Prediction Based Energ -Aware Fault- Tolerant Scheduling Algorithm for Bigdata Cloud Data Center

Resumen

Cloud data centers provide a wide range of facilities to end-users in different domains such as health care, scientific computing, smart grid, e-commerce, and nuclear science. The task and resource failures are inevitable due to the growing number of CDC resources, which provide Bigdata infrastructures. However, reliable on-demand resources are needed for service providers to fulfill the service level agreement of customers. Therefore, it is of great significance to ensure the reliability and availability of such systems. In this Project, our aim is to predict a task failure according to the requested resources before the actual failure occurs, and leverage the prediction to design a task scheduling scheme, thus reducing the task execution failure and total energy consumption. To this end, a normal scheduling algorithm AI-driven Prediction based Energy-aware Fault-tolerant Scheduling scheme (PEFS) is proposed. The existing system fault-tolerant techniques in CDCs include replication, checkpoint, job migration, retry, task resubmission, etc. Some studies introduced methods based on certain principles, such as retry, resubmission, replication, renovation of software, screening, and migration, to harmonize the fault-tolerant mechanism with CDC task scheduling. However, for parallel and distributed computing systems, the most widely adopted and acknowledged method is to replicate data to multiple hosts. In the proposed scheme can intelligently predict task failure and achieves better fault tolerance and reduces total energy consumption better than the existing schemes. AI-driven Prediction based Energy-aware Fault-tolerant Scheduling scheme (PEFS) is proposed the prediction of the possibility of failure of incoming tasks, so that further scheduling strategy can be developed based on the prediction