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Time-sensitive networks, as in the context of IEEE-TSN and IETF-Detnet, require bounds on worst-case delays. Various network analysis tools compute such bounds; these tools are based on different methods and provide delay bounds that are all valid but may differ; furthermore, it is generally not known which tool will provide the best bound. To obtain the best possible bound, users need to implement multiple pieces of code with a different syntax for every tool, which is impractical and error-prone. To address this issue, we present Saihu, a Python interface that integrates the three most frequently used worst-case network analysis tools: xTFA, DiscoDNC, and Panco. They altogether implement six analysis methods. Saihu provides a general interface that enables defining a network in a single file and executing all tools simultaneously without any modification. Saihu further exports analysis results as formatted reports automatically and allows quick generation of certain types of networks. With its simplified steps of execution, Saihu reduces the burden on users and makes it accessible for anyone working with time-sensitive networks. An introductory video is available at https://youtu.be/MiOhLay8Kr4.

We consider the problem of coordination among replicated SDN controllers, where the challenge is to ensure a consistent view of the network while reacting to network events in a prompt manner. Existing solutions are either consensus-based, which achieve consistency at the expense of high latency; or eventual-consistency-based, which have low latency at the expense of severe limitations on the types of applications and policies implementable by the controller. We propose the Fast and Consistent Controller-Replication (FCR) scheme. FCR is based on a deterministic agreement mechanism that performs agreement on the input of controllers, instead of agreement on the output as done in consensus mechanisms. We formally prove that FCR provides the same guarantees in terms of implementable applications and network policies, as any deterministic single-image controller. Through simulation and implementation, we show that these guarantees can be implemented with little latency overhead, compared to eventual-consistency approaches, and can be achieved significantly faster than consensus-based approaches.

We study the stability of a Markovian model of electricity productionand consumption that incorporates production volatility due torenewables and uncertainty about actual demand versus plannedproduction. We assume that the energy producer targets a fixed energyreserve, subject to ramp-up and ramp-down constraints, and thatappliances are subject to demand-response signals and adjust theirconsumption to the available production by delaying their demand. Whena constant fraction of the delayed demand vanishes over time, we showthat the general state Markov chain characterizing the system ispositive Harris and ergodic (i.e., delayed demand is bounded with highprobability). However, when delayed demand increases by a constantfraction over time, we show that the Markov chain is non-positive (i.e., there exists a non-zero probability that delayed demand becomesunbounded). We exhibit Lyapunov functions to prove our claims. Inaddition, we provide examples of heating appliances that, whendelayed, have energy requirements corresponding to the two consideredcases.