Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Area of Science:

• Computer Science
• Distributed Systems
• Artificial Intelligence

Background:

• Fog computing (FC) extends cloud capabilities to the network edge, addressing IoT computational needs.
• Effective task scheduling is crucial for energy efficiency, resource optimization, and timely task completion in FC.

Purpose of the Study:

• To comprehensively review advancements in task scheduling methodologies for fog computing systems.
• To identify key challenges and propose future research directions for robust FC task scheduling.

Main Methods:

• Systematic literature analysis of various task scheduling approaches: priority-based, greedy, metaheuristics, learning-based, hybrid, and nature-inspired.
• Identification and discussion of challenges including dynamic environments, heterogeneity, scalability, resource constraints, security, and transparency.

Main Results:

• Evaluation of the strengths and limitations of diverse task scheduling algorithms in the context of fog computing.
• Identification of critical challenges hindering the performance and adoption of current FC task scheduling solutions.

Conclusions:

• Future research should focus on integrating machine learning, federated learning, resource-aware algorithms, security techniques, and explainable AI.
• Addressing these research directions will foster more robust, adaptable, secure, and sustainable fog computing systems.