Protecting against link failures in communication networks is essential to increase robustness, accessibility, and reliability of data transmission. Recently, network coding has been proposed as a solution to provide agile and cost efficient network protection against link failures, which does not require data rerouting, or packet retransmission. To achieve this, separate paths have to be provisioned to carry encoded packets, hence requiring either the addition of extra links, or reserving some of the resources for this purpose. In this paper, we propose network protection codes against a single link failure using network coding, where a separate path using reserved links is not needed. In this case portions of the link capacities are used to carry the encoded packets. The scheme is extended to protect against multiple link failures and can be implemented at an overlay layer. Although this leads to reducing the network capacity, the network capacity reduction is asymptotically small in most cases of practical interest. We demonstrate that such network protection codes are equivalent to error correcting codes for erasure channels. Finally, we study the encoding and decoding operations of such codes over the binary field.
@cite_0: In this paper we show how information contained in robust network codes can be used for passive inference of possible locations of link failures or losses in a network. For distributed randomized network coding, we bound the probability of being able to distinguish among a given set of failure events, and give some experimental results for one and two link failures in randomly generated networks. We also bound the required field size and complexity for designing a robust network code that distinguishes among a given set of failure events
@cite_15: With network coding, intermediate nodes between the source and the receivers of an end-to-end communication session are not only capable of relaying and replicating data messages, but also of coding incoming messages to produce coded outgoing ones. Recent studies have shown that network coding is beneficial for peer-to-peer content distribution, since it eliminates the need for content reconciliation, and is highly resilient to peer failures. In this paper, we present our recent experiences with a highly optimized and high-performance C++ implementation of randomized network coding at the application layer. We present our observations based on an extensive series of experiments, draw conclusions from a wide range of scenarios, and are more cautious and less optimistic as compared to previous studies.
@cite_14: A novel randomized network coding approach for robust, distributed transmission and compression of information in networks is presented, and its advantages over routing-based approaches is demonstrated.
@cite_3: Network Monitoring is a challenging problem that has received a lot of attention in the Internet community, particularly in the context of overlay networks. Independently, recent advances in Network Coding have shown that it is possible to increase network capacity and better share the available resources by allowing intermediate nodes to perform processing operations, in addition to just forwarding packets. In this work, we propose the use of Network Coding techniques to improve Network Monitoring. As a specific application, we use our approach for the well-known problem of network tomography, and in particular for inferring link loss rates from end-to-end measurements. We demonstrate that our approach can decrease the bandwidth used by probes, improve the accuracy of estimation, and decrease the complexity of selecting paths or trees to send probes. Overall, we argue that the use of Network Coding techniques shows great promise for improving several aspects of monitoring in overlay networks.
Monitoring network information flow using network coding was introduced in @cite_0 @cite_14 . @cite_3 , it was shown how to use network coding techniques to improve network monitoring in overlay networks. Practical aspects of network coding has been shown in @cite_15 .