This study contributes to the advancement of scheduling algorithms designed to address storage, backup, and recovery challenges within cloud environments. Companies must efficiently store and back up their workflows to ensure operational continuity. Data goes through several stages: it is initially stored on appropriate servers, then backed up, and finally recovered as needed. In cloud environments, these processes are typically managed by the service provider. However, as cloud workflows grow in capacity and the number of customers increases, issues such as data misallocation can arise, leading to elevated costs, poor response times, and increased power consumption. To tackle these challenges, this work proposes several algorithms aimed at not only allocating information to suitable resources but also scheduling the backup process for optimal resource selection. This approach helps prevent resource misuse, minimizes unnecessary data transfers, and facilitates easier information retrieval for customers, all while ensuring data protection and flexibility. The study introduces three algorithms: Mixed-Probabilistic algorithm (MPA), Iterative Mixed-Probabilistic algorithm (IMA), and Reverse-mixed probabilistic algorithm (RMA), and evaluates their performance across five classes of instances. Experimental results indicate that the IMA algorithm outperformed the others in 57.4% of cases, achieving a gap of 0.313 and a processing time of 0.388 s.