2026· IEEE Transactions on Reliability· Vol 75, pp. 2736-2750· 0 citations· 45 references
Computer Science
Abstract
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.
Overall, HFC-GEOA offers a scalable, health-conscious, and fault-tolerant scheduler solution that fully leverages the best latency performance at scale with health-conscious energy usage and stable reliability in heterogeneous fog computing systems.
This research proposes a cost-aware, genetic-based task scheduling algorithm tailored for fog-cloud environments, which seeks to improve cost efficiency for real-time applications with strict deadlines, and demonstrates that the proposed algorithm surpasses existing techniques like Round-Robin and Trade-off algorithms.
Youssef Oukissou, Hamza Elhaou, Driss Ait Omar et al.· 1 citation
A novel hybrid approach combining Bee Colony Optimization and Genetic Algorithm for efficient task scheduling in multi-core processor systems that leverages the global exploration capabilities of BCO and the exploitation strengths of GA to achieve optimal task-to-core assignments while minimizing makespan and balancing...
C. Igiri, Victor Peters, Igu Ajumoke Elizabeth· Journal of Artificial Intell...· 0 citations
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.
This paper proposes a QoS-aware and energy-efficient metaheuristic optimization-based service placement strategy for an integrated IoT and fog computing environment to improve QoS and demonstrates that the developed hybrid algorithm reduces energy consumption by 3.09% and minimize network usage significantly compared w...
Pallavi Mettupalli Venkata, Thatikonda Supraja, Priyanka Chawla et al.· Journal of Supercomputing· 0 citations
Efficient energy scheduling in heterogeneous computing environments is a critical challenge, as task allocation decisions directly affect both energy consumption and execution performance. This work presents an energy aware scheduling framework based on a discretized grasshopper optimization algorithm (GOA), designed t...
Macauley Opuwari, C. Igiri, D. Ikeh· International Journal Of Eng...· 0 citations
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