ANN-assisted multi-cloud scheduling recommender

Amirmohammad Pasdar*, Tahereh Hassanzadeh, Young Choon Lee, Bernard Mans

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution


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.

Original languageEnglish
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages9
ISBN (Print)9783030638191
Publication statusPublished - 2020
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: 18 Nov 202022 Nov 2020

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference27th International Conference on Neural Information Processing, ICONIP 2020


  • Cloud computing
  • Recommender systems
  • Scheduling

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