Enhanced Quantum-Driven Bacterial Colony Optimization for Efficient Load Balancing in Cloud Computing
Keywords:
QuantumComputing,Load Balancing,Bacterial Colony Optimization,Convergence Rate,Swarm Intelligence.Abstract
Effective load balancing is essential in the dynamic and resource-intensive world of cloud computing to guarantee optimal resource utilization, decreased latency, and improved service reliability. An enhanced BCO algorithm based on quantum mechanism (QBCO) is
presented in this study to solve load balancing issues in cloud environments. In the suggested hybrid strategy, the improved search efficiency of quantum-inspired methods like superposition and quantum bit representation is combined with the exploratory and adaptable capabilities of BCO.
References
J. Balicki, "Many-objective quantum-inspired particle swarm optimization algorithm for placement of virtual machines in smart computing cloud," Entropy, vol. 24, no. 1, p. 58, 2021.
S. Janakiraman and M. D. Priya, "Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments," Sustainable Computing: Informatics and Systems, vol. 38, p. 100875, 2023.
