International Business Machines Corporation

Anton Zorin

Sameep Mehta

Ritesh Kumar Gupta

Manish Kesarwani

Niels Dominic Pardon

Abderrahim Labbi

Embodiments for identifying an optimal cloud computing environment for a computing task is disclosed. Embodiments comprises receiving a computing task to be executed in a cloud computing environment, wherein the computing task requires a set of cloud computing environment parameter values of the cloud computing environment, pre-selecting a set of candidate cloud computing environments, each of which meets the set of cloud computing environment parameter values, ranking the candidate cloud computing environments using reward-based ranking parameter values of the candidate cloud computing environments as an additional selection constraint, and selecting the highest ranking cloud computing environment as the optimal cloud computing environment for the computing task. Furthermore, embodiments comprise executing the computing task in the optimal cloud computing environment, monitoring execution when executing the computing task, and updating parameter values of the reward-based ranking for the selected optimal cloud computing environment.

Selecting best cloud computing environment in a hybrid cloud scenario

The method performs at the orchestration interface at which update information, including changes to tasks of a workflow, is received from a task manager system (TMS), where the workflow includes a set of tasks, inputs to the tasks, and outputs from the tasks. The inputs and outputs determine runtime dependencies between the tasks. Based on the update information received, the orchestration interface populates a topology of nodes and edges as a directed acyclic graph (DAG) that maps nodes to tasks and edges to runtime dependencies between tasks, based on node inputs and outputs. The orchestration interface instructs the execution of the tasks and handling dependencies by interacting with a task execution system (TES) and by traversing the DAG, the orchestration interface identifies tasks that depend on completed tasks as per the runtime dependencies and instructs the TES to execute the dependent tasks identified.

Automatically orchestrating a computerized workflow

The exemplary embodiments disclose a system and method, a computer program product, and a computer system. The exemplary embodiments may include receiving a data analysis request, using a knowledge graph for determining a source dataset based on the received data analysis request, wherein the knowledge graph represents an extract, transform and load (ETL) based ontology, wherein the knowledge graph comprises nodes representing entities and edges representing relationships between the entities, and wherein the entities are instances of concepts of the ETL based ontology, building an ETL workflow for processing the source dataset in accordance with a data lineage associated with the source dataset in the knowledge graph, and executing the ETL workflow.

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Waedenswil, Switzerland

Anton Zorin