To guarantee correct operation of their networks, operators have to promptly detect and diagnose data-plane issues, like broken interface cards or link failures. Networks are becoming more complex, with a growing number of Equal Cost MultiPath (ECMP) and link bundles. Hence, some data-plane problems (e.g. silent packet dropping at one router) can hardly be detected with control-plane protocols or simple monitoring tools like ping or traceroute.In this paper, we propose a new technique, called SCMon, that enables continuous monitoring of the data-plane, in order to track the health of all routers and links. SCMon leverages the recently proposed Segment Routing (SR) architecture to monitor the entire network with a single box (and no additional monitoring protocol). In particular, SCMon uses SR to (i) force monitoring probes to travel over cycles; and (ii) test parallel links and bundles at a per-link granularity. We present original algorithms to compute cycles that cover all network links with a limited number of SR segments. Further, we prototype and evaluate SCMon both with simulations and Linux-based emulations. Our experiments show that SCMon quickly detects and precisely pinpoints dataplane problems, with a limited overhead.
The smart table constraint represents a powerful modeling tool that has been recently introduced. This constraint allows the user to represent compactly a number of well-known (global) constraints and more generally any arbitrarily structured constraints, especially when disjunction is at stake. In many problems, some constraints are given under the basic and simple form of tables explicitly listing the allowed combinations of values. In this paper, we propose an algorithm to convert automatically any (ordinary) table into a compact smart table. Its theoretical time complexity is shown to be quadratic in the size of the input table. Experimental results demonstrate its compression efficiency on many constraint cases while showing its reasonable execution time. It is then shown that using filtering algorithms on the resulting smart table is more efficient than using state-of-the-art filtering algorithms on the initial table.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.