A Self-Adaptive Layered Sleep-Based Method for Security Dynamic Scheduling in Cloud Storage

2017 ◽  
Author(s):  
Xin Liu ◽  
Yanju Zhou
Keyword(s):  
Cloud Storage ◽  
Dynamic Scheduling ◽  
Self Adaptive

scholarly journals Harmony: Towards Automated Self-Adaptive Consistency in Cloud Storage

2012 ◽  
Author(s):  
Houssem-Eddine Chihoub ◽  
Shadi Ibrahim ◽  
Gabriel Antoniu ◽  
Maria S. Perez
Keyword(s):  
Cloud Storage ◽  
Self Adaptive

File heat-based Self-adaptive Replica Consistency Strategy for Cloud Storage

2014 ◽  
Vol 9 (8) ◽  
Author(s):  
Zhen Zhou ◽  
Shuyu Chen ◽  
Tao Ren ◽  
Tianshu Wu
Keyword(s):  
Cloud Storage ◽  
Replica Consistency ◽  
Self Adaptive

Dynamic Scheduling with Service Curve for QoS Guarantee of Large-Scale Cloud Storage

2018 ◽  
Vol 67 (4) ◽  
pp. 457-468 ◽  
Author(s):  
Yu Zhang ◽  
Qingsong Wei ◽  
Cheng Chen ◽  
Mingdi Xue ◽  
Xinkun Yuan ◽  
...  
Keyword(s):  
Cloud Storage ◽  
Large Scale ◽  
Dynamic Scheduling ◽  
Qos Guarantee ◽  
Service Curve

A Self-Adaptive Prediction Algorithm for Cloud Workloads

2015 ◽  
Vol 7 (2) ◽  
pp. 65-76 ◽  
Author(s):  
Li Mao ◽  
Deyu Qi ◽  
Weiwei Lin ◽  
Chaoyue Zhu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Dynamic Scheduling ◽  
Main Idea ◽  
Prediction Method ◽  
Prediction Algorithm ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Adaptive Prediction ◽  
Self Adaptive

It is difficult to analyze the workload in complex cloud computing environments with a single prediction algorithm as each algorithm has its own shortcomings. A self-adaptive prediction algorithm combining the advantages of linear regression (LR) and a BP neural network to predict workloads in clouds is proposed in this paper. The main idea of the self-adaptive prediction algorithm is to choose the better prediction method of the future workload. Some experiments of prediction algorithms are conducted with workloads on the public cloud servers. The experimental results show that the proposed algorithm has a relatively high accuracy on the workload predictions compared with the BP neural network and LR. Furthermore, in order to use the proposed algorithm in a cloud data center, a dynamic scheduling architecture of cloud resources is designed to improve resource utilization and reduce energy consumption.

Dynamic Scheduling of Post-Earthquake Emergency Material Based on the Baseline Perspective

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Ziqi Tang ◽  
Hui Hu ◽  
Guangyuan Yang ◽  
Rundong Wu
Keyword(s):  
Dynamic Scheduling
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