An energy-efficient cloud system with novel dynamic resource allocation methods View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-06

AUTHORS

Chao-Tung Yang, Shuo-Tsung Chen, Jung-Chun Liu, Yu-Wei Chan, Chien-Chih Chen, Vinod Kumar Verma

ABSTRACT

In the last decade, cloud computing has brought enormous changes to people’s lives. Cloud computing gives a client-driven computational model. In this case, the pay-per-use model enables a flexible method of deploying and sharing distributed services and resources. Furthermore, computational resources are progressively reassembled and insulated to give various types of services. Current solutions on how to efficiently utilize energy consumption are inadequate. Thus, we propose two methods to tackle the problem, which are the dynamic resource allocation method and the energy saving method, respectively. In this work, we firstly deployed an infrastructure platform based on OpenStack to actualize the proposed approach with live migration of virtual machines (VMs). Secondly, we allocated the dynamic resources and proposed the energy saving algorithm. Thirdly, we monitor the power distribution unit status to record the energy consumption of the system. Finally, in the experiments, we found that the proposed algorithms cannot just accomplish the proficient use of VMs’ resources but also thrift the energy usage of physical machines. Our experimental results demonstrate about 39.89% of energy is saved from 20 VCPUs of 20 GB memory. More... »

PAGES

1-22

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11227-019-02794-w

DOI

http://dx.doi.org/10.1007/s11227-019-02794-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112587589


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