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Optimizing Task Scheduling in Fog Computing with Deadline Awareness

Mohammad Sadegh Sirjani, Mohammad Ahmad, Somayeh Sobati-Moghadam

TL;DR

The paper addresses the challenge of deadline-aware, energy-efficient task scheduling in IoT–fog environments, where scheduling is NP-hard. It introduces RIGEO, a hybrid framework that classifies fog nodes into low- and high-traffic groups and routes short-deadline tasks to low-traffic nodes using IGEO while directing long-deadline tasks to high-traffic nodes via reinforcement learning, all within a three-layer IoT–Fog–Cloud architecture. The approach is formalized with problem definitions for response time, deadline violations, and energy consumption, and evaluated through MATLAB simulations showing up to 29% energy savings, 86% faster response times, and 19% fewer deadline violations compared with state-of-the-art methods. The work advances practical, adaptive edge computing by improving QoS and energy efficiency in dynamic, heterogeneous fog environments.

Abstract

The rise of Internet of Things (IoT) devices has led to the development of numerous time-sensitive applications that require quick responses and low latency. Fog computing has emerged as a solution for processing these IoT applications, but it faces challenges such as resource allocation and job scheduling. Therefore, it is crucial to determine how to assign and schedule tasks on Fog nodes. This work aims to schedule tasks in IoT while minimizing the total energy consumption of nodes and enhancing the Quality of Service (QoS) requirements of IoT tasks, taking into account task deadlines. This paper classifies Fog nodes into two categories based on their traffic level: low and high. It schedules short-deadline tasks on low-traffic nodes using an Improved Golden Eagle Optimization (IGEO) algorithm, an enhancement that utilizes genetic operators for discretization. Long-deadline tasks are processed on high-traffic nodes using reinforcement learning (RL). This combined approach is called the Reinforcement Improved Golden Eagle Optimization (RIGEO) algorithm. Experimental results demonstrate that RIGEO achieves up to a 29% reduction in energy consumption, up to an 86% improvement in response time, and up to a 19% reduction in deadline violations compared to state-of-the-art algorithms.

Optimizing Task Scheduling in Fog Computing with Deadline Awareness

TL;DR

The paper addresses the challenge of deadline-aware, energy-efficient task scheduling in IoT–fog environments, where scheduling is NP-hard. It introduces RIGEO, a hybrid framework that classifies fog nodes into low- and high-traffic groups and routes short-deadline tasks to low-traffic nodes using IGEO while directing long-deadline tasks to high-traffic nodes via reinforcement learning, all within a three-layer IoT–Fog–Cloud architecture. The approach is formalized with problem definitions for response time, deadline violations, and energy consumption, and evaluated through MATLAB simulations showing up to 29% energy savings, 86% faster response times, and 19% fewer deadline violations compared with state-of-the-art methods. The work advances practical, adaptive edge computing by improving QoS and energy efficiency in dynamic, heterogeneous fog environments.

Abstract

The rise of Internet of Things (IoT) devices has led to the development of numerous time-sensitive applications that require quick responses and low latency. Fog computing has emerged as a solution for processing these IoT applications, but it faces challenges such as resource allocation and job scheduling. Therefore, it is crucial to determine how to assign and schedule tasks on Fog nodes. This work aims to schedule tasks in IoT while minimizing the total energy consumption of nodes and enhancing the Quality of Service (QoS) requirements of IoT tasks, taking into account task deadlines. This paper classifies Fog nodes into two categories based on their traffic level: low and high. It schedules short-deadline tasks on low-traffic nodes using an Improved Golden Eagle Optimization (IGEO) algorithm, an enhancement that utilizes genetic operators for discretization. Long-deadline tasks are processed on high-traffic nodes using reinforcement learning (RL). This combined approach is called the Reinforcement Improved Golden Eagle Optimization (RIGEO) algorithm. Experimental results demonstrate that RIGEO achieves up to a 29% reduction in energy consumption, up to an 86% improvement in response time, and up to a 19% reduction in deadline violations compared to state-of-the-art algorithms.

Paper Structure

This paper contains 20 sections, 10 equations, 6 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Energy Efficiency
  3. Deadline Constraints
  4. Latency Reduction
  5. Cost Optimization
  6. Reliability and Fault Tolerance
  7. Methodology
  8. Problem Statement
  9. Response time
  10. Deadline violation
  11. Energy Consumption
  12. Proposed Method (RIGEO)
  13. Reinforcement Learning
  14. Golden Eagle Optimization (GEO)
  15. Improved Golden Eagle Optimization (IGEO)
  16. ...and 5 more sections

Figures (6)

  • Figure 1: The architecture of the IoT-Fog-Cloud network.
  • Figure 2: Mutation operator
  • Figure 3: Crossover operation. (a) One-point crossover. (b) Two-point crossover
  • Figure 4: Average Total Energy Consumption (Joules) with varying task numbers (200-600) and 20 FNs over 50 runs
  • Figure 5: Average Deadline Violation Time (ms) with varying task numbers (200-600) and 20 FNs over 50 runs
  • ...and 1 more figures