Run-Time Reconfiguration Strategy and Implementation of Time-Triggered Networks

Abstract

Time-triggered networks are deployed in avionics and astronautics because they provide deterministic and low-latency communications. Remapping of partitions and the applications that reside in them that are executing on the failed core and the resulting re-routing and re-scheduling are conducted when a permanent end-system core failure occurs and local resources are insufficient. We present a network-wide reconfiguration strategy as well as an implementation scheme, and propose an Integer Linear Programming based joint mapping, routing, and scheduling reconfiguration method (JILP) for global reconfiguration. Based on scheduling compatibility, a novel heuristic algorithm (SCA) for mapping and routing is proposed to reduce the reconfiguration time. Experimentally, JILP achieved a higher success rate compared to mapping-then-routing-and-scheduling algorithms. In addition, relative to JILP, SCA/ILP was 50-fold faster and with a minimal impact on reconfiguration success rate. SCA achieved a higher reconfiguration success rate compared to shortest path routing and load-balanced routing. In addition, scheduling compatibility plays a guiding role in ILP-based optimization objectives and ‘reconfigurable depth’, which is a metric proposed in this paper for the determination of the reconfiguration potential of a TT network.

Enhancing low‐priority traffic reconfiguration designs in mixed‐critical avionics networks

“…This approach increases the likelihood of successfully scheduling the TT flows within each subset. Moreover, since the planning order of TT flows within the same subset is adjacent, selecting the same directed links as part of their routing is easier, which contributes to the scheduling success rate (as verified in our previous work [14]).…”

“…This approach increases the likelihood of successfully scheduling the TT flows within each subset. Moreover, since the planning order of TT flows within the same subset is adjacent, selecting the same directed links as part of their routing is easier, which contributes to the scheduling success rate (as verified in our previous work [14]). $$$$\begin{align} \def\eqcellsep{&}\begin{array}{c}\forall {f_i},{f_j} \in {F_{TT}}:\\[6pt] SC{\left({{f_i},{f_j}} \right)} = {\left({1 - \frac{{{l_i} + {l_j} - 1}}{{gcd}}} \right)} \times lcm, \end{array} \end{align}$$$$where $$gcd$$ and $$lcm$$ denote the greatest common divisor and least common multiple, respectively, of the periods of all TT flows.…”

“…The topology is generated using the FNLR and JTTPS algorithms in 5.2, respectively, and the traffic model adopts Group C. The reconfiguration is triggered by randomly simulating a certain number of directed link failures. After the fault is triggered, the simulation mainly includes the following two steps(TT and RC): (1) The reconfiguration of TT traffic is uniformly performed using a joint routing and scheduling ILP‐based solver(JILP) in both topologies generated by JTTPS and FNLR to maintain consistency [14]. (2) The recovery of RC traffic (rerouting) is uniformly performed using a constraint‐based ILP solver and communication effectiveness verification [17], again to maintain consistency.…”

“…Recalling the concept of scheduling compatibility (SC) from our previous work [14], we apply Equation (12) to solve for the scheduling compatibility between all flows pairwise and form a pairwise interconnected undirected graph. Then, adopting the Normalized Cut (NCut) framework commonly used in image processing, we divide all TT flows into balanced subsets with an equal number of elements.…”

“…Our previous research extended the application scenario of network layer reconfiguration by combining the application migration decision problem with routing and scheduling [13]. Moreover, we optimized the reconfiguration routing algorithm by incorporating scheduling compatibility, which explored the trade-off between reconfiguration solution success rate, efficiency, and quality under different redundant resource states of the network [14]. However, the aforementioned studies do not account for the impact of TT traffic on low-priority flow reconfiguration and corresponding reconfiguration issues.…”