TL;DR
This work aims to design an efficient framework by incorporating a novel hybrid metaheuristic algorithm that combines Draco Lizard Optimization (DLO) and Sand Cat Optimization (SCO) for optimal task offloading and resource allocation for IoT applications.
The rapid advancement of edge computing has transformed the distributed computing paradigm. Edge computing enables storage and computation to perform at the network edge. For optimized performance, offloading has a crucial role that enhances the system efficacy by improving the quality of service (QoS) parameters. Therefore, this work proposes an Artificial Rabbit Optimization (ARO)-based framework for efficient task offloading-based allocation of resources in edge computing environments. The proposed technique improves the performance of Internet of Things (IoT) applications by using an intelligent task execution strategy that diminishes energy consumption, delay, and cost. A multi-objective function is framed that considers the above performance metrics and optimizes performance subject to delay and power constraints. The experimental results demonstrate that the proposed framework outperforms the benchmark approaches, reducing delay by up to 44.06%, energy consumption by up to $\mathbf{3 8. 9 6 \%}$, and cost by up to $\mathbf{2 8. 4 8} \boldsymbol{\%}$.
An energy-and fairness-aware task offloading (EFATO) scheme through a GA to achieve the optimal task scheduling and loading within multiple edge servers and a novel fitness function is proposed that combines energy cost, computational delay and fairness index.
A distributed Multi-stage Adaptive Deferred Acceptance (MA-DA) algorithm is proposed that enables a stable and Pareto-optimal assignment of tasks to edge computing nodes (ECNs) and determines a reasonable task execution sequence and ensures the prioritized completion of delay-sensitive tasks.
Ensuring reliability and real-time performance of task offloading in fog computing remains a critical challenge. To address this, this article considers a dual-objective optimization problem of reliability and execution time for task offloading in energy-constrained fog computing. We first propose a more realistic fog computing system model that incorporates Rayleigh fading. Second, we introduce a reliability and time balanced Pareto ant colony optimization algorithm (RTPACO) based on the Pareto ant colony optimization (PACO algorithm. This algorithm is specifically designed for task offloading scenarios in fog computing. Lastly, we compared RTPACO with other multiobjective optimization algorithms using several metrics, including convergence and diversity (evenness and spread). To evaluate the performance of the algorithms, we employed the widely-used Hypervolume (HV) metric. The experimental results demonstrate that RTPACO consistently achieves a superior Pareto front, with HV improvements ranging from 17.2% to 50.2% compared to existing algorithms.
The findings reveal that the proposed framework outperforms the methods compared by achieving 22.5% lower latency and 23% lower energy consumption.
The proposed RSO–SFO framework integrates both strategies within a unified fitness function designed to minimize deployment cost while ensuring efficient allocation of fog resources, confirming the effectiveness and robustness of the proposed hybrid strategy for optimal service placement in fog-based IoT environments.