Research on Load Prediction Based on Improve GWO and ELM in Cloud Computing

2019 ◽  
Author(s):  
Shengcai Zhang ◽  
Dezhi An ◽  
Zhenxiang He
Keyword(s):  
Cloud Computing ◽  
Load Prediction

Host load prediction with long short-term memory in cloud computing

2017 ◽  
Vol 74 (12) ◽  
pp. 6554-6568 ◽  
Author(s):  
Binbin Song ◽  
Yao Yu ◽  
Yu Zhou ◽  
Ziqiang Wang ◽  
Sidan Du
Keyword(s):  
Cloud Computing ◽  
Short Term Memory ◽  
Load Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Host Load Prediction ◽  
Host Load

scholarly journals Elastic neural network method for load prediction in cloud computing grid

2019 ◽  
Vol 9 (2) ◽  
pp. 1201
Author(s):  
Kefaya S. Qaddoum ◽  
Nameer N. El Emam ◽  
Mosleh A. Abualhaj
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Virtual Machine ◽  
User Satisfaction ◽  
Load Prediction ◽  
Virtual Machine Migration ◽  
Adaptive Smoothing ◽  
Evolving System ◽  
Computing Grid ◽  
Dynamic Resource

<span>Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches.</span>

Multi-step-ahead host load prediction using autoencoder and echo state networks in cloud computing

2015 ◽  
Vol 71 (8) ◽  
pp. 3037-3053 ◽  
Author(s):  
Qiangpeng Yang ◽  
Yu Zhou ◽  
Yao Yu ◽  
Jie Yuan ◽  
Xianglei Xing ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Load Prediction ◽  
Echo State Networks ◽  
Host Load Prediction ◽  
Host Load
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