scholarly journals K‐ear: Extracting data access periodic characteristics for energy‐aware data clustering and storing in cloud storage systems

2020 ◽  
Author(s):  
Xindong You ◽  
Tian Sun ◽  
Dawei Sun ◽  
Xunyun Liu ◽  
Xueqiang Lv ◽  
...  
Keyword(s):  
Data Clustering ◽  
Cloud Storage ◽  
Storage Systems ◽  
Data Access ◽  
Energy Aware

DAC-MACS: Effective Data Access Control for Multiauthority Cloud Storage Systems

2013 ◽  
Vol 8 (11) ◽  
pp. 1790-1801 ◽  
Author(s):  
Kan Yang ◽  
Xiaohua Jia ◽  
Kui Ren ◽  
Bo Zhang ◽  
Ruitao Xie
Keyword(s):  
Access Control ◽  
Cloud Storage ◽  
Storage Systems ◽  
Data Access ◽  
Data Access Control

scholarly journals Two-Factor Data Access Control With Efficient Revocation for Multi-Authority Cloud Storage Systems

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 393-405 ◽  
Author(s):  
Xiaoyu Li ◽  
Shaohua Tang ◽  
Lingling Xu ◽  
Huaqun Wang ◽  
Jie Chen
Keyword(s):  
Access Control ◽  
Cloud Storage ◽  
Storage Systems ◽  
Data Access ◽  
Data Access Control ◽  
Factor Data

scholarly journals An Optimized Replica Distribution Method in Cloud Storage System

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Yan Wang ◽  
Jinkuan Wang
Keyword(s):  
Response Time ◽  
Information Acquisition ◽  
Cloud Storage ◽  
Storage Systems ◽  
Markov Chain Model ◽  
Storage System ◽  
Data Access ◽  
Chain Model ◽  
Distribution Method ◽  
Acquisition Method

Aiming at establishing a shared storage environment, cloud storage systems are typical applications of cloud computing. Therefore, data replication technology has become a key research issue in storage systems. Considering the performance of data access and balancing the relationship between replica consistency maintenance costs and the performance of multiple replicas access, the methods of replica catalog design and the information acquisition method are proposed. Moreover, the deputy catalog acquisition method to design and copy the information is given. Then, the nodes with the global replica of the information replicate data resources, which have the high access frequency and the long response time. Afterwards, the Markov chain model is constructed. And a matrix geometric solution is used to export the steady-state solution of the model. The performance parameters in terms of the average response time, finish time, and the replica frequency are given to optimize the number of replicas in the storage system. Finally, numerical results with analysis are proposed to demonstrate the influence of the above parameters on the system performance.

scholarly journals MQHD: Dynamic Load Balancing Method for Energy-Aware Storage Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaoling Xie
Keyword(s):  
Load Balancing ◽  
Dynamic Load ◽  
Storage Systems ◽  
Pressure Ratio ◽  
Data Access ◽  
Dynamic Load Balancing ◽  
Energy Aware ◽  
Experimental Conditions ◽  
Migration Strategy ◽  
Load Pressure

Aiming at the problem that some disks of energy-aware storage systems are easily overloaded, a dynamic load balancing method based on multiqueue and heat degree (MQHD) is proposed. According to data popularity, MQHD divides data into multiple least recently used (LRU) queues by data access frequency and access temporal locality and uses heat degree to measure the load pressure brought by each data unit to a disk. When a disk is overloaded, MQHD calculates the load pressure ratio (LPR) according to the disk overload degree and then selects some appropriate data to migrate according to the LPR. The experimental results show that, compared with the popular data concentration method (PDC), the workload-adaptive management method (WAM), and the energy model-based file migration strategy (EM-FMS), MQHD is the most effective. Under given experimental conditions, the request change ratio (RCR) value of MQHD is 0.371, EM-FMS is 0.2872, and WAM is 0.0114. Compared with EM-FMS, MQHD has better rapidity and less overhead.

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