Abstract
Cloud computing has been widely adopted, in the forms of public clouds and private clouds, for many benefits, such as availability and cost-efficiency. In this paper, we address the problem of scheduling jobs across multiple clouds, including a private cloud, to optimize cost efficiency explicitly taking into account data privacy. In particular, the problem in this study concerns several factors, such as data privacy of job, varying electricity prices of private cloud, and different billing policies/cycles of public clouds, that most, if not all, existing scheduling algorithms do not ‘collectively’ consider. Hence, we design an ANN-assisted Multi-Cloud Scheduling Recommender (MCSR) framework that consists of a novel scheduling algorithm and an ANN-based recommender. While the former scheduling algorithm can be used to schedule jobs on its own, their output schedules are also used as training data for the latter recommender. The experiments using both real-world Facebook workload data and larger scale synthetic data demonstrate that our ANN-based recommender cost-efficiently schedules jobs respecting privacy.
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Pasdar, A., Hassanzadeh, T., Lee, Y.C., Mans, B. (2020). ANN-Assisted Multi-cloud Scheduling Recommender. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_84
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