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<p>Cloud computing infrastructures need effective load balancing to satisfy the competing requirements of energy efficiency and makespan. Conventional methods favor single parameters, resulting in poor resource utilization and scalability. This paper proposes a novel algorithm–WALCLOUD (i.e., a new multi-objective load balancing method inspired by the Walrus Optimization Algorithm (WOA) to reduce energy consumption and makespan while improving resource allocation balance). The algorithm employs an adaptive weighted fitness function that adapts dynamically the energy consumption, task execution rate, resource usage, and workload imbalance. Compared with CBWO, COA, and EBWOA, WALCLOUD optimizes energy use by up to 60%, makespan by 64%, and degree of imbalance by 12-96%. Its scalability in heterogeneous clouds is a pointer to its fitness for large-scale dynamic deployments. Through the incorporation of walrus-inspired exploration-exploitation algorithms, WALCLOUD efficiently searches complex solution spaces, providing a scalable and optimal solution for current cloud infrastructures.</p>

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WALCLOUD: Walrus-inspired algorithm for load balancing in CLOUD computing with energy efficiency and makespan

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Deepa,
  • Mohit Sajwan

摘要

Cloud computing infrastructures need effective load balancing to satisfy the competing requirements of energy efficiency and makespan. Conventional methods favor single parameters, resulting in poor resource utilization and scalability. This paper proposes a novel algorithm–WALCLOUD (i.e., a new multi-objective load balancing method inspired by the Walrus Optimization Algorithm (WOA) to reduce energy consumption and makespan while improving resource allocation balance). The algorithm employs an adaptive weighted fitness function that adapts dynamically the energy consumption, task execution rate, resource usage, and workload imbalance. Compared with CBWO, COA, and EBWOA, WALCLOUD optimizes energy use by up to 60%, makespan by 64%, and degree of imbalance by 12-96%. Its scalability in heterogeneous clouds is a pointer to its fitness for large-scale dynamic deployments. Through the incorporation of walrus-inspired exploration-exploitation algorithms, WALCLOUD efficiently searches complex solution spaces, providing a scalable and optimal solution for current cloud infrastructures.