Data-intensive and long-lasting applications running in the form of workflows are being increasingly dispatched to cloud computing systems. Current scheduling approaches for graphs of dependencies fail to deliver high resource efficiency while keeping computation costs low, especially for continuous data processing workflows, where the scheduler does not perform any reasoning about the impact new input data may have in the workflow final output. To face such a challenge, we introduce a new scheduling criterion, Quality-of-Data (QoD), which describes the requirements about the data that are worthy of the triggering of tasks in workflows. Based on the QoD notion, we propose a novel service-oriented scheduler planner, for continuous data processing workflows, that is capable of enforcing QoD constraints and guide the scheduling to attain resource efficiency, overall controlled performance, and task prioritization. To contrast the advantages of our scheduling model against others, we developed WaaS (Workflow-as-aService), a workflow coordinator system for the Cloud where data is shared among tasks via cloud columnar database.
A service-oriented scheduler planner was developed in REF to get resource efficiency, overall controlled performance and task prioritization for continuous data processing workflows.
WaaS: Workflow-as-a-Service for the Cloud with Scheduling of Continuous and Data-Intensive Workflows
Comput. J.
Comput. J.
Computer Science