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Distributed Algorithm with Inherent Intelligence for Multi-cloud Resource Provisioning

Part of the Studies in Computational Intelligence book series (SCI,volume 705)

Abstract

Dynamic distributed algorithm for provisioning of resources has been proposed to support heterogeneous multi-cloud environment. Multi-cloud infrastructure heterogeneity implies the presence of more diverse sets of resources and constraints that aggravate competition among providers. Sigmoidal and logarithmic functions have been used as the utility functions to meet the indicated constraints in the Service Level Agreement (SLA). Spot instances as the elastic tasks can be supported with Logarithmic functions while the algorithm always guaranteed Sigmoidal functions have the priority over the Logarithmic functions. The model uses diverse sets of resources scheduled in a multi-clouds environment by the proposed Ranked Method (RM) in a time window “slice”. To maximize the revenue and diminish cost of services in the pooled aggregated resources of multi-cloud environment, the multi-dimensional self-optimization problem in distributed autonomic computing systems is proposed.

Keywords

  • Cloud computing
  • Scheduler
  • Multi-clouds
  • Federated cloud
  • Spot instances

This work was supported, in part, by Open Cloud Institute at University of Texas at San Antonio, Texas, USA and by Grant number FA8750-15-2-0116 from Air Force Research Laboratory and OSD, USA. The authors gratefully acknowledge use of the services of Chameleon cloud and Jetstream cloud, funded by NSF awards 1419165 and 1445604 respectively.

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Correspondence to Seyed Ali Miraftabzadeh or Paul Rad .

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Miraftabzadeh, S.A., Rad, P., Jamshidi, M. (2017). Distributed Algorithm with Inherent Intelligence for Multi-cloud Resource Provisioning. In: Sangaiah, A., Abraham, A., Siarry, P., Sheng, M. (eds) Intelligent Decision Support Systems for Sustainable Computing. Studies in Computational Intelligence, vol 705. Springer, Cham. https://doi.org/10.1007/978-3-319-53153-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-53153-3_5

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