Home > Research > Publications & Outputs > Combined cloud

Electronic data

  • author accepted version for pure

    Accepted author manuscript, 2.33 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Combined cloud: a mixture of voluntary cloud and reserved instance marketplace

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
  • Wei Shen
  • Wan-chun Dou
  • Fan Wu
  • Shaojie Tang
  • Qiang Ni
Close
<mark>Journal publication date</mark>5/11/2016
<mark>Journal</mark>Journal of Computer Science and Technology
Issue number6
Volume31
Number of pages14
Pages (from-to)1110-1123
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Voluntary cloud is a new paradigm of cloud computing.It provides an alternative selection along with some well-provisioned clouds.However,for the uncertain time span that participants share their computing resources in voluntary cloud,there are some challenging issues,i.e.,fluctuation,under-capacity and low-benefit.In this paper,an architecture is first proposed based on Bittorrent protocol.In this architecture,resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle.Actually,these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud.Then,the fault rate of each node is used to evaluate the uncertainty of its sharing time.By leveraging a linear prediction model,it is enabled by a distribution function which is used for evaluating the computing capacity of the system.Moreover,the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud.At last,the system performance is validated by two sets of simulations.And the experimental results show the effectiveness of our computational method for resource reservation optimization.