Cloud acceleration-pro active-solutionslinux-ow2
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Agenda1. Background: INRIA, ActiveEon
2. ProActive OS Toolkit:Programming, Scheduling, Resourcing
3. UC 1: Genomics+Cloud
4. UC 2: Finance
5. UC 3: IT, SOA Web Server
Log Analysis
D. Caromel, et al.
Cloud et Accélération des
Applications Java avec
ProActive Parallel Suite
Parallelism+Distribution+Virtualization
Enterprise Grids & Clouds
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1. Background
1. Background
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OASIS Team & INRIA
A joint team, Now about 35 persons
2004: First ProActive User Group
2009, April: ProActive 4.1, Distributed & Parallel:
From Multi-cores to Enterprise GRIDs
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OASIS Team Composition (35)
Researchers (5): D. Caromel (UNSA, Det. INRIA)
E. Madelaine (INRIA)
F. Baude (UNSA)
F. Huet (UNSA)
L. Henrio (CNRS)
PhDs (11): Antonio Cansado (INRIA, Conicyt)
Brian Amedro (SCS-Agos)
Cristian Ruz (INRIA, Conicyt)
Elton Mathias (INRIA-Cordi)
Imen Filali (SCS-Agos / FP7 SOA4All)
Marcela Rivera (INRIA, Conicyt)
Muhammad Khan (STIC-Asia)
Paul Naoumenko (INRIA/Région PACA)
Viet Dung Doan (FP6 Bionets)
Virginie Contes (SOA4ALL)
Guilherme Pezzi (AGOS, CIFRE SCP)
+ Visitors + Interns
PostDoc (1): Regis Gascon (INRIA)
Engineers (10):
Elaine Isnard (AGOS)
Fabien Viale (ANR OMD2, Renault )
Franca Perrina (AGOS)
Germain Sigety (INRIA)
Yu Feng (ETSI, FP6 EchoGrid)
Bastien Sauvan (ADT Galaxy)
Florin-Alexandru.Bratu (INRIA CPER)
Igor Smirnov (Microsoft)
Fabrice Fontenoy (AGOS)
Open position (Thales)
Trainee (2): Etienne Vallette d’Osia (Master 2 ISI)
Laurent Vanni (Master 2 ISI)
Assistants (2): Patricia Maleyran (INRIA)
Sandra Devauchelle (I3S)Located in Sophia Antipolis, between
Nice and Cannes,
Visitors and Students Welcome!
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Co-developing, Support for ProActive Parallel Suite
Worldwide Customers: Fr, UK, Boston USA
Startup Company Born of INRIA
Some Partners:
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2. ProActive Parallel SuiteDealing with Multi-Cores & Virtualization
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Product: ProActive Parallel Suite
Java Parallel
Toolkit
Multi-Platform
Job Scheduler
Resource
Manager
Strong Differentiation:
Java Parallel Programming + Integration + Portability: Linux, Windows, Mac +
Versatility: Desktops, Cluster, Grid, Clouds = Perfect Flexibility
Used in Production Today:
50 Cores 300 Cores early 2010
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ProActive Parallel Suite
Three fully compatible modules:
Programming Scheduling
Resource Management
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ProActive Programming:
Active Objects
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A
ProActive : Active objects
Proxy
Java Object
A ag = newActive (“A”, […], VirtualNode)
V v1 = ag.foo (param);
V v2 = ag.bar (param);
...
v1.bar(); //Wait-By-Necessity
V
Wait-By-Necessity
is a
Dataflow
Synchronization
JVM
A
JVM
Active Object
Future Object Request
Req. Queue
Thread
v1v2 ag
WBN!
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TYPED
ASYNCHRONOUS GROUPS
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Broadcast and Scatter
JVM
JVM
JVM
JVM
agcg
ag.bar(cg); // broadcast cg
ProActive.setScatterGroup(cg);
ag.bar(cg); // scatter cg
c1 c2c3c1 c2c3
c1 c2c3c1 c2c3
c1 c2c3
c1 c2c3
s
c1 c2c3
s
Broadcast is the default behavior
Use a group as parameter, Scattered depends on rankings
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Dynamic Dispatch Group
JVM
JVM
JVM
JVM
agcg
c1
c2
c3
c4
c5
c6
c7
c8c0
c9c1
c2
c3
c4
c5
c6
c7
c8c0
c9
c1
c2
c3
c4
c5
c6
c7
c8c0
c9
Slowest
Fastest
ag.bar(cg);
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Abstractions
for Parallelism
The right Tool to do the Task right
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ProActive Parallel Suite
Workflows in Java
Master/Workers
SPMD
Components
…
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16Denis Caromel
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GCM Fractal Deployment Standard
Protocols: Rsh, ssh
Oarsh, Gsissh
Scheduler, and Grids: GroupSSH, GroupRSH, GroupOARSH
ARC (NorduGrid), CGSP China Grid, EEGE gLITE,
Fura/InnerGrid (GridSystem Inc.)
GLOBUS, GridBus
IBM Load Leveler, LSF, Microsoft CCS (Windows HPC Server 2008)
Sun Grid Engine, OAR, PBS / Torque, PRUN
Clouds: Amazon EC2
Interoperability:
Cloud will start with existing IT infrastructure,
Build Non Intrusive Cloud with ProActive
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GCM Official Standardization
Grid Component Model
Overall, the standardization is supported by industrials:
BT, FT-Orange, Nokia-Siemens, NEC,Telefonica, Alcatel-Lucent, Huawei …
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18Denis Caromel
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GCM StandardizationFractal Based Grid Component Model
4 Standards:
1. GCM Interoperability Deployment
2. GCM Application Description
3. GCM Fractal ADL
4. GCM Fractal Management API
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IC2D: Optimizing
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2020
IC2D
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IC2D
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ChartIt
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Pies for Analysis and Optimization
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Video 1:
IC2D Optimizing
Monitoring, Debugging, Optimizing
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Scheduling & Resourcing
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ProActive Scheduling
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ProActive Scheduling Big Picture
RESOURCES
Multi-platform Graphical Client (RCP)
File-based or LDAP authentication
Static Workflow Job Scheduling, Native and
Java tasks, Retry on Error, Priority Policy,
Configuration Scripts,…
Dynamic and Static node sources, Resource
Selection by script, Monitoring and Control
GUI,…
ProActive Deployment capabilities:
Desktops, Clusters, Clouds,…
ProActive
Scheduler
ProActive
Resource Manager
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Scheduler: User Interface
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Job
Another Example : Picture Denoising
Split
Denoise DenoiseDenoiseDenoise
Merge
•with selection on native executable availability (ImageMagik, GREYstoration)
• Multi-platform selection and command generation
•with file transfer in pre/post scripts
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ProActive Resourcing
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RESOURCING User Interface
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32Denis Caromel
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Easily manage actual VMs
Most of VMs can be dynamically provisioned: Start
Stop
Clone
Destroy
Supported VMs today: VMware,
KVM
Xen, Xen Server
QMU
Microsoft Hyper-V
Provision Application containers: Manage applications with pre-defined VMs
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Clusters to Grids to Clouds:
e.g. on Amazon EC2
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Node source Usecase :
Configuration for external cloud with EC2
ProActive
Scheduler
ProActive
Resource Manager
Dedicated resources
LSF
Static Policy
Amazon EC2
EC2
Dynamic
Workload Policy
Desktops
Desktops
Timing Policy
12/24
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Video 2:
Scheduler, Resource Manager
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ProActive Parallel Suite
Three fully compatible modules
Programming Scheduling
Resource Management
Clutch Power: Solid Building Blocks
for Flexible Solutions
ResourcingScheduling
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Use Cases
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Use Case 1:Genomics
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Resources set up
Cluster
Desktops
CloudsEC2
SOLID
machine from
Nodes
can be
dynamically
added!
16nodes
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First Benchmarks
The distributed version with ProActive of Mapreads has been tested on the INRIA cluster with two settings: the Reads file is split in either 30 or 10 slices
Use Case: Matching 31 millions Sequences with the Human Genome (M=2, L=25)
4 Time FASTER from 20 to 100Speed Up of 80 / Th. Sequential : 50 h 35 mn
On going Benchmarks on Windows Desktops and HPCS 2008 …
EC2 only test: nearly the same performances as the local SOLiD cluster (+10%)
For only $3,2/hour, EC2 has nearly the same perf. as
the local SOLiD cluster (16 cores, for 2H30)
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Benchmark: local vs. hybrid cloud
Use case: 3 runs performed in parallel containing a total of 28,5
millions of reads to be matched against the human genome
SOLID nodes only
Standard configuration
using SOLID embedded
nodes: 12
SOLID and EC2 nodes
12 SOLiD nodes
12 EC2 machines(type: “mlarge”, 2 nodes each)
Total computation time:
12.5 hours
Total computation time:
8 hours
Gain: 4,5 hours (36% faster)
EC2 costs: $40
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Benchmark: local vs. EC2 cloud
Execution time
(min)
Cost
(US$)
Standard PBS config 300 NA
ProActive Amazon EC2 340 20 US$
For only $3,2/hour, the EC2 setup has nearly the
same performances as the local SOLiD cluster
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UC 2: Acceleration of
Financial Valuations
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A High Performance Solution
A Collaboration between Pricing Partners and ActiveEon
Price-it® Excel Accelerated by ProActive Parallel
Suite®
A Global Solution: fully integrated with the
same functionalities and interface as Price-it
Excel while increasing its computing power
High Quality Service: from both companies
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Some Technical Facts
Price-It®
C++ library developed by Pricing Partners
Pricing solution dedicated to highly complexfinancial derivatives
Specification and Constraints
Accelerate Price-It® Excel product Built on Price-It® library, this product integrates
an interface with Excel for input data managementand results display
Focus on highly parallelizable Greek computation
Operating system: Windows
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How Does it Work?
Price-it Computing Distribution
Price-it
Excel
ProActive
Scheduler
Pool of shared
resources
Automatic execution
via job scheduler
Regular Price-it Excel
Interface
Price-it
Excel
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Accelerated Price-it Performances
Use Case: Bermuda
Vanilla, Model
American MC
Test conditions:
One computation
is split in 130
tasks that are
distributed
Each task uses
300ko
Sequential Distributed
More than 3 times faster
with only 4 nodes!
4 nodes 5 nodes 6 nodes 7 nodes 8 nodes 9 nodes
Even 6 times faster
with 9 nodes!
Increased Productivity: Reduces Price-it Execution
Time by 6 or more!
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UC 3: SOA Analysis
of Web Server Logs
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Parallel Services
Separation: BPEL – Parallel Serv. – Task Flow
Standards et Portable
Flexibility
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Parallel Services
Operational Services…
Parameter
Sweeping
Service
Divide &
Conquer
Other
Operational
Service
…
Domain specific Service
…Other
Basic Service
High level Business Process
Enterprise Grid
Resource Man.
Scheduling
Scheduling `of Taskflow Jobs
Job
Scheduling Parameter
Sweeping
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Agos Web
Client
BPEL/WS
Web Client
MySQL
Console
Scheduler/
RM
Interface
Admin
BPEL
Console
Admin WS
Console
BPEL
Cas d’études AMADEUS : Démonstration
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WS
Scheduler
RM Grille
DB
Log Transfer
Application
ID
Log Parsing
Log Transfer Application ID
Log Parsing
Log Parsing
Log Parsing
Log Parsing
Log Parsing
Log Parsing
Logs stockés
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Grid Monitoring integration
Scheduler
DB
Jobs
Scheduler
Resource
Manager
Job
X
Job
Y
Pool of nodes in the grid
VM1 VM2NATIVE
1VM3
NATIVE
3
NATIVE
2VM4 VMx
Business
Availability Center
Discovery
DDMUniversal
CMDB
Indicators
SiS
Discover Jobs,
Tasks &
Resources
Collect Grid
Components
statistics
indicators
JMX interface
JDBC
interface Create
Configuration
Management
System
information
Collect Grid Jobs
Type status
indicators
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AGOS Platform Management
• Monitoring of the entire platform
• Cover all layers in the scope
•Provide monitoring dashboard and
reports
Tasks scheduler &
Resources manager
• Integration with grid
components
• Grid insights through indicator
collection and running jobs on
grid resources
HP- Business Availability Center
(HP-BAC)
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Conclusion
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ProActive in Cloud Stack
IaaS
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Conclusion
Java Parallel
Toolkit
Multi-Platform
Job Scheduler
Resource
Manager
Flexibility
Clutch Power
Portability:
Windows, Linux, Mac
Versatility:
Desktops, Grids, Clouds
Free Professional
Open Source Software
Multi-Core: Ready for the next revolution
Virtualization: Dynamic Load Balancing onto VMs
Cloud: Smooth transition available (Desktop , Server, Cluster)
Dynamic Workload Management with Virtualization
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5656
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4. Cloud Seeding
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Cloud Seeding with ProActive
Amazon EC2 Execution
Cloud Seeding strategy to mix heterogeneous
computing resources :
External GPU resources
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Amazon EC2
GPU nodes
CPU nodes
ProActive Scheduler
+ Resource ManagerWeb Interface
User
Noised video file
Cloud Seeding with ProActive
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Amazon EC2
GPU nodes
CPU nodes
ProActive Scheduler
+ Resource ManagerWeb Interface
User
User submit its noised video to the web
interface
Cloud Seeding with ProActive
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Amazon EC2
GPU nodes
CPU nodes
ProActive Scheduler
+ Resource ManagerWeb Interface
User
Web Server submit a denoising job the
ProActive Scheduler
Cloud Seeding with ProActive
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Amazon EC2
GPU nodes
CPU nodes
ProActive Scheduler
+ Resource ManagerWeb Interface
User
CPU nodes are used to split the video into
smaller ones
Cloud Seeding with ProActive
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Amazon EC2
GPU nodes
CPU nodes
ProActive Scheduler
+ Resource ManagerWeb Interface
User
CPU nodes are used to split the video into
smaller ones
Cloud Seeding with ProActive
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Amazon EC2
GPU nodes
CPU nodes
ProActive Scheduler
+ Resource ManagerWeb Interface
User
GPU nodes are responsible to denoise
these small videos
Cloud Seeding with ProActive
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Amazon EC2
GPU nodes
CPU nodes
ProActive Scheduler
+ Resource ManagerWeb Interface
User
GPU nodes are responsible to denoise
these small videos
Cloud Seeding with ProActive
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Amazon EC2
GPU nodes
CPU nodes
ProActive Scheduler
+ Resource ManagerWeb Interface
User
CPU nodes merge the denoised video parts
Cloud Seeding with ProActive
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Amazon EC2
GPU nodes
CPU nodes
ProActive Scheduler
+ Resource ManagerWeb Interface
User
CPU nodes merge the denoised video parts
Cloud Seeding with ProActive
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Amazon EC2
GPU nodes
CPU nodes
ProActive Scheduler
+ Resource ManagerWeb Interface
User
The final denoised video is sent back to
the user
Cloud Seeding with ProActive
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6. SOA, SLA and QoS
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Standard system at Runtime: No Sharing
NoC: Network On ChipProofs of Determinism
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AGOS: Grid Architecture for SOA
AGOS Solutions
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Building a Platform for Agile SOA with Grid
In Open Source with Professional Support
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AGOS Infrastructure Management
• Monitoring of entire infrastructure
• Communicates with upper layer
management software (HP BAC)
Citrix XenCenter
• Hypervisor and VM
management
• Communicates with upper
layer management software
(HP BAC)
HP Systems Insight Manager (HP-
SIM)
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AGOS and HP Management tools Integration
ServicesProcesses and Jobs
Grid ComponentsScheduler, Resource Manager
Hardware InfrastructureServers, Storage, Network Components
Hypervisors/Virtual MachinesXen and Vmware Hosts and Guests
Dis
covery
Monitoring