Dynamic Scheduling of Contextually Categorised Internet of Things Services in Fog Computing Environment

Area of Science:

• Computer Science
• Network Engineering
• Distributed Systems

Background:

• Fog computing addresses Quality of Service (QoS) needs like low latency and high throughput in upcoming solutions.
• Internet of Things (IoT) environments benefit from fog computing due to local devices and container virtualization.
• Optimizing fog computing requires algorithm-based service scheduling considering targeted QoS parameters.

Purpose of the Study:

• To propose a novel scheduling algorithm for fog computing environments.
• To enhance service performance by optimizing key QoS parameters.
• To demonstrate the effectiveness of dynamic scheduling in volatile network conditions.

Main Methods:

• Categorization of IoT services to inform scheduling algorithm design.
• Development of a scheduling algorithm considering processing, user, and service contexts.
• Simulation-based performance evaluation of the proposed algorithm.

Main Results:

• The proposed algorithm effectively schedules service components across fog-to-cloud environments.
• Dynamic scheduling demonstrated responsiveness to changing network conditions and QoS parameters.
• Significant enhancement in service performance based on targeted QoS criteria was observed.

Conclusions:

• The developed dynamic scheduling algorithm optimizes fog computing performance in IoT.
• The algorithm's context-aware approach ensures efficient resource utilization.
• This work contributes to realizing the full potential of fog computing for QoS-sensitive applications.