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Petroleum reservoir simulation uses computer models to predict fluid flow in porous media, aiding to forecast oil production. Engineers execute numerous simulations with different geological realizations to refine the accuracy of the model. These experiments require considerable computational resources, which are not always available within the on-premises infrastructure. Commercial public cloud platforms can offer many advantages, such as virtually unlimited scalability and payper-use pricing. This paper introduces MSCHEDULER, a meta scheduler framework for reservoir simulations at Petrobras, a Brazilian energy company. It efficiently executes jobs in the cloud, utilizing spot Virtual Machines (VMs) to reduce costs and ensure job completion even with VM termination. Contributions include a novel methodology for reservoir simulation checkpointing, a cost-based scheduler, and an analysis of the strategy using real production jobs from Petrobras., This work was funded by Petroleo Brasileiro S.A. – Petrobras, by Project Universal/CNPq no 404087/2021-3 and CNE/FAPERJ no E-26/201.012/2022(271103). Also by Spanish Ministry of Science (MICINN, AEI, ERDF/FEDER) under grant agreement PID2021-126248OB-I00, MCIN/AEI/10.13039/ 501100011033/FEDER, UE, and Generalitat de Catalunya (AGAUR) 2021-SGR-00478., Peer Reviewed, Postprint (author's final draft)