A learning architecture for scheduling workflow applications in the cloud
MetadataShow full item record
This item's downloads: 794 (view details)
Barrett, E., Howley, E., & Duggan, J. (14-16 Sept. 2011). A learning architecture for scheduling workflow applications in the cloud. Paper presented at the Web Services (ECOWS), 2011 Ninth IEEE European Conference on.
The scheduling of workflow applications involves the mapping of individual workflow tasks to computational resources, based on a range of functional and non-functional quality of service requirements. Workflow applications such as scientific workflows often require extensive computational processing and generate significant amounts of experimental data. The emergence of cloud computing has introduced a utility-type market model, where computational resources of varying capacities can be procured on demand, in a pay-per-use fashion. In workflow based applications dependencies exist amongst tasks which requires the generation of schedules in accordance with defined precedence constraints. These constraints pose a difficult planning problem, where tasks must be scheduled for execution only once all their parent tasks have completed. In general the two most important objectives of workflow schedulers are the minimisation of both cost and make span. The cost of workflow execution consists of both computational costs incurred from processing individual tasks, and data transmission costs. With scientific workflows potentially large amounts of data must be transferred between compute and storage sites. This paper proposes a novel cloud workflow scheduling approach which employs a Markov Decision Process to optimally guide the workflow execution process depending on environmental state. In addition the system employs a genetic algorithm to evolve workflow schedules. The overall architecture is presented, and initial results indicate the potential of this approach for developing viable workflow schedules on the Cloud.
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. Please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.
The following license files are associated with this item:
Showing items related by title, author, creator and subject.
Barrett, Enda (2013-09-30)The advent of on-demand computing facilitated by computational clouds, provides an almost unlimited resource supply to support the execution of applications and processes. Through a process known as virtualisation large ...
Scerri, Simon (2010-10-31)The Social Semantic Desktop adopts Semantic Web technology on the desktop to provide a universal platform for personal - and distributed - information management, social networking and community creation. The social semantic ...
Murray, Clodagh Mary (2012-09-29)This thesis investigated the differences in response variability between children and adolescents with a diagnosis of autism spectrum disorder (ASD) and neurotypical children and adolescents. It also examined ,methods for ...