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This page describes the research agenda of MNLAB for PhD. and Master students seeking topics/projects, and for collaborators and Funding sources
Monitoring Networks and Distributed Systems 
  • Scalable monitoring architectures in standard (SNMP) and proprietary protocols
  • Web-based Hierarchical Filtering-based Monitoring ( HiFi )
  • Monitoring of end-to-end multicast
  • Fault, performance and security management of multicast sessions
  • API for monitoring distributed services
  • Real-time monitoring/event-filtering
  • Integrating system and service monitoring and control
  • Active (programmable) monitoring and active network management
  • Automatic reactive control (application steering) for managing distributed multimedia
  • Distributed event correlation
  • Distributed debugging using event-based and state-based monitoring
Interactive Distance Learning
 
  • Next generation interactive distance learning ( IRI as a previous project): Internet-based IDL, scalable serves,
  • Interactive information retrieval of recorded multimedia sessions
  • Scalable Co-browsing using IP Multicast
Adaptive Reliable Transport Multicast Protocol
 
  • Design & development of a hybrid reliable multicast protocol
  • Network interoperability of heterogeneous reliable multicast Protocols
  • DR selection and fault recover in multicast routing
  • Flow and congestion control in reliable multicasting (i.e., slow clients problem)
End-to-End QoS Networks
 
  • Java-based Interface for NS
  • DiffServ technique for Web
  • Bandwidth brokers for end-to-end QoS
  • Traffic Engineering
Network Security: Firewalls
  • Analyzing firewall policies for conflict-free configurations (Anomaly Detection)
  • Enhancing firewall intelligence, performance, and scalability
  • Designing traffic-aware packet classification techniques
  • Load balancing over multi-firewall configurations
Network Security: DDoS Detection and Mitigation
  • Discovering DDoS via lightweight traffic analysis
  • Building a distributed DDoS detection framework
  • Developing metrics for anomaly detection