Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments

Abstract

The Internet of Things applications have become popular because of their lightweight nature and usefulness, which require low latency and response time. Hence, Internet of Things applications are deployed with the fog management layer (software) in closely located edge servers (hardware) as per the requirements. Due to their lightweight properties, Internet of Things applications do not consume many computing resources. Therefore, it is common that a small-scale data center can accommodate thousands of Internet of Things applications. However, in small-scale fog computing environments, task scheduling of applications is limited to offering low latency and response times. In this paper, we propose a latency-aware task scheduling method for Internet of Things applications based on artificial intelligence in small-scale fog computing environments. The core concept of the proposed task scheduling is to use artificial neural networks with partitioning capabilities. With the partitioning technique for artificial neural networks, multiple edge servers are able to learn and calculate hyperparameters in parallel, which reduces scheduling times and service level objectives. Performance evaluation with state-of-the-art studies shows the effectiveness and efficiency of the proposed task scheduling in small-scale fog computing environments while introducing negligible energy consumption.

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

“…However, it did not provide any solution to obviate the starvation of smaller tasks. In [69], an artificial neural networks-based algorithm reduced latency through data partitioning to compute hyperparameters in parallel, thus minimizing response time and latency. However, the completion rate of incoming tasks was not optimized.…”

A comprehensive review on internet of things task offloading in multi-access edge computing

“…Such methods support more decision optimization parameters and can cope with more complex task-offloading scenarios. In addition, numerous intelligent learning models and decision-making frameworks have been used to support IoT task offloading in MEC due to the rapid development of the AI research field [ [26] , [27] , [28] , [29] ].…”

EAEFA: An Efficient Energy-Aware Task Scheduling in Cloud Environment

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