TY  - EJOU
AU  - Chauhan, Nidhika 
AU  - Kaur, Navneet 
AU  - Khan, Jawad 
AU  - Jung, Younhyun 
AU  - Farman, Haleem 
AU  - Sedik, Ahmed 
AU  - Khattak, Sohaib Bin Altaf 

TI  - Hybrid Fuzzy Spark Lion Whale Algorithm for Energy-Efficient Resource Allocation and Task Migration in Cloud Data Centers
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - 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.
KW  - Cloud computing; resource allocation; task migration; fuzzy logic; metaheuristic optimization; energy efficiency; virtual machine placement; deep reinforcement learning; Apache Spark

DO  - 10.32604/cmc.2026.084063