Parallel Ant Colony Optimization Algorithm for Efficient Cloud Computing Container Allocation

Containers have completely changed how modern applications are designed and delivered by offering anextremely modular, scalable, and efficient environment. Scheduling and allocating resources—like CPU,memory, and network bandwidth—into containerized cloud computing environments is a challengingoperation. Optimization strategies mitigate these issues by guaranteeing optimal resource utilization, loweringoperational expenses, and improving overall system performance. A popular swarm intelligence technique foraddressing optimization issues is Ant Colony Optimization (ACO), which is motivated by the social dynamicsof ant colonies. Resource utilization in container cloud computing environments should be made efficientwhile maintaining high performance and scalability by combining the advantages of parallel computing withthe strengths of ACO. To increase efficiency when dealing with big and complex issue cases, parallel computingtechniques are typically used. This enables ACO strategies to produce excellent results in realistic executiontimes, while taking on challenging optimization problems among the utilization of containers, virtual machines,and hosts. This paper explores the use of the Parallel Ant Colony Optimization (PACO) technique for containersin a cloud computing environment to improve load balancing, optimize resource allocation, and achieve higherlevels of scalability and energy efficiency. The suggested method shows great promise for streamlining cloudoperations through the use of parallelism, a decrease in computational overhead, and an enhancement inoverall system performance. The experimental findings demonstrate that the proposed method outperformsthe MACO and FCFS algorithms in terms of execution time and throughput. Also, it keeps aconsiderable improvement in resource utilization between virtual and physical computers.