@Article{cmc.2026.084063,
AUTHOR = {Nidhika Chauhan, Navneet Kaur, Jawad Khan, Younhyun Jung, Haleem Farman, Ahmed Sedik, Sohaib Bin Altaf Khattak},
TITLE = {Hybrid Fuzzy Spark Lion Whale Algorithm for Energy-Efficient Resource Allocation and Task Migration in Cloud Data Centers},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27940},
ISSN = {1546-2226},
ABSTRACT = {The exponential growth of cloud data centers necessitates highly efficient resource allocation and task migration strategies. However, multi-dimensional memory fragmentation severely limits the efficacy of standard scheduling algorithms under heavy-tailed, real-world workloads. This paper proposes Fuzzy-SLW, a hybrid swarm-intelligence architecture that integrates a Mamdani fuzzy-inference pre-filter with a distributed Spark Lion-Whale Optimization (SLWO) core via Apache Spark. The fuzzy pre-filter mathematically prunes the search space using non-compressible hardware constraints, while the Spark execution model resolves the traditional serial bottleneck of swarm intelligence. Evaluated within a discrete-event environment utilizing the Google Cluster Trace (2019), Fuzzy-SLW demonstrates a greater than 240% relative improvement (+42.2 percentage points) in virtual machine utilization over load-scattering metaheuristics and avoids the premature policy convergence observed in Deep-DQN baselines. For large-population offline optimization configurations (<mml:math id="mml-ieqn-1"><mml:mi>P</mml:mi><mml:mo>≥</mml:mo><mml:mn>5000</mml:mn></mml:math> individuals), the distributed architecture achieves a 5.85 times sub-linear Amdahl speedup; below this population threshold, including the <mml:math id="mml-ieqn-2"><mml:mi>P</mml:mi><mml:mo>=</mml:mo><mml:mn>20</mml:mn></mml:math> configuration used for online, per-task scheduling, thread-pool context-switching overhead dominates and distributed partitioning does not improve wall-clock latency. The results empirically quantify the necessary tradeoff between aggressive hardware consolidation and Service Level Agreement preservation, establishing Fuzzy-SLW as a scalable solution for power-constrained hyper-scale environments.},
DOI = {10.32604/cmc.2026.084063}
}