7th January, 2025 || IIT Hyderabad, India
In the quest for a sustainable future, the combination of Software-Defined Networking (SDN) and Machine Learning (ML) within Hybrid Intelligent Computing emerges as a powerful driver. SDN’s centralized control and programmability create a basis for optimizing resources, thereby reducing energy consumption in network infrastructures and data centers. This architecture also facilitates the smooth incorporation of renewable energy sources, further lowering the carbon footprint of network operations.
ML enhances network management by utilising historical and real-time data to forecast traffic patterns, detect abnormalities, and optimise routing decisions, thereby complementing the capabilities of SDN. This proactive and flexible method improves the effectiveness of the network, minimising energy consumption during periods of high usage and promoting environmental initiatives. Furthermore, the combination of SDN with ML effectively enhances resource optimisation, guaranteeing efficient utilisation of processing power and energy, thus minimising wastage. This collaboration also facilitates proactive maintenance, which decreases network downtime and improves dependability. Additionally, the efficient implementation of edge computing reduces data transmission distances, resulting in energy conservation. SDN and ML play a crucial role in smart city initiatives and IoT deployments by enabling effective traffic control, reducing congestion, and minimising pollution. Through the utilisation of real-time sensor data, these technologies optimise traffic flow by dynamically altering traffic lights and routing, thereby making a valuable contribution to creating a more environmentally friendly urban setting. Overall, the integration of Software-Defined Networking (SDN) and Machine Learning (ML) in Hybrid Intelligent Computing is a crucial advancement towards a sustainable and environmentally conscious future. This integration ensures that network management is aligned with the principles of environmental responsibility. Workshops should emphasize current topics of interest within the broader areas of Distributed Computing and Communication Networks. Some of the topics of interest are mentioned below, but not limited to:
5G and Beyond
Ad-hoc, Sensor, PAN and Mesh Networks
Backscatter and Ultra-low Power Networks
Biological Distributed Algorithms
Blockchains
Cloud Computing and Big Data Processing
Cognitive Networking
Disaster response
Distributed AI/ML
Distributed Data-Analytics
Distributed game theory
Energy and Sustainability
Edge, Fog Computing and Mobile Offloading
Fault-tolerance, Reliability, and Availability of Distributed Systems
Federated Learning
Internet of Things and Cyber-Physical systems
Mobile Crowdsensing and Social Network Analysis and Mining
Next-generation internet
Pervasive and Mobile Computing
Peer-to-Peer Networks - Architectures and Algorithms
Security, Privacy and Game Theory in Distributed Computing and Networking
Smart City Applications
Self-* (Self-organization, Self-stabilization, Self-healing etc) and Autonomic Computing
Unmanned Aerial Vehicles
Vehicular Networks
Indian Institute of Technology Hyderabad,
Kandi-502284
Sangareddy, Telangana, India