Loading...
Send to Friend
 Bookmark
 Review List
  1. A hybrid method for reduction of energy consumption in cloud networks

A hybrid method for reduction of energy consumption in cloud networks

Tarahomi, M ; Sharif University of Technology | 2018

574 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/CSICSSE.2017.8320143
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2018
  4. Abstract:
  5. Cloud computing consists of physical data centers each of which including hundreds or thousands of computers. A key enabler technology is virtualization. The Virtualization allows us to separate virtual machines in a way that each of these so-called virtualized machines can be configured with a number of hosts according to the type of application of the user. It is also possible to dynamically alter the allocated resources of a virtual machine. One of the most important challenges in terms of cloud computing is to maintain an optimized load balance in cloud environment on servers because this issue is critical for energy consumption. In this paper, we propose a hybrid method for reduction of energy consumption in cloud networks which enables us to save energy through finding a suitable configuration for placement of virtual machines and through being aware of special features of virtual environments for scheduling and balancing their dynamic loads by the help of live migration method. The experimental results show that the proposed method reduced power consumption when comparing with other algorithms. © 2017 IEEE
  6. Keywords:
  7. Cloud network ; Energy saving ; Live migration ; Load balancing ; Cloud computing ; Dynamic loads ; Energy conservation ; Energy utilization ; Network security ; Resource allocation ; Scheduling ; Virtual machine ; Virtual reality ; Virtualization ; Cloud environments ; Cloud networks ; DVFS ; Enabler technologies ; Hybrid method ; Live migrations ; Physical data ; Reduced power consumption ; Green computing
  8. Source: 18th CSI International Symposium on Computer Science and Software Engineering, CSSE 2017, 25 October 2017 through 27 October 2017 ; Volume 2018-January , 2018 , Pages 18-23 ; 9781538613023 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/8320143