The fault tolerance method of virtual machines (VM) guarantees reliability to the service capability of cloud platforms. VM workloads are dynamic and uncertain, and thus, they affect the reliability and task processing capability of entire cloud platforms. In this study, a fault tolerance method based on the VM workload consolidation model was proposed to solve problems concerning the reliability and task processing capability of cloud platforms caused by VM workloads, thus improving the reliability of VMs and overall performance of cloud platforms. First, the method was analyzed on the basis of the distinct relationship of VM workload and VM reliability and task processing capability. Then, the workload state of VM was predicted and analyzed by linear regression using VM workload monitoring data, and the VM workload consolidation algorithm was constructed based on expected workload constraint and optimization of fault tolerance time. Finally, the fault tolerance method based on the VM workload consolidation model was compared with the Radom method and the Max method. Research results demonstrate the potential of the proposed method to improve VM reliability in cloud platforms by 20% and 47% compared with those for the Radom and Max methods, respectively. In the same workload phase, the task completion rate of the proposed method increased significantly (15% and 30%, and 22% and 30%), and the percentages were higher than those for the Radom and Max methods, respectively. Moreover, the proposed method shortened task response time. This study concludes that the workload consolidation of VMs can increase the reliability and task processing capability of VMs. This proposed method can provide technological support to the fault tolerance of VMs in cloud platforms.

Virtual machine (VM) availability awareness is a key task scheduling technology in a cloud platform. However, the dynamic change and uncertainty of VM availability constitute difficulties for task scheduling, and quality requirements of task services cannot be satisfied and thus seriously affect the task scheduling capacity of the cloud platform. A task scheduling algorithm based on VM availability awareness was proposed to solve the unmatching problem between VM availability and quality of service (QoS) to improve task scheduling capacity of VMs. This algorithm combined available task processing capacities of VMs and task requirement features to establish a differential entropy model between VM availability and task availability requirement. Task availability matching and scheduling was realized through the principle of maximum entropy, and task scheduling was optimized from the aspect of balance of server workloads. Finally, a comparative verification between the task scheduling algorithm and Random and Minmin algorithms was implemented. Results demonstrate that through the task scheduling algorithm based on VM availability awareness, the task execution speed of VMs is higher than those of Random and Minmin algorithms by 28% and 6%, respectively. Therefore, task execution speed of the cloud platform is significantly elevated, and server workloads are more balanced than those in Random and Minmin algorithms. Within the same time, QoS satisfaction rate is higher than those of Random and Minmin algorithms by 10% and 2%, respectively. QoS task requirement is satisfied while task completion time is reduced. This study concludes that the VM availability awareness method satisfies the task requirements and improves task processing performance of the VM in the cloud platform. Relevant conclusions can provide technical support for task scheduling of the VM in the cloud platform.