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Open Access

ARTICLE

Hybrid Fuzzy Spark Lion Whale Algorithm for Energy-Efficient Resource Allocation and Task Migration in Cloud Data Centers

Nidhika Chauhan1,*, Navneet Kaur1, Jawad Khan2, Younhyun Jung2, Haleem Farman3, Ahmed Sedik3,4, Sohaib Bin Altaf Khattak3
1 Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India
2 School of Computing, Gachon University, Seongnam, Republic of Korea
3 Smart Systems Engineering Laboratory, College of Engineering, Prince Sultan University, Riyadh, Saudi Arabia
4 Department of Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
* Corresponding Author: Nidhika Chauhan. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.084063

Received 17 April 2026; Accepted 24 July 2026; Published online 14 August 2026

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 (P≥5000 individuals), the distributed architecture achieves a 5.85 times sub-linear Amdahl speedup; below this population threshold, including the P=20 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.

Keywords

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

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