The 3rd International Conference on Intelligent and Cloud Computing (ICICC-2022) aims at the crossroads between scientists, researchers, practitioners and students from diverse domains in cloud computing research. The conference aims at attracting contributions of system and network design that can support existing and future applications and services. In recent years, intelligent and cloud computing attracted significant attention both in research and industry. This approach corresponds to natural human vision and is the best way to represent, generate and implement various contemporary achievements. The 2nd International Conference on Intelligent and Cloud Computing (ICICC-2022) will provide a forum that will bring together researchers, academia and practitioners from industry to meet and exchange their ideas and recent research achievements in all aspects of intellegent and Cloud Computing, together with their applications in the contemporary world. The scope of this conference has been kept wide and following are the topics covered (But not limited to):

• Cloud computing system and network design
• Cloud network protocol design and management
• Optimization for cloud computing, networking, and applications
• Green cloud system design
• Cloud storage design and networking
• Cloud system and storage security
• Cloud network virtualization techniques
• Modelling for cloud system, network and storage
• Performance analysis for cloud system, network and storage
• Big data storage and networking in the Clouds
• Intra-cloud computing and networking
• Mobile Cloud system design
• Cloud media and storage design
• Cloud system interoperability
• Utility computing solutions in Cloud systems
• Cloud forensics
• Networking for cloud computing
• Machine learning, data mining for cloud computing
• Edge, fog, and mobile edge computing
• Security, privacy, trust for cloud computing
• Machine learning for cloud resource management
• Machine learning for network measurement
• Data-driven methodology and architecture
• Networking for machine learning systems
• Resource management and device placement for machine learning systems
• Measurement and diagnosis for machine learning systems

An author of an accepted paper must be registered at full prior to uploading the camera-ready version. The maximum length of the camera-ready version is 10 pages (for regular) and 10 pages (for work-in progress).