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Combined task- and network-level scheduling for distributed time-triggered systems

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Ethernet-based time-triggered networks (e.g. TTEthernet) enable the cost-effective integration of safety-critical and real-time distributed applications in domains where determinism is a key requirement, like the aerospace, automotive, and industrial domains. Time-Triggered communication typically follows an offline and statically configured schedule (the synthesis of which is an NP-complete problem) guaranteeing contention-free frame transmissions. Extending the end-to-end determinism towards the application layers requires that software tasks running on end nodes are scheduled in tight relation to the underlying time-triggered network schedule. In this paper we discuss the simultaneous co-generation of static network and task schedules for distributed systems consisting of preemptive time-triggered tasks which communicate over switched multi-speed time-triggered networks. We formulate the schedule problem using first-order logical constraints and present alternative methods to find a solution, with or without optimization objectives, based on satisfiability modulo theories (SMT) and mixed integer programming (MIP) solvers, respectively. Furthermore, we present an incremental scheduling approach, based on the demand bound test for asynchronous tasks, which significantly improves the scalability of the scheduling problem. We demonstrate the performance of the approach with an extensive evaluation of industrial-sized synthetic configurations using alternative state-of-the-art SMT and MIP solvers and show that, even when using optimization, most of the problems are solved within reasonable time using the incremental method.

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  1. The terms task and tt-task as well as message and tt-message will be used interchangeably in this paper.

  2. Note that TTEthernet supports three traffic classes, namely time-triggered (TT), rate-constrained (RC), and best-effort (BE). We explicitly base this work on the TT traffic class in order to establish a time-triggered paradigm across the network and application domains. Extending the results presented in this paper to accommodate other traffic classes is a concern currently being addressed in the context of mixed-criticality systems (e.g. Steiner 2011; Tamas-Selicean et al. 2012).

  3. We have identified a clear performance disparity between available MIP solvers, which in practice has limited our evaluation scope to a single one of the state-of-the-art MIP solver.

  4. The assignment of tasks to CPUs is completely done during design time and corresponds to system requirements as well as other physical constraints (e.g. sensing tasks assigned to the node where the sensors are physically connected).

  5. Note that in ARINC (2009) virtual links are defined as multicast, e.g. with one sender and one or more receivers whereas in this work we constrain VLs to being unicast, e.g. one sender and one receiver. Our model can be extended to support multicast VLs without compromising the validity of the methods. For the sake of simplicity we leave this trivial extension as future work.

  6. Note that despite we do not implicitly synthesize a schedule for the incoming frames the arrival schedule is a trivial transformation of the related schedules of the predecessor frames.

  7. Note that physical links and by extension also ports are full-duplex, and therefore, each ingress port has an egress port as counterpart. For this analysis we only need to consider incoming traffic.

  8. Frames of the same task scheduled sequentially on the time-line can be joined into a bigger virtual task to increase the performance of the feasibility test.

  9. Note that other tests with pseudo-polynomial complexity (Pellizzoni and Lipari 2004; Baruah et al. 1990) could be used instead, but these are only sufficient or deal with restricted task sets.

  10. By “arbitrary” we mean that the SMT solver will return the first valid solution that it finds which, depending on the implementation, is not chosen according to schedulability criteria but rather depends on the specific generic search mechanism of the solver.

  11. We thank Gurobi Optimization, Inc for their generous licensing support.

  12. This finding is reaffirmed in Meindl and Templ (2013), in which the authors present a detailed performance comparison of several commercial and open-source MIP solvers for a particular problem domain.

  13. This ratio is chosen as a representative figure based on the author’s experience. Note, however, that the evaluation and validity of the presented method is not bound to these values and can be generalized to any proportion between free and communicating tasks.

  14. The parameter specifies that after a certain threshold, nodes are to be compressed and written to disk instead of stored in memory (Gurobi Optimization 2014, p. 497).


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The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement no 610640 (DREAMS).

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Correspondence to Silviu S. Craciunas.

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This paper is an extended version of Craciunas and Serna Oliver (2014).

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Craciunas, S.S., Oliver, R.S. Combined task- and network-level scheduling for distributed time-triggered systems. Real-Time Syst 52, 161–200 (2016).

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