Authors:
Eric Schulte-Zurhausen
and
Mirjam Minor
Affiliation:
Goethe University, Germany
Keyword(s):
Workflow, Cloud Management, Task Placement.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Process Management
;
Cloud Computing
;
e-Business
;
Enterprise Engineering
;
Enterprise Information Systems
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Service Monitoring and Control
;
Services Science
;
Symbolic Systems
Abstract:
Moving workflow management to the cloud raises novel, exciting opportunities for rapid scalability of workflow execution. Instead of running a fixed number of workflow engines on an invariant cluster of physical machines, both physical and virtual resources can be scaled rapidly. Furthermore, the actual state of the resources gained from cloud monitoring tools can be used to schedule workload, migrate workload or conduct split and join operations for workload at run time. However, having so many options for distributing workload forms a computationally complex configuration problem which we call the task placement problem. In this paper, we present a case-based framework addressing the task placement problem by interleaving workflow management and cloud management. In addition to traditional workflow and cloud management operations it provides a set of task internal operations for workload distribution.