Autonomic scheduling of deadline-constrained bag of tasks in hybrid clouds

The use of Hybrid Cloud technologies in large scale applications allows organizations to complement on-premises infrastructure with hired infrastructure from Public Cloud providers. The efficient use of the hired resources to provide the expected quality of service while dealing with the heterogeneity and uncertainty of Public Clouds is the main difficulty. A scheduler able to deal with deadline-constrained bag of tasks in Hybrid Clouds is presented in this work. The main contribution of this scheduler is that task runtime estimations are not necessary as inputs. The scheduler includes a runtime estimator based on sampled data to generate the estimation autonomously. A discrete event simulator was developed in order to validate the proposed scheduler in different scenarios. Results show that an estimator based on the Chebyshev's inequality obtains very good results in terms of deadlines met and cost.