Practical Single Node Failure Recovery Using Fractional Repetition Codes in Data Centers

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.

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Practical multiple node failure recovery in distributed storage systems

2016 IEEE Symposium on Computers and Communication (ISCC) ◽

10.1109/iscc.2016.7543851 ◽

2016 ◽

Author(s):

M. Itani ◽

S. Sharafeddine ◽

I. ElKabbani

Keyword(s):

Storage Systems ◽

Distributed Storage ◽

Failure Recovery ◽

Node Failure ◽

Multiple Node ◽

Distributed Storage Systems

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Modeling and Algorithm for Multiple Spanning Tree Provisioning in Resilient and Load Balanced Ethernet Networks

Mathematical Problems in Engineering ◽

10.1155/2015/676542 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Steven S. W. Lee ◽

Kuang-Yi Li ◽

Chieh-Ching Lin

Keyword(s):

Load Balancing ◽

Spanning Trees ◽

Telecommunication Networks ◽

Failure Recovery ◽

Single Node ◽

Network Failure ◽

Network Protection ◽

Single Link ◽

Ethernet Network ◽

Ethernet Networks

We propose a multitree based fast failover scheme for Ethernet networks. In our system, only few spanning trees are used to carry working traffic in the normal state. As a failure happens, the nodes adjacent to the failure redirect traffic to the preplanned backup VLAN trees to realize fast failure recovery. In the proposed scheme, a new leaf constraint is enforced on the backup trees. It enables the network being able to provide 100% survivability against any single link and any single node failure. Besides fast failover, we also take load balancing into consideration. We model an Ethernet network as a twolayered graph and propose an Integer Linear Programming (ILP) formulation for the problem. We further propose a heuristic algorithm to provide solutions to large networks. The simulation results show that the proposed scheme can achieve high survivability while maintaining load balancing at the same time. In addition, we have implemented the proposed scheme in an FPGA system. The experimental results show that it takes only fewμsec to recover a network failure. This is far beyond the 50 msec requirement used in telecommunication networks for network protection.

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Survivable virtual topology mapping for single-node failure in IP over WDM network

10.1117/12.904411 ◽

2011 ◽

Author(s):

Fen Yuan ◽

Xiaoliang Niu ◽

Xin Li ◽

Shanguo Huang ◽

Wanyi Gu

Keyword(s):

Virtual Topology ◽

Single Node ◽

Node Failure ◽

Ip Over Wdm ◽

Wdm Network ◽

Topology Mapping

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A Replication-Based Mechanism for Fault Tolerance in MapReduce Framework

Mathematical Problems in Engineering ◽

10.1155/2015/408921 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Yang Liu ◽

Wei Wei

Keyword(s):

Fault Tolerance ◽

Large Scale ◽

Failure Time ◽

Programming Model ◽

Large Data ◽

Distributed Data ◽

Mapreduce Framework ◽

Single Node ◽

Node Failure ◽

Tolerance Method

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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