RESEARCH

• Privacy-Preserving Decentralised Machine Learning (aka. Federated Learning)

• Acceleration of Distributed Machine Learning

• Computer Networking, Distributed Systems and Cloud Computing.

• Congestion Control and Traffic Engineering.

• Software Defined Networking and Network Function Virtualization.

• Mobile/Wireless/Sensor Networking.

• Feedback Control, Hybrid and Switched systems and Stability Analysis.

• Artificial Intelligence, Optimization and Genetic algorithms.

Optimizing Distributed Machine Learning Applications

We are exploring and working on subjects that fall on the intersection between Computer/Network Systems Research and Machine Learning.

Cloud Computing and Data Center Networks

We are working extensively on problems related to applications’ performance issues in the non-conventional data center networking environments which differs completely from issues applications’ face in the wild internet. Our primary focus is public could and hence we develop techniques which imposes no modifications to the guest VMs in public clouds. The directions i am focusing under this broad category are exploring the following venues:

• Switch-based approaches to resolve the problems from within the network itself.

• Hypervisor-based approaches to resolve the problems from within the hypervisor of the host OS.

• SDN-based approaches to resolve the problems from within the controller of the network.

Switch based Approaches

In switch-based approaches we aim for developing intelligent Active Queue Management schemes that are designed specifically for data center environments. The schemes shall be simple enough for real adoption and incremental deployment in production large-scale data centers.

1. RWNDQ: Receive-window based Active Queue Management.

2. IQM: Incast-aware Active Queue Management.

3. HSCC: Smart Hysteresis-based Active Queue Management.

Find out more.

Hypervisor-based Approaches

Full list of publications.

SDN-based Approaches

Full list of publications.