As the size of the cloud-based applications and its tasks are increasing exponentially, it is necessary to estimate the load balancing metrics in the real-time cloud computing environments. Hybrid load balancing framework play a vital role in the cloud-based applications and tasks monitoring and resource allocation. Most of the conventional load balancing metrics are dependent on limited number of cloud metrics and type of virtual machines. Also, these models require high computational memory and time on large number of tasks. In this paper, an advanced multi-level statistical load balancer-based parameters estimation model is designed and implemented on the real-time cloud computing environment. In this model, a novel statistical load balancing data collector is used to find the best metrics for the load balance computation. In this model, different types of tasks are simulated under different virtual machine types such as small, medium and large instances. Experimental results show that the proposed multi-level based statistical load balancing collector has better efficiency than the conventional models in terms of memory utilization, CPU utilization, runtime and reliability are concerned.
Real-time Roadmap Generation and Updating Method between Heterogeneous Navigation Systems for Unknown Roads in Cloud Computing Environment