A Hybrid Approach of BBO and PSO for Resource Management in Fog Computing

Efficient resource management is crucial for fog computing to achieve optimal performance and resource utilization over different cloud nodes. In order to overcome this issue, a hybrid BBO-PSO has been proposed. Inspired by species migration, BBO treats fog nodes as habitats with suitability indices specifying the performance of each in terms to tasks and adapt inferior convergence schedules dynamically based on resource load without locality-awareness while minimizing latency through migration and mutation ha1933b. The resulting system is akin to the PSO, which uses swarm intelligence for resource configuration (e.g., particles in a nearby cluster), and adjusts their position via historical information as well global best-known placements based on performance metrics such as increased resource utilization, reduced request latency and energy efficiency. This paper takes a hybrid approach by directly integrating these techniques through exchanging suitability indices and best-known positions, thereby combining the exploration ability of BBO with the targeted search capability of PSO. Migration and mutation techniques are optimized to cope with adapting mechanisms changes in environments thus accommodates workload patterns as well network conditions. This BBO-PSO hybrid approach holds significant promise for improving fog computing's scalability and robustness.