1. DBOS DecisionBrain Optimization Server
Filippo Focacci Michel Eisenmann

2. Leading Developer of Advanced Analytics Solutions
We implement advanced analytics solutions to help companies reach their business operation goals
THOUGHT LEADERSHIP
IBM PARTNERSHIP
GLOBAL PRESENCE
30+ years experience in advanced analytics
Robust growth trajectory with over 30 employees, most PhDs
Largest global IBM business partner for optimization solutions
Beacon Award finalist in 2018
France
Hong Kong

3. MANUFACTURING / LOGISTICS
Global Partners Who Trust our Advanced Analytics
CUSTOMERS
WORKFORCE
DEPLOYMENTS
MANUFACTURING / LOGISTICS
SUPPORT TO R&D

4. A Platform Built to Ease Optimization Development & Deployment
A lean but powerful system designed to help you build and deploy fully scalable applications – quickly, easily, efficiently.
MODULAR ARCHITECTURE
Comes with pre-embedded workers and support for optimization models using CPLEX or OPL
STATE-OF-THE-ART TECHNOLOGY
Relies on standards and open source software, supports both local (Docker) and enterprise (OpenShift) configuration
EMBEDDED MONITORING CAPABILITIES
Simple web console makes it easy for users and administrators to monitor real-time job execution, KPI evolutions, and manage queuing and failovers

5. Main Concepts
Easy–to-access from a Web Console or an external Client (Rest API)
MAIN ARCHITECTURE
Client
Web Console
Master
Optimization
Worker
Tasks: - CPLEX
- CPO
Task: OPL
Database
MongoDB
Messaging
Rabbit HQ
DB Optimization
Server
RUN & MONITOR JOBS
A job has a type (Task) and input/output
One worker may support multiple Tasks

6. DBOS Key Features Interactive Web Console
Off-the-shelf support for IBM ILOG CPLEX
Docker-based deployment
Extensibility and full customization
Support for Kubernetes/OpenShift
Integration via REST API
Replay Executions
OR Department
Development teams
IT Department
Dev/Deployment Teams

7. Web Console – Drag-and-drop interface
Create and start a new job (from task definition)
QUICK JOB CREATION

8. Web Console – Job Monitoring
Track KPIs, progresses and logs in real-time
MONITOR JOB EXECUTION

9. Web Console – System Monitoring
REAL-TIME UPDATES
Quickly review system behavior
Immediately track execution issues

10. Web Console – System Monitoring
REAL-TIME UPDATES
Quickly review system behavior
Immediately track execution issues

11. On-shelf CPLEX/OPL workers
Pre-defined workers for CPLEX/OPL* to minimize processing time and maximize efficiency
CPLEX / CP OPTIMIZER WORKER
(Integer) Linear Programming
Quadratic Programming
Constraint Programming
OPL WORKER
CPLEX/OPL WORKERS
Web Console
Master
Optimization
Worker
Tasks: - CPLEX
- CPO
Task: OPL
Database
MongoDB
Messaging
Rabbit HQ
DBOS
For OR:
Thanks to DBOs, they can use any optimization language, they want to run the optimization
-Easy to run optimization
INPUTS
PROCESS
OUTPUT
Optimization model
Data & settings
Solution
*version 12.8+

12. Docker-based Deployment
As efficient as virtual machines, but much faster and modern with small footprints on servers, making it deployable everywhere.
DOCKER PACKAGING
Client
Web Console
Master
Optimization
Worker
Tasks: - CPLEX
- CPO
Task: OPL
Database
MongoDB
Messaging
Rabbit HQ
DBOS
Local with Docker-compose
Quick and simple
Template available
DEPLOYMENT
Speaks to IT guy

13. Easy to extend and customize
Have the flexibility to design custom workers using Java-based code
CUSTOM WORKERS
Custom
Worker
Your Task(s)
DEFINE AND IMPLEMENT
TASK(S) WITH:
Optimization
Server
Web Console
Your optimization code
Your machine learning code
Any kind of CPU intensive computation
Master
Messaging
Rabbit HQ
Optimization
Worker
Tasks: - CPLEX
- CPO
Speaks to Developer
Database
Optimization
Worker
Task: OPL
MongoDB

14. Example: Simple Java Client
Speaks to Developer

15. Example: Simple Custom Worker
Speaks to Developer

16. Web Console - Ready to Support Extensions
CLICK-TO-RUN
No web development required to support extensions
IMMEDIATE DATA PREP
Dynamically transforms and prepares data no matter its format or complexity

17. Designed for Advanced Deployment
Supports both an industrial deployment (full Kubernetes and OpenShift compatibility) and local deployment using Docker-Compose
DEPLOYMENT
Cluster with OpenShift
Web Console
Master
Optimization
Worker
Database
MongoDB
Messaging
Rabbit HQ
Server
Failover
Easy scalability
Easy automation
Resource monitoring
IT guy
Quick and simple
Template available
Local with Docker-compose

18. Seamless, Easy Integration
Use of REST API technology makes it easy to integrate with projects involving complex mathematical modeling
MULTIPLE LANGUAGES / FRAMEWORKS SUPPORT
Developer persona : enables them to use multiple languages (focus on this)
Not importatn for IT guy

19. Replay Job Executions – Monitor and Retrieve
Client
Web Console
Master
Optimization
Worker
Database
MongoDB
Messaging
Rabbit HQ
Server
Replay
DOWNLOAD LOGS & REPRODUCE
Monitor Failures
Retrieve input conditions

20. Map-Reduce for Optimization: Build and Deploy Optimization Decomposition on Distributed Resources in the Cloud 
Today
11:00am-12:45pm
Room 306