EA Parallelization Framework

This framework serves the parallelization of Evolutionary Algorithms on a cluster. Parallelization Models implemented till now:

1. Coarse-Grained Parallelization Model (Island Model)
2. Global Model (Master-Slave Model)
3. Coarse-Grained Global Hybrid Model (Island - Master-Slave Hybrid Model)

Getting Started

This framework runs on the cluster using Kubernetes orchestration and Container-Virtualization (Docker).

Prerequisites

• You need to have a kubernetes cluster and the kubectl command tool installed on your local machine.
• linux OS
• Docker installed
• Java 8 or higher
• Spring Boot
• Redis local / on cluster
• IntelliJ or Eclipse
• Maven

Usage

1. Upload the Docker images to the cloud using Dockerfile
2. Deploy the Microservices using the .yaml files in kubernetes folder
3. Open frontend in browser and submit job to be optimized

Built With

• Spring Boot
• Maven - Dependency Management
• Redis - Used to implement the publish/subscribe messaging pattern
• Docker - Container Virtualization
• Kubernetes - Used to implement Microservices Architecture on Cluster

References

Framework Papers:

• Khalloof, H., Mohammad, M., Jakob, W., Shahoud, S., Duepmeier, C., Hagenmeyer, V. A Generic and Scalable Solution for Hierarchical Parallelization of Population-Based Metaheuristics: A Microservices and Container Virtualization Approach
• Khalloof, H., Jakob, W., Liu, J., Braun, E., Shahoud, S., Duepmeier, C., Hagenmeyer, V.: A Generic Distributed Microservices and Container based Framework for Metaheuristic Optimization
• Khalloof, H., Ostheimer, P., Jakob, W., Shahoud, S., Duepmeier, C., Hagenmeyer, V.: A Distributed Modular Scalable and Generic Framework for Parallelizing Population-Based Metaheuristics.

Contact: Hatem Khalloof

Authors

• Hatem Khalloof

• Phil Ostheimer

• Mohammad Mohammad