An Intelligent Runtime Dependability Forecast Based Hybrid Task Scheduling in Cloud

Cloud computing has emerged as a promising solution for meeting the computational objectives of high-performance systems by efficiently ‎scheduling cloud workloads to available resources. However, it becomes problematic to achieve multiple objectives simultaneously, such as ‎optimizing throughput, minimizing makespan, and improving resource utilization for effective Task Scheduling (TS). In response to this ‎issue, the TS, based on many optimization algorithms, was recently developed. These algorithms optimally allocate tasks to appropriate ‎resources to achieve multiple objectives simultaneously. Most of these algorithms enhance the resource usage and the network throughput ‎while minimizing the makespan, but ignore memory and bandwidth consumption. Combining plan-based and backfilling strategies, this ‎article proposes a hybrid TS algorithm that could fulfill numerous criteria for effective TS. Initially, the reliability of the job runtimes is pre-‎dicted using a meta-learner called a Gated Recurrent Unit (GRU) network. Then, the tasks are sorted into two categories: reliable and unreli-‎able. The Dove Optimization Algorithm (DOA) is used for plan-based scheduling of jobs with higher runtime estimates, while the Analytic ‎Hierarchy Process (AHP) method is used to backfill jobs with lower estimated runtimes. In addition, a dynamic allocation of CPU cores is ‎made for backfilling based on the resource limit ratio of reliable jobs to newly requested jobs. Lastly, the simulation results show that the ‎proposed algorithm outperforms the existing TS algorithms. GRU-DOA-AHP reduces makespan by 20.38%, increases throughput by ‎‎30%, and decreases memory usage by up to 26.19% compared to traditional algorithms‎.