Short-Term Prediction Model to Maximize Renewable Energy Usage in Cloud Data Centers

2017 ◽  
pp. 203-218 ◽  
Author(s):  
Atefeh Khosravi ◽  
Rajkumar Buyya
Keyword(s):  
Renewable Energy ◽  
Prediction Model ◽  
Data Centers ◽  
Energy Usage ◽  
Short Term ◽  
Cloud Data ◽  
Term Prediction ◽  
Cloud Data Centers ◽  
Short Term Prediction

Renewable energy prediction: A novel short-term prediction model of photovoltaic output power

2019 ◽  
Vol 228 ◽  
pp. 359-375 ◽  
Author(s):  
Ling-Ling Li ◽  
Shi-Yu Wen ◽  
Ming-Lang Tseng ◽  
Cheng-Shan Wang
Keyword(s):  
Renewable Energy ◽  
Prediction Model ◽  
Output Power ◽  
Short Term ◽  
Energy Prediction ◽  
Term Prediction ◽  
Short Term Prediction

Optimized Renewable Energy Use in Green Cloud Data Centers

2019 ◽  
pp. 314-330
Author(s):  
Minxian Xu ◽  
Adel N. Toosi ◽  
Behrooz Bahrani ◽  
Reza Razzaghi ◽  
Martin Singh
Keyword(s):  
Renewable Energy ◽  
Energy Use ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Green Cloud

TAR Based Hotspot Prediction in Cloud Data Centres

2019 ◽  
Vol 11 (3) ◽  
pp. 1-22
Author(s):  
Anu Valiyaparambil Raveendran ◽  
Elizabeth Sherly Sherly
Keyword(s):  
Prediction Model ◽  
Resource Utilization ◽  
Data Centers ◽  
Linear Regression Analysis ◽  
Load Prediction ◽  
Bandwidth Utilization ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Cloud Data Centers

In this article, the authors studied hotspots in cloud data centers, which are caused due to a lack of resources to satisfy the peak immediate requests from clients. The nature of resource utilization in cloud data centers are totally dynamic in context and may lead to hotspots. Hotspots are unfavorable situations which cause SLA violations in some scenarios. Here they use trend aware regression (TAR) methods as a load prediction model and perform linear regression analysis to detect the formation of hotspots in physical servers of cloud data centers. This prediction model provides an alarm period for the cloud administrators either to provide enough resources to avoid hotspot situations or perform interference aware virtual machine migration to balance the load on servers. Here they analyzed the physical server resource utilization model in terms of CPU utilization, memory utilization and network bandwidth utilization. In the TAR model, the authors consider the degree of variation between the current points in the prediction window to forecast the future points. The TAR model provides accurate results in its predictions.

A Short-Term Prediction Model of Topic Popularity on Microblogs

2013 ◽  
pp. 759-769 ◽  
Author(s):  
Juanjuan Zhao ◽  
Weili Wu ◽  
Xiaolong Zhang ◽  
Yan Qiang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

A multi-output prediction model for physical machine resource usage in cloud data centers

2022 ◽  
Author(s):  
Yongde Zhang ◽  
Fagui Liu ◽  
Bin Wang ◽  
Weiwei Lin ◽  
Guoxiang Zhong ◽  
...  
Keyword(s):  
Prediction Model ◽  
Data Centers ◽  
Resource Usage ◽  
Cloud Data ◽  
Physical Machine ◽  
Cloud Data Centers
Sign in / Sign up

Export Citation Format

Share Document