scholarly journals Developing the Cloud-integrated data replication framework in decentralized online social networks

2016 ◽  
Vol 82 (1) ◽  
pp. 113-129 ◽  
Author(s):  
Songling Fu ◽  
Ligang He ◽  
Xiangke Liao ◽  
Chenlin Huang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Data Replication ◽  
Decentralized Online Social Networks

scholarly journals Decentralized Online Social Networks

2010 ◽  
pp. 349-378 ◽  
Author(s):  
Anwitaman Datta ◽  
Sonja Buchegger ◽  
Le-Hung Vu ◽  
Thorsten Strufe ◽  
Krzysztof Rzadca
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Decentralized Online Social Networks

scholarly journals Analyzing the Impact of Storage Shortage on Data Availability in Decentralized Online Social Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Songling Fu ◽  
Ligang He ◽  
Xiangke Liao ◽  
Kenli Li ◽  
Chenlin Huang
Keyword(s):  
Social Networks ◽  
Mobile Devices ◽  
Online Social Networks ◽  
Storage Capacity ◽  
Data Availability ◽  
Novel Method ◽  
Smart Mobile ◽  
The Impact ◽  
Availability Model ◽  
Decentralized Online Social Networks

Maintaining data availability is one of the biggest challenges in decentralized online social networks (DOSNs). The existing work often assumes that the friends of a user can always contribute to the sufficient storage capacity to store all data. However, this assumption is not always true in today’s online social networks (OSNs) due to the fact that nowadays the users often use the smart mobile devices to access the OSNs. The limitation of the storage capacity in mobile devices may jeopardize the data availability. Therefore, it is desired to know the relation between the storage capacity contributed by the OSN users and the level of data availability that the OSNs can achieve. This paper addresses this issue. In this paper, the data availability model over storage capacity is established. Further, a novel method is proposed to predict the data availability on the fly. Extensive simulation experiments have been conducted to evaluate the effectiveness of the data availability model and the on-the-fly prediction.

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