Profit and resource availability-constrained optimal handling of high-performance scientific computing tasks

High-performance computing becomes the most important field in various industries and organizations, which needs to be concentrated more today due to their massive resource requirements. It is required to provide more resources to accomplish the high-performance tasks which are very difficult to handle. This problem is resolved in the earlier research method, namely similarity aware high-performance scientific application scheduling. This method would group the similar tasks and provide the optimal resource for the similar kinds of tasks together. However, the existing research method doesn’t focus on the resource similarity where it is complex to identify the proper resource for the task execution based on their characteristics. This would lead to increased time complexity and reduced accuracy in optimal resource allocation. To solve this problem, new framework, namely profit and resource availability aware optimal scheduling, is introduced in this work. In this research method, initially resources that contribute to similar characteristics would be identified by constructing the distance matrix and then learning-based optimal scheduling is done to allocate the proper resources for the incoming tasks accurately. Here, support vector machine is utilized to learn the optimal allocation of tasks and hybrid cuckoo genetic algorithm is implemented for the optimal scheduling process. The implementation is done in the CloudSim environment which concludes the proposed work. The proposed method would ensure optimum results than the existing methods in terms of reduced time complexity and improved accuracy.