| Grant number: | 14/08607-4 |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| Start date: | July 01, 2014 |
| End date: | June 30, 2015 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computer Systems |
| Principal Investigator: | Edmundo Roberto Mauro Madeira |
| Grantee: | Thiago Augusto Lopes Genez |
| Supervisor: | Rizos Sakellariou |
| Host Institution: | Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| Institution abroad: | University of Manchester, England |
| Associated to the scholarship: | 12/02778-6 - Dynamic Scheduling of Multiple Workflows for SaaS/PaaS Cloud Providers Considering Two SLA Levels, BP.DR |
Abstract The workflow paradigm has become the standard to represent complex scientific (e-Science) problems and their execution flows. With the increasing of the processing demands of these applications upon a massive data sets, more computational demand is being required to execute them. Cloud computing is becoming a financially sustainable computing environment for the owner of a private cloud to run these (or part of) workflow applications. In order to fulfill the application demands, schedulers for such cloud environment have a fundamental role in deciding which types of instances should be leased from a public cloud providers to be joined to the available in-house resources. In the meantime, several applications can be submitted to run at the same time and the scheduler should produce a schedule for each application such that satisfies its demand of quality of service (QoS) requirements. Nevertheless, imprecise information on the input data provided to the scheduler can lead to a misleading schedules and as a result can negatively impact the performance of applications. In this document we propose a detailed study of scheduling algorithms designed to handle workflows on clouds. The aim of this research is the development a dynamic and online algorithm for scheduling multiple workflows concurrently under non-clairvoyant condition, where the available information used as input to the scheduler is uncertain or unknown, e.g., not stationary. Dynamic because the available information of the computational resources are subject to change unpredictably over time, while online due to the workflows can arrive at different time instants and the scheduler should cope with them concurrently. Through algorithms developed in this research, we intend to achieve qualified schedules that ensures the expected performance of the concurrent executions of applications on clouds, regardless of the uncertain information used as input to the scheduler. (AU) | |
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