The new generation of computing optimization algorithms has enabled the introduction of machine learning and deep learning mechanisms. It comes with new promises of improvement in existing models. Cloud, Internet of Things (IoT), and fog computing deal with many such optimization models which can improve with an increase in the performance of the system. Many algorithms such as task allocation, virtual machine scheduling, migration algorithm, power-efficient systems, trust models, and scaling algorithm are some of the existing algorithms in cloud computing. They come with a great scope of performance improvement in the system that can be achieved using algorithms inspired by nature and the latest prediction algorithms.

However, existing algorithms in cloud and fog computing suffer from limited computing capabilities, high energy cost, high computation cost, low utilization, and efficiency without scaling features. To overcome these drawbacks, there is a need for further research in terms of improving models for better performance. Fortunately, artificial intelligence (AI) and machine learning (ML) technologies incorporating existing models provide a promising way toward next-generation algorithms with predictive methodology. Nevertheless, the integration of AI and ML with cloud and edge/fog techniques is still a critical issue that needs intensive research and tuning.

This Special Issue aims to bring together original research and review articles discussing optimization in cloud, IoT, and fog computing using AI, metaheuristic approaches, or nature-inspired algorithms. This Special Issue is looking for any optimization model which can improve the system performance using new generation algorithms using artificial intelligence, deep learning, or hybrid algorithms. Submissions are expected to discuss new approaches and emerging research areas.

Potential topics include but are not limited to the following:

• Optimization algorithms for SAAS (Software as a Service) and PAAS (Platform as a Service)
• ML and AI-based optimization approaches
• Optimization in fog computing using AI/ML
• Task scheduling optimization in cloud and fog computing
• Cloud resource allocation
• Cloud virtual machine optimization
• Power-efficient algorithms in cloud and fog computing
• Trust and fault aware algorithms in cloud and fog computing
• Security protocols for cloud computing
• Resource scaling in cloud computing
• Bio-inspired algorithm for scheduling in cloud computing
• Heuristic-based algorithm in cloud computing
• Reliability in cloud computing
• Architectures and systems in cloud computing
• Hybrid cloud environment
• Datacentre networking in cloud and fog computing
• Fault tolerance and reliability of big data systems in cloud and fog computing