Cloud computing has attracted scientists to deploy scientific applications by offering services such as Infrastructure-as-a-service (IaaS), Software-as-a-service (SaaS), and Platform-as-a-Service (PaaS). The research community is able to get access to resources on-demand within a short period of time. But, as the demand for cloud resources is dynamic in nature, this affects resource availability during scheduling. Hence, there is a need for efficient management of resources so that tasks can be scheduled based on their execution requirements. To provide a solution, a resource prediction based scheduling approach has been introduced in this paper which automates the resource allocation for scientific applications in a virtualized cloud environment. This research work focuses on the design of an optimized prediction based scheduling approach which maps the tasks of scientific application with the optimal VM by combining the features of swarm intelligence and TOPSIS. The proposed approach minimizes the execution time, cost, and SLA violation rate in comparison to existing scheduling heuristics.

云计算通过提供诸如基础架构即服务（IaaS），软件即服务（SaaS）和平台即服务（PaaS）之类的服务吸引了科学家部署科学应用。研究社区能够在短时间内按需访问资源。但是，由于对云资源的需求本质上是动态的，因此这会影响调度期间的资源可用性。因此，需要对资源进行有效的管理，以便可以基于任务的执行要求来计划任务。为了提供一种解决方案，本文介绍了一种基于资源预测的调度方法，该方法可自动为虚拟化云环境中的科学应用分配资源。这项研究工作集中在基于优化预测的调度方法的设计上，该方法结合了群体智能和TOPSIS的功能，将科学应用的任务与最佳VM进行了映射。与现有的调度试探法相比，该方法可将执行时间，成本和SLA违规率降至最低。