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Global Locations

Partnerships

Key information

Management

Denis

Caromel, CEO

François

Tournesac, CSO

Brian Amedro,

CTO

Founded in 2007 by Denis Caromel in Sophia-Antipolis, Spin-off of INRIA

Addressing $80 Billion Hybrid Cloud Market with 27% CAGR

Disruptive Patented Technology w/ Exceptional Business Outcomes

60% of the revenue from international

Sophia-Antipolis (France)

Paris (France)

London (United Kingdom)

San-Jose (United States)

Montreal (Canada)

Fribourg (Switzerland)

Dakar (Senegal)

ProActive Solution

Job Scheduling, Workload Automation

Orchestration & Meta-Scheduling

On-premises and on all clouds

Open Source

REST APIsApp-specific Interfaces Integrated Web Portals

ProActive Workflows

Scheduling Orchestration

Meta-schedulingResource Allocation

Big Data, Data Science,

Third Party Software

Local

Scheduler

Resource Manager

Fault Tolerance

Cloud bursting

Resource agnostic

Micro-service

Etc.

Workflow Automation

Cloud Data Lake

LSF

Clusters

Legal & General

$1,200,000 Azure Computing per Year

• Utilize ActiveEon’s ProActive to distribute the load

• Burst calculations into Azure

• “Infinite capacity” reduce Time To Results

Legal & GeneralCHALLENGES

Azure Migration

Resource allocation according to CPU and

memory available

Solvency II analysis on 2.5 million Monte Carlo

scenarios

RESULTS

Optimized resource allocation

Algorithm acceleration

Shorter scenario analysis

Scalable solution

• Migration from private datacenter

• Replacing old Tibco Datasynapse

• Replacing old IBM Algobatch

MAIN DRIVER

REQUIREMENTS

• Cloud capable

• Dynamically define and prioritize workloads

• Minimize time to delivery of results

• Save resources

COMPANY PROFILE

• Industry: Insurance

• Product: Compliance algorithm

Legal & General

Time (hours)

105 160

1024 W

ork

ers

Profile Results

Time (hours)

1024 W

ork

ers

Profile Results

105 140

I/O intensive tasks

Aggregation &

Reporting

Low Priority

CPU-Intensive tasks

Risk Watch

Simulation

Medium Priority

CPU-Intensive tasks

Risk Watch Simulation

High Priority

CPU-Intensive tasks

Risk Watch Simulation

“More Value, Faster”

Full computation without intermediate result High Priority

ResultsFull Report

2H 5H

Batch optimization with ProActive:

• Enforce strong priorities

• Optimal compact execution

• Start tasks as early as possible

• Pipeline and co-allocate

• CPU-intensive with I/O intensive

tasks

18H

End-to-end execution

from 18h on-prem to 5h on Azure

Legal & General with Azure

Task monitoring with ProActive

Automated grid start on Azure

Life cycle management of Azure nodes

Fault-Tolerance: this host died, and ProActive rescheduled the tasks executing on it, and routed Tasks around the faulty node afterwards

Execution on 1 024 Azure nodes

CHALLENGES

Multiple secured environments behind a firewall

Scale to support Big Data needs

Connect to various databases with specific RBAC

RESULTS

Secured communication between environments

Shared resources for maximum performances

• Allocation of resources across environments

• Respect RBAC during compute time

MAIN DRIVER

REQUIREMENTS

• Support use of Docker

• Support Hadoop, Python, SAS, Spotfire, Greenplum

• High availability

COMPANY PROFILE

• Industry: Government

• Product: Data analysis for criminality reduction

Main Benefits

• Central Orchestration Tool

• Workflow expressiveness: Universal & Comprehensive

• Management of Security forhighly sensitive environments

• Management of Resources for all appliances (SAS, GREENPLUM, TIBCO, …)

Execution prod Analytical prod Staging Dev

Virtualized Infrastructure using Docker4 000 Physical Cores

PEPS: Sentinel Satellite Image Analysis

ProActive task

ProActive nodes

Data from Mining

Machine Sensors

Data Processing

on Premises &

in the Clouds

Health and performance

of machines

Schedule data analytics

hourly or on events

Data

Real-Time Control & Optimizations

IoT Automation in the Cloud

ActiveEon allowed to migrate

from AWS to Azure

SchedulerPassive

Main Benefits

Deployed On Premise (Capex) or on a Hosting Service (Opex)

Auto-scaling on infrastructure to match capacity and demand

Huge costs optimization using only the VMs needed and interruptible low cost instances (e.g. EC2 Spot instances)

Capacity to deliver the system on any third party Mediametriecustomer

TV Audience

Measurement

SchedulerActive

EC2 Spot Instances

Low costs

EC2 Instances

Regular costs

IaaS

On-Prem

Azure PoC in the Box

Azure Node Source InfrastructureAzure Node Source Infrastructure

Scale automaticallyLeverage Azure services

Existing Resources

Local & Network Resources Private Cloud, HPC & Others

Resource

Manager

Workflow

Scheduler

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Azure Node Source Infrastructure

LSF

Using Azure Scale Sets

Deploy over

150 k nodes

150 K Nodes Benchmarks on Azure

150 K Nodes Benchmarks on Azure

• Up to 150 000 Nodes with a single Scheduler

instance on Azure

BIG COMPUTE READY

RESPONSIVE & RELIABLE

FAST & SCALABLE

• Average Response time of the scheduler: 6 ms

(Min 4 ms, Max 38 ms)

• Time to deploy Nodes:

• 10K: 5 mn

• Scale up to 40K: 10 mn

• Scale up to 80K: 15 mn

• Scale up to 150K: 25 mn

(To be compared: Average Time to buy a 150-Cores Servers/Cluster: 1 Year!)