Scientific workflows are used to process large amounts of data and perform complex analyses; thus, they require powerful computing resources to produce the desired results in an acceptable time and at reasonable costs. For this purpose, distributed resources such as cloud computing, with access to virtualized, infinite, and elastic resources are used to execute the workflows. For mapping tasks to computational resources, the problem must be modeled as a scheduling problem. The algorithm presented in this research is a hybrid algorithm based on a mathematical model called MHPSLP that performs the scheduling problem by breaking the problem into smaller subsets including scheduling bags of tasks, providing resources using an mixed integer linear mathematical (MILP) model. The benefit of this method against compared

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