Cost-Efficient Scheduling Heuristics for Deadline Constrained Workloads on Hybrid Clouds

Cloud computing offerings are maturing steadily, and their use has found acceptance in both industry and research. Cloud servers are used more and more instead of, or in addition to, local compute and storage infrastructure. Deciding which workloads to outsource to what cloud provider in such a setting, however, is far from trivial. This decision should maximize the utilization of the internal infrastructure and minimize the cost of running the outsourced tasks in the cloud, while taking into account the applications' quality of service constraints. Such decisions are generally hard to take by hand, because there are many cost factors, pricing models and cloud provider offerings to consider. In this work, we tackle this problem by proposing a set of heuristics to cost-efficiently schedule deadline-constrained computational applications on both public cloud providers and private infrastructure. Our heuristics take into account both computational and data transfer costs as well as estimated data transfer times. We evaluate to which extent the different cost factors and workload characteristics influence the cost savings realized by the heuristics and analyze the sensitivity of our results to the accuracy of task runtime estimates.