Neighborhood Failure Localization in All-Optical Networks via Monitoring Trails

Shared protection, such as failure dependent protection (FDP), is well recognized for its outstanding capacity efficiency in all-optical mesh networks, at the expense of lengthy restoration time due to multi-hop signaling mechanisms for failure localization, notification, and device configuration. This paper investigates a novel monitoring trail (m-trail) scenario, called Global Neighborhood Failure Localization (G-NFL), that aims to enable any shared protection scheme, including FDP, for achieving all-optical and ultra-fast failure restoration. We firstly define neighborhood of a node, which is a set of links whose failure states should be known to the node in restoration of the corresponding working lightpaths (W-LPs). By assuming every node can obtain the on-off status of traversing m-trails and W-LPs via lambda monitoring, the proposed G-NFL problem routes a set of m-trails such that each node can localize any failure in its neighborhood. Bound analysis is performed on the minimum bandwidth required for m-trails under the proposed G-NFL problem. Then a simple yet efficient heuristic approach is presented. Extensive simulation is conducted to verify the proposed G-NFL scenario under a number of different definitions of nodal neighborhood which concern the extent of dependency between the monitoring plane and data plane. The effect of reusing the spare capacity by FDP for supporting m-trails is examined. We conclude that the proposed G-NFL scenario enables a general shared protection scheme, toward signaling-free and ultra-fast failure restoration like p-Cycle, while achieving optimal capacity efficiency as FDP

Scalable fault management architecture for dynamic optical networks : an information-theoretic approach

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.MIT Barker Engineering Library copy: printed in pages.Also issued printed in pages.Includes bibliographical references (leaves 255-262).All-optical switching, in place of electronic switching, of high data-rate lightpaths at intermediate nodes is one of the key enabling technologies for economically scalable future data networks. This replacement of electronic switching with optical switching at intermediate nodes, however, presents new challenges for fault detection and localization in reconfigurable all-optical networks. Presently, fault detection and localization techniques, as implemented in SONET/G.709 networks, rely on electronic processing of parity checks at intermediate nodes. If similar techniques are adapted to all-optical reconfigurable networks, optical signals need to be tapped out at intermediate nodes for parity checks. This additional electronic processing would break the all-optical transparency paradigm and thus significantly diminish the cost advantages of all-optical networks. In this thesis, we propose new fault-diagnosis approaches specifically tailored to all-optical networks, with an objective of keeping the diagnostic capital expenditure and the diagnostic operation effort low. Instead of the aforementioned passive monitoring paradigm based on parity checks, we propose a proactive lightpath probing paradigm: optical probing signals are sent along a set of lightpaths in the network, and network state (i.e., failure pattern) is then inferred from testing results of this set of end-to-end lightpath measurements. Moreover, we assume that a subset of network nodes (up to all the nodes) is equipped with diagnostic agents - including both transmitters/receivers for probe transmission/detection and software processes for probe management to perform fault detection and localization. The design objectives of this proposed proactive probing paradigm are two folded: i) to minimize the number of lightpath probes to keep the diagnostic operational effort low, and ii) to minimize the number of diagnostic hardware to keep the diagnostic capital expenditure low.(cont.) The network fault-diagnosis problem can be mathematically modeled with a group testing-over-graphs framework. In particular, the network is abstracted as a graph in which the failure status of each node/link is modeled with a random variable (e.g. Bernoulli distribution). A probe over any path in the graph results in a value, defined as the probe syndrome, which is a function of all the random variables associated in that path. A network failure pattern is inferred through a set of probe syndromes resulting from a set of optimally chosen probes. This framework enriches the traditional group-testing problem by introducing a topological structure, and can be extended to model many other network-monitoring problems (e.g., packet delay, packet drop ratio, noise and etc) by choosing appropriate state variables. Under the group-testing-over-graphs framework with a probabilistic failure model, we initiate an information-theoretic approach to minimizing the average number of lightpath probes to identify all possible network failure patterns. Specifically, we have established an isomorphic mapping between the fault-diagnosis problem in network management and the source-coding problem in Information Theory. This mapping suggests that the minimum average number of lightpath probes required is lower bounded by the information entropy of the network state and efficient source-coding algorithms (e.g. the run-length code) can be translated into scalable fault-diagnosis schemes under some additional probe feasibility constraint. Our analytical and numerical investigations yield a guideline for designing scalable fault-diagnosis algorithms: each probe should provide approximately 1-bit of state information, and thus the total number of probes required is approximately equal to the entropy of the network state.(cont.) To address the hardware cost of diagnosis, we also developed a probabilistic analysis framework to characterize the trade-off between hardware cost (i.e., the number of nodes equipped with Tx/Rx pairs) and diagnosis capability (i.e., the probability of successful failure detection and localization). Our results suggest that, for practical situations, the hardware cost can be reduced significantly by accepting a small amount of uncertainty about the failure status.by Yonggang Wen.Ph.D

Neighborhood Failure Localization in All-Optical Networks via Monitoring Trails

Shared protection, such as failure dependent protection (FDP), is well recognized for its outstanding capacity efficiency in all-optical mesh networks, at the expense of lengthy restoration time due to multi-hop signaling mechanisms for failure localization, notification, and device configuration. This paper investigates a novel monitoring trail (m-trail) scenario, called Global Neighborhood Failure Localization (G-NFL), that aims to enable any shared protection scheme, including FDP, for achieving all-optical and ultra-fast failure restoration. We firstly define neighborhood of a node, which is a set of links whose failure states should be known to the node in restoration of the corresponding working lightpaths (W-LPs). By assuming every node can obtain the on-off status of traversing m-trails and W-LPs via lambda monitoring, the proposed G-NFL problem routes a set of m-trails such that each node can localize any failure in its neighborhood. Bound analysis is performed on the minimum bandwidth required for m-trails under the proposed G-NFL problem. Then a simple yet efficient heuristic approach is presented. Extensive simulation is conducted to verify the proposed G-NFL scenario under a number of different definitions of nodal neighborhood which concern the extent of dependency between the monitoring plane and data plane. The effect of reusing the spare capacity by FDP for supporting m-trails is examined. We conclude that the proposed G-NFL scenario enables a general shared protection scheme, toward signaling-free and ultra-fast failure restoration like p-Cycle, while achieving optimal capacity efficiency as FDP

Unconstraining Graph-Constrained Group Testing

In network tomography, one goal is to identify a small set of failed links in a network using as little information as possible. One way of setting up this problem is called graph-constrained group testing. Graph-constrained group testing is a variant of the classical combinatorial group testing problem, where the tests that one is allowed are additionally constrained by a graph. In this case, the graph is given by the underlying network topology. The main contribution of this work is to show that for most graphs, the constraints imposed by the graph are no constraint at all. That is, the number of tests required to identify the failed links in graph-constrained group testing is near-optimal even for the corresponding group testing problem with no graph constraints. Our approach is based on a simple randomized construction of tests. To analyze our construction, we prove new results about the size of giant components in randomly sparsified graphs. Finally, we provide empirical results which suggest that our connected-subgraph tests perform better not just in theory but also in practice, and in particular perform better on a real-world network topology

On Diagnosis of Forwarding Plane via Static Forwarding Rules in Software Defined Networks

Software Defined Networks (SDN) decouple the forwarding and control planes from each other. The control plane is assumed to have a global knowledge of the underlying physical and/or logical network topology so that it can monitor, abstract and control the forwarding plane. In our paper, we present solutions that install an optimal or near-optimal (i.e., within 14% of the optimal) number of static forwarding rules on switches/routers so that any controller can verify the topology connectivity and detect/locate link failures at data plane speeds without relying on state updates from other controllers. Our upper bounds on performance indicate that sub-second link failure localization is possible even at data-center scale networks. For networks with hundreds or few thousand links, tens of milliseconds of latency is achievable.Comment: Submitted to Infocom'14, 9 page