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A novel coordinated resource provisioning approach for cooperative cloud market

Published: 01 December 2017 Publication History

Abstract

Cloud computing has been the enabling technology for shifting mass scale computation and storage requirements from individually owned clients towards an on-demand and utility styled alternative that provides many services. However, cost of maintaining datacenters, keeping the environmental ramifications of data centers at check, providing affordable computation alternative to users still needs to be addressed in a wholesome manner. One of the most exciting and recent research areas in cloud computing has been cloud federations that can mitigate the aforesaid problems. The past decade has seen immense efforts towards interoperability of clouds leading to realistic cloud federations. Motivated by these advancements and equipped with available technologies, this paper presents a detailed account of a cooperative cloud market. It delineates trading mechanisms of such cloud markets, extent of coordination among market players with illustrative examples. It also presents a novel two-phase coordinated resource reservation and provisioning (CRRP) approach that allocates cloud resources to users to meet the goal of minimizing users' cost. To that end, this paper proposes a novel Most Cost Effective Providers' Resources First (MCEPRF) algorithm. The efficiency of the proposed algorithm has been tested using synthetic data and the simulation results presented herein demonstrate the superiority of the proposed approach over its non-coordinated counterparts.

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Published In

cover image Journal of Cloud Computing: Advances, Systems and Applications
Journal of Cloud Computing: Advances, Systems and Applications  Volume 6, Issue 1
December 2017
524 pages
ISSN:2192-113X
EISSN:2192-113X
Issue’s Table of Contents

Publisher

Hindawi Limited

London, United Kingdom

Publication History

Published: 01 December 2017

Author Tags

  1. Cloud computing
  2. Cloud market
  3. Coordinated resource provisioning
  4. Cost optimization

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