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Priority-Aware Multi-Objective Task Scheduling in Fog Computing Using Simulated Annealing.

S Sudheer Mangalampalli1, Pillareddy Vamsheedhar Reddy2, Ganesh Reddy Karri3

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Summary
This summary is machine-generated.

A new simulated annealing (SA) framework optimizes task scheduling for the Internet of Multimedia Things (IoMT). This approach enhances fog computing by minimizing latency, energy use, and cost while prioritizing critical tasks.

Keywords:
fog computingmulti-objective optimizationsimulated annealing (SA)task scheduling

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • The rapid growth of Internet of Things (IoT) devices and Internet of Multimedia Things (IoMT) applications strains traditional cloud computing infrastructure due to latency and resource allocation issues.
  • Fog computing offers a decentralized solution to mitigate these challenges by moving computation closer to data sources.

Purpose of the Study:

  • To address the NP-hard problem of multi-objective task scheduling in heterogeneous, resource-constrained fog environments.
  • To develop a novel simulated annealing (SA)-based framework for optimizing task scheduling in fog computing.

Main Methods:

  • Proposed a simulated annealing (SA) algorithm for task scheduling in fog computing environments.
  • Integrated a priority-aware penalty function to enhance responsiveness for high-priority tasks.
  • Optimized for multiple objectives: makespan, energy consumption, and execution cost.

Main Results:

  • The SA-based scheduler demonstrated superior performance compared to ACO, PSO, I-FASC, and M2MPA across various metrics.
  • Achieved significant reductions in makespan, energy consumption, and execution cost.
  • Showcased high reliability and scalability across different task volumes.

Conclusions:

  • The proposed SA-based framework is a scalable and effective solution for intelligent task scheduling in fog-enabled IoT infrastructures.
  • The approach successfully balances multi-objective optimization with priority-sensitive task execution.