Hybrid clouds have been an emerging infrastructure model which enables organizations to combine their resource limited private clouds with highly scalable and available public cloud offerings. The hybrid cloud solution promises to mitigate typical utilization problems of private clouds by outsourcing workload peaks quickly and efficiently into the public cloud.
In a scenario of processing workloads that have soft deadlines the use of hybrid clouds introduces the need to determine which workloads are to be outsourced while also keeping the additional cost and impact to workload deadlines to a minimum. This thesis presents and compares multiple heuristics based algorithms designed to solve this scheduling problem. These algorithms are aided by ARIMA based predictions and offer a resource-efficient solution aimed specifically at seasonal workloads with soft global deadlines. An evaluation of the proposed solution is done with real world data and different heuristics for time efficient management of public cloud provisioning are explored. Network traffic and data transfer times between the public and private cloud are not considered in the evaluation.