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Failures in optical networks are inevitable. They may occur during work being done for the maintenance of other infrastructures, or on a larger scale as the result of an attack or large-scale disaster. As a result, service availability, an important aspect of Quality of Service (QoS), is often degraded. Appropriate fault recovery techniques are thus crucial to meet the requirements set by the Service Level Agreements (SLAs) between carriers and their customers. In this paper, we focus on practical issues related to the deployment of fault recovery mechanisms in commercial optical networks. In particular, we outline the most important functionalities that, to the best of our knowledge, need to be implemented, as well as discuss the related problems making deployment of fault recovery mechanisms difficult. Investigated topics include fault recovery challenges (fault detection, location, and recovery), multiple failures recovery, as well as application of reliability mechanisms in Elastic Optical Networks, and in multiprovider multilevel networks.

Recently the constant growth of the wireless communication technology has caused a huge demand for experimental facilities. Hence many research institutes setup public accessible experimental facilities, known as testbeds. Compared to the facilities developed by individual researchers, a testbed typically offers more resources, more flexibilities. However, due to the fact that equipments are located remotely and experiments involve more complex scenarios, the required complexity for analysis is also higher. A deep insight on the underlying wireless environment of the testbed becomes necessary for comprehensive analysis. In this chapter, we present a framework and associated techniques for monitoring the wireless environment in a large scale wireless testbed. The framework utilizes most common resources in the testbed, such as WI-FI nodes, as well as some high-end software-defined radio platforms. Information from both physical layer and network layer are taken into account. We observe that feature detection is more sensitive than general energy detection for dedicated technologies, and distributed spectrum sensing can further improve the detection sensitivity. Such observations are applied to achieve better interference detection. The performance is mainly analyzed experimentally.

Following the iterative research cycle process, this chapter elaborates a methodology and documents the steps followed for the design of a dynamic multicast routing algorithm, referred to as Greedy Compact Multicast Routing. Starting from the design of the dynamic multicast routing algorithm, we then evaluate by simulation on large-scale topologies its performance and compare them with the Abraham compact multicast routing scheme and two other reference schemes, namely the Shortest Path Tree (SPT) and the Steiner Tree (ST) algorithm. Performance evaluation and comparison include i) the stretch of the multicast routing paths also referred to as multicast distribution tree (MDT), ii) the memory space required to store the resulting routing table entries, and iii) the total communication or messaging cost, i.e., the number of messages exchanged to build the MDT. However, such performance evaluation is a necessary but not a sufficient condition to meet in order to expect deployment of multicast routing. Indeed, if one can determine that traffic exchanges are spatially and temporally concentrated, this would provide elements indicating the relevance for the introduction of such scheme in the Internet. Otherwise (if traffic exchanges are spatially and temporally diverse, i.e., highly distributed), then very few of them would benefit from a (shared) point-to-multipoint routing paths and multicast routing scheme would be less useful. For this purpose, we have conducted a multicast tree inference study. In turn, data and results obtained from these studies provides more realistic scenarios for emulation experiments against the currently deployed approach combining MBGP and PIMdeployed in IPTV or mVPN context.