Artificial Intelligence Enabled Effective Fault Prediction Techniques in Cloud Computing Environment for Improving Resource Optimization

Abstract

The bulk of the fault tolerance techniques that are in use today laid their primary emphasis, in the event that a virtual machine fails, on the production of clones to replace it, rather than on the early prediction of the failure itself in advance. Several of the currently used techniques give migration priority over recovery in the event that a virtual machine (VM) fails. This is due to resource constraints and concerns with server availability. Examples of algorithms with a single objective include fault tolerance, migration prediction, and simply expecting failure. Another example is fault tolerance. In this research, we are aiming to determine the most effective strategy to transition from a system that is not operating well to one that does. It is essential to be able to predict the failure of a virtual machine in a timely manner due of issues such as squandered resources, energy, and cost. Since the beginning of cloud computing, there has been an issue with the dependability of virtual computers, often known as VMs. As an integral component of a fault tolerance system, preemptive measures are an absolute need in order to guarantee the continuation of service. As a consequence of this, it is vital to work toward enhancing and emphasizing the proactive failure prediction of virtual machines. The key motivations for this are decreased periods of downtime and enhanced scalability. A technique was utilized to transfer the resources that were predicted to fail from one virtual machine (VM) to another VM in a safe manner. Using the compression strategy reduced the amount of time needed to complete the migration, and resource utilization increased. This article provides artificial intelligence that enables effective fault prediction techniques in cloud computing to improve resource optimization.