Batch Workflow Scheduling Optimization in Cloud Resource Environment

The constantly evolving cloud ecosystems are promising environments for the execution of science-intensive applications within the framework of the new concept of Workflow-as-a-Service (WaaS). WaaS platforms, being the so-called multitenant environments, provide efficient mechanisms for managing continuous and heterogeneous job-flows in cloud computing. However, in this scenario, along with the complexity of scheduling composite workflows, an additional problem arises to efficiently manage the cloud resources, that is, to determine the start and shutdown times of virtual resources, considering the available economic policy. To handle this problem, we propose a modification to the critical jobs method which has been the basis of the Systems Design course in the Department of Computing Technologies for over 15 years. The resulting solution combines several heuristics to optimize the cloud resource management for WaaS platforms and largely determined the successful completion of RSF project. Some experiments with the real-world workflows prove the optimization efficiency of the proposed approach.