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Author:

Zhang, Yi (Zhang, Yi.) | Su, Yi Min (Su, Yi Min.)

Indexed by:

EI Scopus

Abstract:

In recent years, with the rapid development of Internet and virtualization technology, cloud computing, which providing users with on-demand services, has become a research hotspot. Under the environment of cloud computing, the datacenter, consisted by hardware and software, is a loosely coupled resource sharing architecture. The existing cloud computing's inadequacies are as following three aspects: 1. For lacking of real adequate and effective transaction of global bidirectional-way selection, the revenue of most of cloud resource provider is too low. 2. Since not fully considering the scheduling of multi-dimensional cloud resources, existing cloud computing's utilization for multi-dimensional cloud resource is too low. 3. Because existing cloud datacenter does not fully consider the energy consumption of communication between the cloud tasks, its energy consumption is too high. Resource scheduling is a major research direction of cloud computing. First, we make a in-depth investigation and analysis of the research status of cloud computing resource scheduling, and then focus on resource scheduling method to reduce the energy consumption of cloud computing data center. Finally we set an important future research direction of cloud computing resource management research in order to provide a useful reference for cloud computing research. © (2014) Trans Tech Publications, Switzerland.

Keyword:

Algorithms Research Cloud computing Energy utilization Scheduling

Author Community:

  • [ 1 ] [Zhang, Yi]Equipment department, Medical College of Qingdao University, No.38 Dengzhou road, Qingdao, China
  • [ 2 ] [Su, Yi Min]College of Art and Design, Beijing University of Technology, Beijing, China

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Source :

ISSN: 1022-6680

Year: 2014

Volume: 926-930

Page: 2050-2053

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 8

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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