AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic review

Fog and cloud computing are emerging paradigms that enable distributed and scalable data processing and analysis. However, these paradigms also pose significant challenges for workflow scheduling and assigning related tasks or jobs to available resources. Resources in fog and cloud environments are heterogeneous, dynamic, and uncertain, requiring efficient scheduling algorithms to optimize costs and latency and to handle faults for better performance. This paper aims to comprehensively survey existing workflow scheduling techniques for fog and cloud environments and their essential challenges. We analyzed 82 related papers published recently in reputable journals. We propose a subjective taxonomy that categorizes the critical difficulties in existing work to achieve this goal. Then, we present a systematic overview of existing workflow scheduling techniques for fog and cloud environments, along with their benefits and drawbacks. We also analyze different workflow scheduling techniques for various criteria, such as performance, costs, reliability, scalability, and security. The outcomes reveal that 25% of the scheduling algorithms use heuristic-based mechanisms, and 75% use different Artificial Intelligence (AI) based and parametric modelling methods. Makespan is the most significant parameter addressed in most articles. This survey article highlights potentials and limitations that can pave the way for further processing or enhancing existing techniques for interested researchers.