SIREN: Multiobjective Game-Theoretic Scheduler Based on Memory-Driven Gray Wolf Optimization in Fog–Cloud Computing

Abolfazl Younesi

doi:10.1109/JIOT.2026.3666558

SIREN: Multiobjective Game-Theoretic Scheduler Based on Memory-Driven Gray Wolf Optimization in Fog–Cloud Computing

2026-02-20

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DOI：10.1109/JIOT.2026.3666558

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Fog–cloud task scheduling faces the dual challenge of maintaining critical IoT workloads despite node failures while adhering to strict energy budgets. We present SIREN, a game-theoretic framework that treats fog nodes as strategic players, embedding reliability benefits and dynamic voltage and frequency scaling (DVFS)-aware energy costs directly into their payoffs. By searching the joint strategy space with a memory-driven gray wolf optimizer (MDGWO), SIREN adapts placements, selective replication, and frequency settings to workload dynamics. Extensive evaluations on the Alibaba 2018 and Google 2011 cluster traces and on a latency-critical healthcare application demonstrate that SIREN converges to near-Nash schedules that minimize energy while maximizing reliability. Results confirm that SIREN delivers: 1) 100% task success rates (TSRs) in critical healthcare scenarios; 2) $2.08\times $ – $4.24\times $ lower worst case energy consumption than leading baselines; and 3) a $3.9\times $ – $5.8\times $ reduction in network usage, establishing a new benchmark for resilient, energy-efficient fog computing.

Keywords:

Energy management

fault tolerance

fog computing

game theory

gray wolf optimizer

IoT

scheduling

SIREN: Multiobjective Game-Theoretic Scheduler Based on Memory-Driven Gray Wolf Optimization in Fog–Cloud Computing

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