scholarly journals An Automatic Network Live Migration Mechanism for Cloud Computing

2014 ◽  
Author(s):  
Chen Min-Xiou ◽  
Terng Huai-Hsien
Keyword(s):  
Cloud Computing ◽  
Live Migration ◽  
Migration Mechanism

Modeling and Managing Energy Efficiency Data Center by a Live Migration Mechanism in Mobile Cloud Computing Environments

2012 ◽  
Vol 10 (8) ◽  
pp. 1855-1861
Author(s):  
Dawei Sun ◽  
Guiran Chang ◽  
Dongqi Wang ◽  
Dong Chen ◽  
Xingwei Wang
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Data Center ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Live Migration ◽  
Migration Mechanism ◽  
Computing Environments ◽  
Efficiency Data

Live migration improvements by related dirty memory prediction in cloud computing

2017 ◽  
Vol 90 ◽  
pp. 83-89 ◽  
Author(s):  
Tin-Yu Wu ◽  
Nadra Guizani ◽  
Jhih-Siang Huang
Keyword(s):  
Cloud Computing ◽  
Live Migration ◽  
Memory Prediction

Research on Energy-Saving Algorithms with Migration Energy Consumption in Heterogeneous Cloud Environment

2014 ◽  
Vol 986-987 ◽  
pp. 1383-1386
Author(s):  
Zhen Xing Yang ◽  
He Guo ◽  
Yu Long Yu ◽  
Yu Xin Wang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Saving ◽  
Large Scale ◽  
Electrical Energy ◽  
Cloud Environment ◽  
Migration Energy ◽  
Live Migration ◽  
Large Scale Data ◽  
Heterogeneous Cloud

Cloud computing is a new emerging paradigm which delivers an infrastructure, platform and software as services in a pay-as-you-go model. However, with the development of cloud computing, the large-scale data centers consume huge amounts of electrical energy resulting in high operational costs and environment problem. Nevertheless, existing energy-saving algorithms based on live migration don’t consider the migration energy consumption, and most of which are designed for homogeneous cloud environment. In this paper, we take the first step to model energy consumption in heterogeneous cloud environment with migration energy consumption. Based on this energy model, we design energy-saving Best fit decreasing (ESBFD) algorithm and energy-saving first fit decreasing (ESFFD) algorithm. We further provide results of several experiments using traces from PlanetLab in CloudSim. The experiments show that the proposed algorithms can effectively reduce the energy consumption of data center in the heterogeneous cloud environment compared to existing algorithms like NEA, DVFS, ST (Single Threshold) and DT (Double Threshold).

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