Computing and Information Systems - Theses
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ItemResource provisioning and scheduling algorithms for scientific workflows in cloud computing environments( 2016)Scientific workflows describe a series of computations that enable the analysis of data in a structured and distributed manner. Their importance is exacerbated in todays 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, particle accelerators, and gravitational wave detectors. Due to their large-scale nature, scheduling algorithms are key to efficiently automate their execution in distributed environments, and as a result, to facilitate and accelerate the pace of scientific progress. 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. They address fundamental challenges that arise from the multi-tenant, resource-abundant, and elastic resource model and are capable of fulfilling a set of quality of service requirements expressed in terms of execution time and cost. It advances the field by making the following key contributions: 1. A taxonomy and survey of the state-of-the-art scientific workflow scheduling algorithms designed exclusively for IaaS clouds. 2. A novel static scheduling algorithm that leverages Particle Swarm Optimization to generate a workflow execution and resource provisioning plan that minimizes the infrastructure cost while meeting a deadline constraint. 3. A hybrid algorithm based on a variation of the Unbounded Knapsack Problem that finds a trade-off between making static decisions to find better-quality schedules and dynamic decisions to adapt to unexpected delays. 4. A scalable algorithm that combines heuristics and two different Integer Programming models to generate schedules that minimize the execution time of the work- flow while meeting a budget constraint. 5. The implementation of a cloud resource management module and its integration to an existing Workflow Management System.