Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds

Cloud computing has found broad acceptance in both industry and research, with public cloud offerings now often used in conjunction with privately owned infrastructure. Technical aspects such as the impact of network latency, bandwidth constraints, data confidentiality and security, as well as economic aspects such as sunk costs and price uncertainty are key drivers towards the adoption of such a hybrid cloud model. The use of hybrid clouds introduces the need to determine which workloads are to be outsourced, and to what cloud provider. These decisions should minimize the cost of running a partition of the total workload on one or multiple public cloud providers while taking into account the application requirements such as deadline constraints and data requirements. The variety of cost factors, pricing models and cloud provider offerings to consider, further calls for an automated scheduling approach in hybrid clouds. In this work, we tackle this problem by proposing a set of algorithms to cost-efficiently schedule the deadline-constrained bag-of-tasks applications on both public cloud providers and private infrastructure. Our algorithms take into account both computational and data transfer costs as well as network bandwidth constraints. We evaluate their performance in a realistic setting with respect to cost savings, deadlines met and computational efficiency, and investigate the impact of errors in runtime estimates on these performance metrics.

► This contribution focuses on scheduling algorithms for deadline-based workloads in a hybrid cloud setting.
► Computational and data transfer costs as well as data transfer times are considered.
► We propose cost-aware scheduling algorithms, which manage to reduce costs and are shown to be resistant to inaccuracies in the user provided runtimes.