Reliable Task Scheduling in Cloud Computing Using Optimization Techniques for Fault Tolerance

To propose a reliable cloud computing task deployment algorithm for the optimization theory. The current research on cloud computing task deployment mainly only focuses on one of the two goals: reliability and optimization theory. This paper studies how to provide fault tolerance for task execution failure while minimizing the number of servers used to perform all tasks, thus reducing the problem of optimization theory. This article provides fault recovery capability through task replication, providing two instances of each task that make up the job. Task copies can be deployed either on a dedicated backup server or to the server where the main task is located by sharing the same computing resources and running at less than the execution speed of the main task. We propose a reliable cloud computing task deployment algorithm for optimizing theoretical optimization and service quality perception. For users, the completion time of the service is usually limited, and if a timeout occurs, it will cause a loss to the cloud service provider. For the actual completion time performance of the task at the last moment, the algorithm RER is about 2% to 10% more than the algorithm QSRE at xtr = 0.75. Time out times of the algorithm RER (xtr = 0.75). Suppose the task fails at a random time. In that case, the algorithm RER (xtr = 0.75) has a 10% -15% probability over the execution period of the job, and the algorithm RER has a 42% to 63% probability of timeout. The algorithm RER (xtr = 0.5) is 12% to 22% less than the algorithm QSRE. This paper studies how to minimize the number of servers used to perform all task copies while ensuring service quality and providing fault tolerance, thus reducing the problem of optimization theory. © 2024 Slovene Society Informatika. All rights reserved.