NF-MORL: a neuro-fuzzy multi-objective reinforcement learning framework for task scheduling in fog computing environments

Abstract

The proliferation of IoT devices has exerted significant demand on computing systems to process data rapidly, efficiently, and in proximity to its source. Conventional cloud-based methods frequently fail because of elevated latency and centralized constraints. Fog computing has emerged as a viable option by decentralizing computation to the edge; yet, successfully scheduling work in these dynamic and heterogeneous contexts continues to pose a significant difficulty. This research presents A Neuro-Fuzzy Multi-Objective Reinforcement Learning (NF-MORL), an innovative framework that integrates neuro-fuzzy systems with multi-objective reinforcement learning to tackle task scheduling in fog networks. The concept is straightforward yet impactful: a Takagi-Sugeno fuzzy layer addresses uncertainty and offers interpretable priorities, while a multi-objective actor-critic agent acquires the capacity to reconcile conflicting objectives makespan, energy consumption, cost, and reliability through practical experience. We assessed NF-MORL using empirical data from Google Cluster and EdgeBench. The findings were promising: relative to cutting-edge techniques, our methodology decreased makespan by up to 35%, enhanced energy efficiency by about 30%, reduced operational expenses by up to 40%, and augmented fault tolerance by as much as 37%. These enhancements persisted across various workload sizes, demonstrating that NF-MORL can effectively adjust to fluctuating situations. Our research indicates that integrating human-like reasoning through fuzzy logic with autonomous learning via reinforcement learning can yield more effective and resilient schedulers for actual fog deployments.

Keywords: Energy efficiency; Fault tolerance; Fog computing; Multi-objective reinforcement learning; Neuro-fuzzy systems; Task scheduling.

Fig. 1

Architecture of fog computing system.

Fig. 2

CPU utilization membership function.

Fig. 3

Memory membership function.

Fig. 4

Bandwidth membership function

Fig. 5

Task length membership function.

Fig. 6

Centroid defuzzification.

Algorithm 1

Neuro-fuzzy adaptive task priority evaluation.

Fig. 7

System model architecture.

Algorithm 2

NF-MORL task scheduling.

Fig. 8

Makespan of small datasets.

Fig. 9

Makespan of medium datasets.

Fig. 10

Makespan of large datasets.

Fig. 11

Consumption of energy for small datasets.

Fig. 12

Consumption of energy for medium datasets.

Fig. 13

Consumption of energy for large datasets.

Fig. 14

Execution cost small datasets.

Fig. 15

Execution cost medium datasets.

Fig. 16

Execution cost large datasets.

Fig. 17

Fault tolerance small datasets.

Fig. 18

Fault tolerance medium datasets.

Fig. 19

Fault tolerance large datasets.

Fig. 20

Fog distribution of each episode small datasets.

Fig. 21

Fog distribution of each episode dataset Medium.

Fig. 22

Fog distribution of each episode dataset large.

Fig. 22

Fog distribution of each episode dataset large.

Fig. 23

Scalability of NF-MORL with a rising number of fog agents (10–100). The left axis (blue) represents normalized wall-clock training time in relation to the single-agent baseline, while the right axis (red) indicates scheduling throughput measured in tasks per second.

Fig. 24

A physical fog testbed consisting of 20 Raspberry Pi 4 fog nodes, 50 ESP32-C6 edge devices, and a Dell PowerEdge R740 cloud aggregator, all linked via an authentic 5G campus network (left), accompanied by a live monitoring dashboard that exhibits task completion and power consumption over a 48-hour smart-factory workload (right).