A Review of Energy-efficient Task Schedulers in Fog Computing Systems for AI-IoT Environment

In the artificial intelligence and internet of things era, huge amount of computations and the surge in data generation leads to increase network traffic, making computational offloading essential for end users with limited resources. Cloud-fog computing systems refer to the utilization of non-local computing resources such as various servers, distributed storage and processing units via internet or intranet. Fog computing has emerged as a complementary solution to cloud computing, enhancing data processing and energy efficiency in low-power networks using in-node and edge processing. This paper presents a review on task scheduling techniques for fog computing systems. The focus of the paper is on the energy-efficient and low power high performance scheduler for fog-based architectures in internet of thing environments. As a result, we found that utilizing artificial intelligence and machine learning and also reinforcement learning approaches techniques for optimizations in designing task schedulers and offloading provides high performance and low power fog-based systems in IoT environment.

کلیدواژه ها:

IoT ، Fog-computing ، Task scheduler ، Microservice ، Artificial intelligence ، Machine learning