SDN Control Plane Scalability Framework

Job ID: 40500104

Budget: ₹5,000 – ₹15,000 INR

I’m refining an SDN framework that targets one goal—significantly higher scalability inside the control plane. Your task is to re-architect and prototype the control logic so it can gracefully handle large-scale topologies and high churn without sacrificing responsiveness.

The work will involve choosing (or modifying) a controller platform such as ONOS, Ryu, or OpenDaylight, instrumenting realistic testbeds in Mininet or a cloud lab, and producing clear metrics that demonstrate the improvement over a vanilla deployment. Familiarity with OpenFlow, gRPC-based southbound interfaces, clustering, and state-distribution techniques will be essential.

When you respond, attach concrete past work that proves you have already tackled controller optimization, distributed systems, or comparable networking challenges. I’ll shortlist purely on the strength of those examples; no generic résumés, please.

Deliverables
• Architecture document explaining the scalability strategy
• Clean, runnable source code / configs (Git link)
• Reproducible test environment description
• Performance report comparing baseline vs. enhanced controller

Acceptance criteria will be met when the report shows quantifiable gains (e.g., reduced controller CPU load and sub-second convergence) at a scale at least 2× larger than the baseline scenario.

Publication in SCI - 5 months- 1.25 lakhs
Related categories: Python Git Mininet Ryu Controller Distributed Systems