Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification.

Area of Science:

• Computer Science
• Distributed Systems
• Internet of Things (IoT)

Background:

• Cloud-based Internet of Things (IoT) processing can be inefficient for time-sensitive applications due to bandwidth demands.
• Fog and edge computing paradigms process data closer to IoT end devices, addressing bandwidth and latency challenges.
• Effective resource management is crucial for optimizing performance in cloud, fog, and edge IoT environments.

Purpose of the Study:

• To review resource management techniques applicable to cloud, fog, and edge computing environments.
• To establish an evaluation framework with metrics for resource management algorithms in these distributed computing paradigms.
• To analyze current research contributions and identify challenges in IoT resource management.

Main Methods:

• Systematic review of research papers focusing on resource management in cloud, fog, and edge computing.
• Classification of existing research contributions to facilitate the development of an evaluation framework.
• Analysis of resource management techniques including allocation, workload balancing, provisioning, scheduling, and Quality of Service (QoS).

Main Results:

• An overview and analysis of various resource management techniques for cloud, fog, and edge IoT.
• Identification of key research challenges in applying resource management to these environments.
• A proposed structure for an evaluation framework to assess resource management algorithms.

Conclusions:

• Resource management techniques offer significant opportunities for optimizing cloud, fog, and edge IoT systems.
• The field of resource management for fog and edge computing is in its early stages of development.
• Further research is needed to overcome existing barriers and fully realize the potential of these techniques.