pier Carlo Boggio, 61
10138 Torino, Italy
T. +39 011 2276903
MP. +39 366 693 7444
Oliver Terzo terzo@ismb.it
Istituto Superiore Mario Boella
Via Pier Carlo Boggio, 61
10138 Torino, Italy
T. +39 011 2276855
MP. +39 331 670 6418
QUESTIONS?