Hybrid metaheuristic technique for optimal container resource allocation in cloud

Resource allocation in cloud is becoming more challenging and complex due to the intensifying requirements of cloud services. Efficient management of virtual resources in the cloud is of enormous importance, as it has a large impact on both the operational cost and scalability of the cloud's surroundings. However, one of the most difficult aspects of cloud computing is optimal resource allocation. The resource allocation is done using the objective of reducing the costs connected with it. Hence, an attempt is made to optimize the resource allocation of containers using a new Combined Spider and Honey Bee Optimization (CS-HBO). In the proposed model, the optimal allocation relies on certain constraints like Total Network Distance (TND), System Failure, Balanced Cluster Use, and threshold distance. At last, the supremacy of the presented approach is examined over prevailing techniques regarding various metrics such as cost and convergence analysis. Accordingly, for cost analysis, the proposed method attains the least cost, which is 65.57%, 4.15%, 1.04%, 6.87% and 6.76% improved than existing Marriage in Honey Bee optimization (MHBO), Spider Monkey Optimization (SMO), Whale Random update assisted Lion Algorithm (WR-LA), Genetic Algorithms (GA), and Accelerated particle swarm optimization (APSO) models at the 100th iteration.

Automated summary

This study improves the allocation of cloud container resources by using Combined Spider and Honey Bee Optimization (CS-HBO) method. The comparison of various metrics demonstrates the significant advantage of this method in terms of cost.