چكيده به لاتين
Workflows as tools for automating and optimizing large-scale scientific problems enable the analysis of data in a structured and distributed manner. Their importance is intensified in today's big data era as they become a compelling mean to process and extract knowledge from the ever-growing data produced by increasingly powerful tools such as telescopes and particle accelerators. The scheduling algorithms are key to efficiently automate the execution of these large-scale applications in distributed environments, and as a result, to facilitate and accelerate the pace of scientific progress, especially the e-science.
The emergence of the latest distributed system paradigm, cloud computing, brings with it tremendous opportunities to run workflows at low costs without the need of owning any infrastructure. In particular, Infrastructure as a Service (IaaS) clouds, offer an easily accessible, flexible, and scalable infrastructure for the deployment of these scientific applications by providing access to a virtually infinite pool of resources that can be acquired, configured, and used as needed and are charged on a pay-per-use basis.
This thesis investigates novel resource provisioning and scheduling approaches for scientific workflows in IaaS clouds. In particular, due to the unusual growth of the time needed to produce solutions for larger workflows, in this thesis tried to providing scalable scheduling algorithms from two different perspectives. In the first perspective, the main problem is converted into a new one so that the sum of the computation needed to perform the transformation and solving the new problem grow proportional to the growth of the problem size. In the second perspective which includes two different approaches, by using innovative mechanisms it is tried to only search in the promising limited parts of the search space so that the required computation time grow proportional to the problem size. The results obtained from the evaluations and simulations show that the proposed methods in comparison to others provide better solutions in terms of scalability and the quality of the solutions.
