Python Extension for Multi-Cloud Orchestration
Budget: ₹1,500 – ₹12,500 INR
I am working on an academic research project focused on multi-cloud resource orchestration using a decentralized framework called IMACO (Intelligent Multi-Agent Cloud Orchestration).
Currently, I have implemented a Python-based simulation system that includes:
Task scheduling using real workload data (Google Cluster Workload Traces)
Existing scheduling strategies:
Round Robin (RR)
Cost-based scheduling
IMACO (adaptive rule-based scheduler)
objective :
I want to extend the system by implementing:
1. MAS-Cloud+ (Multi-Agent System)
Each cloud provider should act as an independent agent
Agents evaluate tasks using scoring logic (cost, latency, load, SLA)
A coordinator selects the best agent for task allocation
Output must match existing format for comparison
2. Reinforcement Learning Integration
a) Deep Q-Network (DQN)
Learn optimal cloud selection policy
Replace static rule-based decision-making
b) Multi-Agent PPO (MAPPO)
Enable cooperative learning among multiple agents
Simplified implementation is acceptable
Currently, I have implemented a Python-based simulation system that includes:
Task scheduling using real workload data (Google Cluster Workload Traces)
Existing scheduling strategies:
Round Robin (RR)
Cost-based scheduling
IMACO (adaptive rule-based scheduler)
objective :
I want to extend the system by implementing:
1. MAS-Cloud+ (Multi-Agent System)
Each cloud provider should act as an independent agent
Agents evaluate tasks using scoring logic (cost, latency, load, SLA)
A coordinator selects the best agent for task allocation
Output must match existing format for comparison
2. Reinforcement Learning Integration
a) Deep Q-Network (DQN)
Learn optimal cloud selection policy
Replace static rule-based decision-making
b) Multi-Agent PPO (MAPPO)
Enable cooperative learning among multiple agents
Simplified implementation is acceptable