Performance Evaluation Using Execution Time Aware Bio-Inspired Virtual Machine Placement Strategy

In the generation of computing, technology, and communication, cloud computing plays a prominent role. The utility computing paradigm which provides resources as a service is known as cloud computing. Cloud computing provides efficient use of cloud resources using a scalable simulation and modeling process. This work focuses on the performance evaluation of task assignments on virtual machine execution using a bio-inspired strategy. The quality of service is evaluated using average waiting time and average completion time (ms) of tasks assigned to the virtual machines using heavy-duty virtualization. An integrated architecture is proposed which inculcates the features of the bio-inspired technique and Kubernetes-based orchestration of container or lightweight virtual machine. The presented results are compared against static approaches, i.e., time shared and first come first serve techniques.