A MODIFIED ANT COLONY OPTIMIZATION PLACEMENT TECHNIQUE FOR CONTAINERS IN CLOUD COMPUTING WORLD

Abstract

The container is an evolving lightweight virtualization innovation. Placement of containers at the appropriate platform is essential in the utilization optimization of resources in cloud infrastructures. The ant colony optimization technique was used to schedule tasks and containers on VMs and PMs in the cloud. This thesis proposes a Modified Ant Colony Optimization technique (MACO) for the placement of containers. The new proposal takes into consideration the scheduling history by tracking the load on each VM before choosing it to hold the container to enhance the scheduling decision. The experimental results show that the MACO is better than FCFS and the basic ACO in terms of response time and throughput. we also apply MACO using two different real workloads (Alibaba real workload, planetlab real workload) we note that in case of using planetlab real workload the MACO response time is improved by 90% and 80% for FCFS and traditional ACO approach, respectively. In case of using Alibaba as a real workload, the percentage of the improvement in response time using MACO algorithm is 75% and 25% for FCFS and traditional ACO approach, respectively.