Multicast Traffic Reconfiguration in WDM Network for Single Node Failure Design

Author(s):  
Kunanan Luekijna ◽  
Chaiyachet Saivichit
2011 ◽  
Author(s):  
Fen Yuan ◽  
Xiaoliang Niu ◽  
Xin Li ◽  
Shanguo Huang ◽  
Wanyi Gu

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4835
Author(s):  
Zisang Xu ◽  
Feng Li ◽  
Han Deng ◽  
Minfu Tan ◽  
Jixin Zhang ◽  
...  

With the rapid development of mobile networks, there are more and more application scenarios that require group communication. For example, in mobile edge computing, group communication can be used to transmit messages to all group members with minimal resources. The group key directly affects the security of the group communication. Most existing group key agreement protocols are often flawed in performance, scalability, forward or backward secrecy, or single node failure. Therefore, this paper proposes a blockchain-based authentication and dynamic group key agreement protocol. With our protocol, each group member only needs to authenticate its left neighbor once to complete the authentication, which improved authentication efficiency. In addition, our protocol guarantees the forward secrecy of group members after joining the group and the backward secrecy of group members after leaving the group. Based on blockchain technology, we solve the problem of single node failure. Furthermore, we use mathematics to prove the correctness and security of our protocol, and the comparison to related protocols shows that our protocol reduces computation and communication costs.


2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Yang Liu ◽  
Wei Wei

MapReduce is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster. In cloud environment, node and task failure are no longer accidental but a common feature of large-scale systems. Current rescheduling-based fault tolerance method in MapReduce framework failed to fully consider the location of distributed data and the computation and storage overhead of rescheduling failure tasks. Thus, a single node failure will increase the completion time dramatically. In this paper, a replication-based mechanism is proposed, which takes both task and node failure into consideration. Experimental results show that, compared with default mechanism in Hadoop, our mechanism can significantly improve the performance at failure time, with more than 30% decreasing in execution time.


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