This Chapter studies the resilience of P2P live-streaming topologies. The considered scenario consists of a source peer, which provides the original signal, and a set of peers that are inter-ested in the stream and provide parts of their available bandwidth to cooperatively distribute it among each other. Hence, these systems harness the resources at the end-hosts and thus greatly decrease the server load and aid scalability to large audiences.
With each peer relying on the correct service of all preceding peers on the packet path from the source, these systems are prone to experience service disruption due to delays or departing peers. Especially considering a commercial deployment, service degradation or disruption are unacceptable. Taking into account the existence of malicious parties complicates the problem even further due to the cooperative and open nature of these systems.
4.7.1 Summary
To this end, we propose a novel approach for constructing streaming topologies that are resilient to node failures and DoS attacks. It employs network motifs in online topology control and not just as a statistical measure, for what they have been exclusively used so far. Hence, our new approach relies on decision rules based on local knowledge of the nodes, only.
Our approach consequently does not provide the participating parties with any knowledge on their position within the network nor its overall state. Hence, attackers have no means of inferring the position of other nodes nor their importance and are therefore unable to identify valuable targets for attacks.
In extensive evaluation we compare our new approach to the currently most effective ap-proach in this field. It has been shown to produce streaming topologies that are almost optimally resilient towards DoS attacks [86]. The comparison includes series of topological measures as well as their response to perfect attacks.
The evaluation results indicate that both approaches achieve comparable performance. Both approaches create topologies significantly more resilient to DoS attacks than other methods from related work.
The reference approach achieves a slightly better resilience to attacks. Yet it relies on gathering some knowledge on the succeeding topologies of each node and is characterized by much higher computational and messaging complexity.
The motif based approach on the other hand achieves better topological properties and is independent of any information on the topology other than the direct neighborhood of each node. Therefore, the new approach crucially guarantees better performance under normal circumstances and provides higher privacy to the participating peers, rendering intrusions by malicious parties almost impossible. Both these properties are of immense importance to enable possible commercial deployment.
4.7 Summary and Outlook 81
4.7.2 Outlook
Our new approach currently only aims at creating resilient live-streaming topologies. However, common objectives in this scenario are to decrease the end-to-end delay and to achieve location awareness to efficiently use the infrastructure of the underlying network. We are currently in the process of extending our approach to incorporate location information in order to provide network efficient streaming topologies. We are additionally adapting protocol and local decision rules to decrease overlay path lengths and the observed delays. On a broader view we are pursu-ing the question, whether such simple local decision rules can be applied in other decentralized settings, such as routing and topology adaptation in wireless sensor networks.
5 Finding Communication Bottlenecks in Distributed Environments
Throughout the previous Chapters we have shown that by looking at the local structures of complex networks one can understand the dynamic processes taking place on those networks.
In Chapters 3 and 4 we went one step further and showed that one can even control the dynamic performance of networks by intentionally steering their local structures in live time.
In this Chapter we investigate the reverse perspective: Is it possible to deliberately use dy-namic processes to reveal the topological structure of distributed complex networks? Our answer to that question is yes. As we show shortly, one can indeed engage gossiping (a typ-ical dynamic process in social networks) to find communications bottlenecks in a prominent subclass of distributed complex networks: multihop wireless networks.
The results of our extensive evaluation show that our novel approach is indeed very effective in detecting the few crucial for the network operation nodes and that it clearly outperforms existing state of the art methods.
Nodes in mobile networks are usually unevenly distributed over space. Several dense clusters of nodes are interconnected by a few nodes in sparsely occupied areas. Removing vital nodes along such bridges would partition the network and severely reduce the overall connectivity.
Consequently, detecting and protecting those few vital nodes in live time is crucial for keeping the network operational.
In order to achieve this task, we present our novel approach: BridgeFinder. It is based on an extended gossiping protocol and is significantly faster and more precise than any existing mech-anisms. On the one side, BridgeFinder allows us to calculate good estimates for global graph measures while operating as a fully distributed algorithm and causing only modest messaging overhead. On the other side, in contrast to conventional gossiping algorithms, it has an efficient guarding mechanism against malicious nodes trying to screw the protocol operation.
5.1 Introduction
Our main target environments are distributed wireless environments, such as wireless multihop networks. They impose severe challenges upon any distributed application. Established con-nections or even nodes may suddenly disappear due to many unpredictable factors. Because communication flow depends on the network connectivity, it is crucial to identify and protect the few critical peers within the network. Those are the articulation points which failure leads to malfunction or complete breakdown of the network, as they constitute the few bridgeskeeping the network connected.
Standard approaches for identifying such vital links require global network knowledge, which is unavailable in mobile multihop networks. Even if it were, one would requireO(N3)running time, where N is the number of nodes within the network (by using Floyd-Warshall algorithm for example). Obviously, such approaches are of extremely limited practical use.
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In order to overcome these challenges, we developed BridgeFinder. It is compliant with the Push-Sum gossiping algorithm [108]. BridgeFinder identifies critical paths between densely connected clusters based only on local knowledge. The basic idea is to let a floating value diffusethrough the network and then detect the communications bottlenecks by examining the diffusion speed of the single nodes.
Our extensive evaluation shows that BridgeFinder very efficiently identifies the critical nodes.
Furthermore, it can be integrated in the regular maintenance and application traffic and there-fore produces almost no additional messaging overhead.
Last but not the least, BridgeFinder is augmented with efficient guarding mechanism, provid-ing it with solid resilience against malicious nodes tryprovid-ing to screw the protocol operation. That makes our approach outstanding in the field of gossiping based methods.
5.1.1 Network Prerequisites
There are two requirements the underlying network has to fulfill in order for BridgeFinder to function. First, a node must be able to communicate with other nodes in the network, at least with its direct neighbors. Second, the links in the network must be undirected, i.e.
communications must be bi-directional.
In general, BridgeFinder operates on any network that meets these two criteria. Both criteria are fulfilled by mobile multihop networks, at least to the extent required by BridgeFinder.
5.1.2 Application Domains
BridgeFinder detects critical peers, crucial for communication within the network. Therefore, the fewer the nodes on which communication depends, the higher the benefit of using our ap-proach. Compared to algorithms based on global knowledge in static networks, BridgeFinder is less accurate than standard approaches for detecting critical nodes. However, its most sig-nificant advantage is that it requires no global knowledge and can operate on continuously changing underlying networks. That makes it very attractive for distributed environments, like wireless ad-hoc networks, P2P overlay networks and wireless sensor networks.
Once the critical peers are known, partitioning can be avoided by establishing additional links among susceptible clusters connected by those critical peers. In any complex network, maintaining information flow is equal to the critical nodes remaining operational. The higher the importance of a node, the higher is the impact of losing it and reversely the higher is the benefit of detecting and protecting that node a priori.