in conjunction with 

26th International Conference on Distributed Computing and Networking 

(ICDCN 2025) 

 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