Activeeon is a key technology provider and actor in the cloud migration. Activeeon offers software and middleware solutions for Big Compute, workload automation and HPC. The company also provides workflows solutions for Machine Learning & IA.

1. Paris, Sophia Antipolis, London, San Jose USA
A Key Technology Provider and Actor
in the Cloud Migration
within all Big Compute verticals and at the heart of IA/Machine Learning
revolution

2. AE Mission
Build and Deliver to the Market the Best
Workflows/Orchestrator Suite for
Automation & Big Compute* in the Cloud
At the core of the Revolution:
Big Data – AI / Machine Learning – IoT – Workload Automation – Cloud Migration
* Big Compute: Business applications in need of high number of Cloud VMs/Containers

3. AE Vision
Workflows
For Application People,
Data Scientists, ML + IA Experts
Scheduling & Meta-Scheduling on all Infra
For Legacy, Cloud Migration & Hybrid
Resource Management
For Hybrid and Effectiveness on the Cloud

4. Express Business Needs with Granular
Workflows
• Distributed & Parallel Computing
• On-premises & Cloud Architectures
A 30+ PhD/Engineer team with focused fields of high expertise:
• Big Data
• IA, ML
• IoT
• Finance
• Gov.
• HPC
• ...
• Data Science, Machine Learning, IA, Matlab, R
• Big Compute and HPC
Visdom
ActiveEon Technology
R&D Investment: 250 M/Y at INRIA + 150 M/Y at AE = 400 Man/Year

5. Process Flow & Operation
Execute &
Monitor
Design
Schedule, Monitor &
Connect the
resources
IT department, Data
Scientist, Business Lines,
Activeeon Services IT department,
Business owner
Operational team
1
2
3
PROACTIVE
STUDIO
PROACTIVE
RESOURCES MANAGER
PROACTIVE SCHEDULER
PROACTIVE
AUTOMATION
PORTAL

6. Next Generation Scheduler/Orchestration
Scheduler and Orchestration
Priority
& Planning
Parallel
Executions
Error
Management
Multi Users
</>OpenRESTAPI
Resource Management and Monitoring
Slurm
SGE
PBS
LSF
Multi-
platform
Local
Machine
Network
Resource
Batch
Scheduler
Cloud
Processing and Automation Workflows
Any
language
Secured
Data
Transfers
Meta-
scheduler
ETL, ERP,
ELT, …
Full
integration
Translator

7. Customer Pains
Context: Digital Transformation, Cloud Migration, DevOps call for Automation
New Scalability Requirements (Big Data, ML, IoT ...)
Pains AE Pain Reliefs
Automation is complex and
time-consuming
Unnecessary VMs in the
Cloud are expensive
Currently using old products
with outdated architecture
Powerful Workflows to Automate and
Optimize
Optimal execution of Workload in the
Cloud
Modern Architecture:
• Micro Services
• REST APIs
• Web Interfaces, available
On-prems and in SaaS
20-50% of cloud resources are unused because of VM over-sizing or bad decommissioning strategies

8. 1. Technology & Product
Strong Product Line:
From Workload to Finance, IoT, Machine Learning/AI Automation

9. AE: The Technology Foundation
Patents:
• Method of locating mobile communicating objects within a communications network,
comprising the transmission of location identifiers by repeaters and server updates
https://www.google.com/patents/EP1652346A1?cl=en
• Asynchronous and automatic device and method for transmission of results between
communicating objects
https://www.google.com/patents/US20070147277
400
Man-Year
R&D Investment since 2005
250 M-Y from INRIA
150 M-Y from AE
110Publications
Articles published in International
Conferences and PhD Thesis
2
Patents
- Method of Locating Mobile Objects
within a Communications Network
- Asynchronous and automatic
communication of results
An Industry Thought
Leader
20
M €
Total € Injected in the Product
Development since the beginning

10. Core Product
Processing & Automation Workflows
Scheduling & Orchestration
+ Meta-Scheduling
Resource
Management
& Monitoring
Workflow
Studio
Job
Console
Resource
Manager
Highly Flexible and Scalable architecture:
Micro Services, REST APIs, On Premise or SaaS

11. Next Generation Scheduler/Orchestration
</>
APIApp-specific Interfaces Integrated Web Portals
ProActive Workflows
Big Data, Data Science, Third Party Software
Scheduler
Resource Manager
Fault Tolerance
Cloud bursting
Resource
agnostic
Micro-service
Etc.
Multi-Cloud Orchestration
Meta-scheduling
Resource AllocationWorkflow Automation
LSF
Clusters Scheduler Cloud Local Big
Compute for
Everyone!

12. On Premises & Multi-cloud orchestration
Deploy over
20k cores
Azure Node Sources Private Node Sources
Node Node
PBS
Resource
Manager
Workflow
Scheduler
</>
AWS Node Sources
EC2 EC2
Autoscale
Scale automatically - Leverage All Resources

13. Open Workflow Studio

14. Machine Learning Open Studio
Fully Compliant with Docker
Any Machine/Deep Learning
Libraries
Real Time Visualization
with Visdom, etc.
Catalog solution for Sharing &
Production (DevOps)
Scale with Parallel &
Distributed execution
LearningData Prediction
Visdom
Configuration & Pre-Defined Palettes for AI, ML, DML

15. Machine Learning Open Studio
https://www.youtube.com/watch?v=mbrQxCf4lqM

16. Supporting Specialized Processing
Cuda
OpenCL
FPGA
ASIC
Edges for IoT
(Raspberry Pi, …)

17. Automation Dashboard
A complete High-Level Portal allowing Users to
Execute, Plan, Monitor Jobs & Deploy PaaS Services from a single central place

18. Job Planner: Schedule Recurring Jobs
DefineCalendars AssociateWorkflowstoCalendars VisualizeExecutionPlanning
Manage recurring Jobs
Forecast and check future
Executions
Control recurring jobs from one
endpoint
Schedule Exceptions through
Exclusion Calendars &
Inclusion Calendars

19. Cloud Automation: On-demand PaaS
On-Demand PaaS Services with full Life-Cycle Management

20. Cloud Automation: Monitoring
On-Demand PaaS Services with full Life-Cycle Management

21. Job Planner
Workflow Trigger
Rest API
Event Based
Manual
Recurring Jobs, Exceptions, Planned Jobs Complex Event Processing
Dashboard, Studio & Scheduler InterfacesPOST call
Get
Post
Put
Delete
</>

22. - Activate the rule through the
Cloud Automation dashboard
- Provide the parameters
required for the condition of the
rule
- Manage the lifecycle of our rule
from the same dashboard
- If the condition is met, the rule
will:
 trigger a notification, and/or
 send an email,
 report in a third-party portal,
 launch an action through a
specific Workfow
Monitoring (File System, Host, DB, etc.)

23. Global Locations
Some Supported Languages and Connectors
AWSAzure GCP Docker OpenShiftLinux Windows Solaris VMware Openstack
Infrastructure
Slurm PBS LSF
LSF
HPC Schedulers
Google
Cloud Platform
PBS
Works
Cmd Java Scala Javascript Groovy Ruby Jython Python Perle PHP R Cron LDAPPowerShell
Languages and Predefined Tasks
FTPURL SFTP MySQL Oracle
Data Connectors
Linux Bash
MongoDB Cassandra AWS-S3
Clouds
PostgreSQL Greenplum SQL Server Azure Storage
Azure
Data Lake
KafkaZookeeper Spark Hadoop
Big Data
Azure
Databricks Hadoop HDFS Twitter
LogstashSAP Elasticsearch
SGE
SGE
CNTK Keras PyTorch YOLO H2OTensoFlow
Machine Learning & Artificial Intelligence
Caffe Spark MLlib Pandas JupyterLab
Visdom
Visualization
KibanaSwarm Storm Clearwater
Cognitive
Services
Grafana
Scikit-Learn MXNet
AIX AS/400
C++/C#
Cuda
Specialized
Open/CL FPGA
DLib BigDL
DeepLearning
G4J
Kubernetes

24. 2. Some Typical Customer Cases:
Capabilities & Portfolio Revue
Large Worldwide International Companies
Early Adopters
Using ActiveEon for Critical Business Applications
Finance
IoT
Gov.
Manufacturing
Automotive
Aerospace
Nuclear
RedHat OpenShift

25. Some Major Customers
Telco & IT Bio Tech
& Health
FinanceEngineering Aeronautics Energy
& Space
Some Partners:
Media
Distribution
Government
IoTCosmetics

26. L&G a leading multinational finance and insurance company with headquarters in London
Situation
Comply with new European regulations: Solvency II, Basel III, etc.
Transform legacy system and embrace cloud computing
Solution
Activeeon ProActive and migration to the Cloud have enabled
faster and more reliable execution:
• Cloud bursting
• Error management
• Prioritization
Benefits
From 18 hours to 2 hours for priority reports
Agile development with an objective of 4,000 cores
 $1.2m / year committed spent on Cloud
Finance
Time
64VMs,eachwith16vCPUs

27. Komatsu is a Japanese multinational corporation
It manufactures construction, mining, industrial and military equipment.
Situation
ActiveEon Orchestrates on Cloud execution over hot and cold storage for streaming and batch analytics
> 1,200 tasks executed per hour
Solution
Activeeon ProActive has enabled control over and scheduling over execution:
• Error Management – Notification, Automated Recovery
• Job Planner
• Distribution & Parallelization
Benefits
• Reliable execution to orchestrate multiple services and resources
• Provide consistent results and KPIs to end users and BI Tools
IoT

28. PEPs is the French platform that offers access to the products of the Sentinel satellites (S1A and S1B, S2A and S2B, S3A
and S3B) of the European Union Program for Earth observation and monitoring Copernicus
Situation
Make Sentinel data available to the greatest number and
encourage the development of applications using them (agriculture, maritime field...)
1 petabyte (1015 bytes) in 20 years and 7 petabytes in 2 years!
Solution
Proactive Solution provided by ActiveEon to execute on Azure in hybrid mode
allows enhancing PEPS data and making them available to API providers :
• Multi-Cloud Ecosystem Platform
• Remove complexity for Data Scientists
• Provide Cloud performance
Benefits
• Faster execution, Optimisation of On-Prem ressources & Clouds,
• Easier to use by end-users
Space & Image Processing

29. Home Hoffice is the UK Ministry of Interior. They are using ActiveEon for 2 critical
applications:
• Visa Delivery Process, and
• Big Data & Analytics platform for Crime Reduction (HODAC).
Situation
In need to integrate 25 different sources of Data in order to build a consolidated
Data Lake and analytics platform to be used for many Home Land security
applications.
Solution
ActiveEon used as the central Orchestrator to Schedule and Meta-Schedule all the
Big Data, ETL, Analytics, Machine Learnigs software appliance of the platform
(Hadoop, SAS, TIBCO Spotfire, Python, Anaconda, GreenPlum, ElasticSearch, …).
Benefits
• Central Orchestration Tool
• Workflow Expressiveness: universal & comprehensive
• Management of Security for highly sensitive environments
• Management of Resources for all appliances (SAS, TIBCO,… ).
« ActiveEon is the only solution capable
to Schedule any Big Data Analytics,
mono-threaded, multi-threaded, multi-
core, parallel and distributed »
Cap Gemini Lead Engineer for Home
Office
Gov.: UK Ministry of Interior

30. Digital transformation for manufacturing
BENEFITS
Reduce the distance between the virtual and the
manufacturing process
Take advantage of digitalization in the machine tool
field for intelligent manufacturing and more efficient
production
FEATURES
Cloud-based big data analytics during
machining
Optimization of machining parameters using
workflows
Process simulation and optimization tools
Physical measurements and monitoring
Virtual / real part model correction
Use of AI
TARGETED SECTORS
Manufacturing, automotive, aerospace
Cloud processing services in manufacturing
END USERS

31. Workflows for HPC multi-physics engineering
simulations in automotive and aerospace
BENEFITS
Thermal resistance for engine partsFEATURES
Parallel evaluation of optimal mesh size for
the best tradeoff between execution time
and result accuracy
Complex workflow management: monitoring,
scheduling and orchestration
Infrastructure management: on-premises and
cloud HPC
Data collection and processing
END USERS
Pollution levels in a district
Workflow for exploration of tradeoff
between execution time and result accuracy
DOMAIN: COMPUTATIONAL FLUID DYNAMICS (CFD) AND POST-PROCESSING TOOLS
Acceleration and Automation of
Design Analysis and Optimizations

32. Deep Learning forAnomaly Detection in
Satellite Manufacturing
FEATURES
Detection of wires defect on a set of images
from production line using Deep Learning
Deep Learning on images of wires: occlusion,
variation, noise, grayscale, semantic analysis
Detection of defaults using a pre-defined wire
model and computing a distance measure
Workflows for model training and prediction for
parallel execution
BENEFITS
Automatic detection of defaults in hybrid
circuits manufacturing
Higher precision of Machine Learning results
Faster results with parallel execution of
machine learning workflows
Workflows can be used for other applications
Faulty wires come out in red

33. Big DataAnalysis forAutomatedAnomaly
Tracking in Satellite Communication
FEATURES
Data analysis: checking packets number of service
telemetries, order and type
Incident evolution forecasts
Big data workflows for automation of Test Scenarios
Automatic detection of remote controls that didn’t
receive expected telemetries
Data visualization in browser
BENEFITS
Automatic and early detection of defaults via trends
analysis of test results
Engineering process improvement: margin assessment,
robustness analysis, model elaboration based on actual
behaviors
Workflows allowing to accelerate treatments of fast-
growing test data amounts
Data fetching from many sources
ProActive workflow for service
telemetries verification
Visualisation of anomalies

34. Acceleration of Non-Destructive Evaluation (NDE)
for Nuclear Energy, Oil & Gas,Aerospace
FEATURES
NDE batch processing, parametric studies, non-
regression tests on multiple clusters
Transfer Input and Output data
Event programming to follow executions
Workflow process definition
Activeeon guidance and support
Cloud version: Execution on Microsoft Azure with
50 VMs/day per CIVA user  25K nodes/year
A potential of $1M$ Azure spending per Year
BENEFITS
Flexibility and enabler of interoperability between
heterogeneous infrastructures
Ability to run large POD (Probability of Detection)
computations, which were taking months on a
single computer
Large-scale simulations with Microsoft Azure cloud
Radiography – Pipes weld inspection
ABOUT CIVA NDE SOLUTION:
Multi-technique (Ultrasound, Eddy current,
Radiography) software platform developed by
the CEA LIST and its partners
The software is distributed by EXTENDE and
its distributors
Eddy current - Simulations
END USERS
Nuclear Energy, Oil & Gas, Aeronautics, Transportation

35. Orchestration of RedHat OpenShift On-Prem & OnAzure
Orchestrate & Manage all layers: IaaS, PaaS, SaaS.
Multi-Cloud, Hybrid, Scalable,

36. Platform for Cosmetic Formulation for 2000 persons around the world and
for Innovation Team. (Statistic, Machine Learning, Use of Language R)
2 000 persons
around the World
Innovation Team
(Statistics, ML, R)
Workflows OrchestrationMonitoring
Data
Compute
Data
Mining
Private
Network
+
HTTPS

37. Resource Manager
Scheduler Calendar
Sync
200 to 300 jobs
planned per week
72 000 patient diagnostics
delivered to nurses
Main Benefits
Job Visualization within Calendar
Edit job planning from both
interfaces
Visualize parallel tasks
Visualize task information in one
view
Usage of customer’s external database:
Oracle 11g Database
using Red Hat Hibernate ORM
(Object – Relational – Mapping)
Formerly part of
Task-Centric View Used

38. Scheduler
Passive
Mediametrie:
TV Audience
Measurement
Scheduler
Active
EC2 Spot Instances
Low costs
EC2 Instances
Regular costs
IaaS
On-Prem
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)

39. CHALLENGES
Process 500 terabytes per year
Flexibility and enabler of interoperability
between heterogeneous services
Job affinity with data location
Transfer sensitive data to the cloud for
processing
RESULTS
Efficient metagenomics pipeline
Granular compute management
User friendly system for maximum utilization
Secure transfers
Simple workflow process definition
Workflow model and data management
Compute migration from on-prem to the cloud
MAIN DRIVER
REQUIREMENTS
Guidance and support to achieve high
performances
Fit in hybrid architecture multiplatform
Integration with R
FlexLM support (licenses manager)
Remote Visualization for interactive tasks
COMPANY PROFILE
Industry: BioTech
Product: Metagenomics

40. Quantitative Metagenomics Platform
for gene profiling and statistical analysis
Domain-specific
Users
Windows
Cluster 1
192 cores
Linux
Cluster 2
366 cores
Scheduler
Web Portal
Total
DNA
QC/Library preparation
SoLiD/Illumina
Sequencing
1TB /
Sequence
Analysis
40TB
Parallel DataBase
Pre, Post Processing of Data Analysis
Flexibility, Speed of Analysis
Granular execution
Fast
Architecture Overview

41. ProActive
Cloud Watch
Environment Environment
MachineLearninginITLogAnalysisforErrorDetection&PredictioninFinancialMarket
Analysis &
Classification
• Machine Learning
• Artificial Intelligence
• Probabilistic Analysis
Resources /
Applications /
Services
Resources /
Applications /
Services
Resources /
Applications /
Services
Business Users
11 1
1
2
3
Collect data from
any sources
Update model
Update event
driven system
Events
Monitoring
Complex Event
Processing
• Rule based
• Actions triggering
3
Alert
Predictive
Incident
Request for incident
analysis
2
Automated
Preventive
Action
Incidents
Incidents
Finance Domain: Deep ML for IT Infrastructure
Main Benefits
Openness and diversity of ML
frameworks to be used (vs.
Splunk)
Both Batch and Streaming
Workflow Expressiveness:
universal & comprehensive
IT Users

42. 3. Company / Team
400 man / year of R&D
2 patents
30 highly qualified engineers out of which 17 are PhD’s
References in all Industries in the US and EMEA

43. Global Locations
Partnerships
Key information
Management
Denis
Caromel,
CEO
François
Tournesac, CSO
Brian
Amedro,
CTO
Company
ISV 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

44. 2005 An R&D Team of 45 persons headed by Denis Caromel developing a
Core Kernel for Distributed, Parallel & Cloud at INRIA (largest EU Computer
Science Research Institute, 6 000 persons).
Foundation of ActiveEon
Co-development between INRIA Team & ActiveEon
IP Technology Transfer from INRIA to ActiveEon
2007
2009 Scheduler added to the Core
2011 Resource Manager added
2013
2014
2016
2017
Orchestration with Powerful Workflows added
First very large customer references in Production
International Expansions in UK, USA, Africa
ActiveEon Story
R&D Investment
250 M/Y at INRIA + 150 M/Y at AE = 400 Man/Year

45. Paris, Sophia Antipolis, London, San Jose USA @activeeon
contact@activeeon.com
+33 988 777 660
Automate Accelerate & Scale
10K Nodes, 20K Tasks, 1M Jobs