scholarly journals Reducing Energy Consumption With Cost Budget Using Available Budget Preassignment in Heterogeneous Cloud Computing Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 20572-20583 ◽  
Author(s):  
Yuekun Chen ◽  
Guoqi Xie ◽  
Renfa Li
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Computing Systems ◽  
Heterogeneous Cloud ◽  
Reducing Energy

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).

scholarly journals THE ADVANTAGE OF GENETIC ALGORITHM IN ENERGY-EFFICIENT SCHEDULING FOR HETEROGENEOUS CLOUD COMPUTING

2016 ◽  
Vol 4 (7) ◽  
pp. 1-9
Author(s):  
Hang Zhou ◽  
Samina Kausar ◽  
Ningning Dong
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Scheduling Problem ◽  
New Energy ◽  
Heavy Burden ◽  
Heterogeneous Cloud ◽  
Energy Efficient Scheduling ◽  
Computing Infrastructure

Nowadays Energy Consumption has been a heavy burden on the enterprise cloud computing infrastructure. This paper focuses on the hardware factors in energy consumption. Inspired by DVFS, it proposes a new energy-efficient (EE) model. This paper formulates the scheduling problem and genetic algorithm is applied to obtain higher efficiency value. Simulations are implemented to verify the advantage of genetic algorithm. In addition, the robustness of our strategy is validated by modifying the relevant parameters of the experiment.

scholarly journals Reducing Energy Consumption with Dvfs for Real-Time Services in Cloud Computing

2014 ◽  
Vol 16 (3) ◽  
pp. 53-57 ◽  
Author(s):  
Vrunda J. Patel ◽  
◽  
Prof. Hitesh A. Bheda
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Real Time ◽  
Reducing Energy

scholarly journals Auto Resource Management to Enhance Reliability and Energy Consumption in Heterogeneous Cloud Computing

2020 ◽  
Vol 12 (2) ◽  
pp. 85-108
Author(s):  
Moataz H. Khalil ◽  
Mohamed Azab ◽  
Ashraf Elsayed ◽  
Walaa Sheta ◽  
Mahmoud Gabr ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Resource Management ◽  
Heterogeneous Cloud
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