Multimedia Tools and Applications

, Volume 75, Issue 22, pp 14307–14328 | Cite as

QoS constraints-based energy-efficient model in cloud computing networks for multimedia clinical issues

  • Dingde JiangEmail author
  • Lei Shi
  • Peng Zhang
  • Xiongzi Ge


For many applications of multimedia medical devices in clinical and medical issues, cloud computing becomes a very useful way. However, high energy consumption of cloud computing networks for these applications brings forth a large challenge. This paper studies the energy-efficient problem with QoS constraints in large-scale cloud computing networks. We use the sleeping and rate scaling mechanism to propose a link energy consumption model to characterize the network energy consumption. If there is no traffic on a link, we will let it be sleeping. Otherwise, it is activated and we divide its energy consumption into base energy consumption and traffic energy consumption. The former describes the constant energy consumption that exists when the link runs, while the later, which is a quadratic function with respect to the traffic, indicates the relations between link energy consumption and the traffic on the link. Then considering the relation among network energy consumption, number of active links, and QoS constraints, we build the multi-constrained energy efficient model to overcome the high energy consumption in large-scale cloud computing networks. Finally, we exploit the NSF and GEANT network topology to validate our model. Simulation results show that our approach can significantly improve energy efficiency of cloud computing networks.


Energy efficiency Cloud computing Modeling Constraint optimization Multimedia medical devices 



This work was supported in part by the National Natural Science Foundation of China (Nos. 61571104, 61071124), the General Project of Scientific Research of the Education Department of Liaoning Province (No. L20150174), the Program for New Century Excellent Talents in University (No. NCET-11-0075), the Fundamental Research Funds for the Central Universities (Nos. N120804004, N130504003), and the State Scholarship Fund (201208210013). The authors wish to thank the reviewers for their helpful comments.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.TSSGWaterford Institute of TechnologyWaterfordIreland
  3. 3.Department of Computer Science and EngineeringUniversity of MinnesotaTwin CitiesUSA

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