A system that needs to interact with the physical world in a timely manner is called a real-time system. When such a system is composed of multiple subsystems, or nodes, each of which is a geographically separate system, such a system of systems is called a distributed real-time system. The computation at each node must adhere to the timing requirements, and the connecting communication channels must never cause delays that trigger further timing violations. In this paper, we introduce Deterministic Network Channels, a network construct using Time Sensitive Networking QoS mechanisms that add reliable and deterministic communication for distributed tasks. Introducing such network channels as a construct allows designers to focus on higher-level primitives when building distributed systems. We describe our reference implementation and evaluate it by extending Timed C with network channels. Building on this, we also perform a thorough performance evaluation to determine practical bounds for both Linux and TSN under heavy workloads and adverse network conditions to show how the proposed reference implementation performs in real-world scenarios. In our tests, we can synchronize two separate machines running commercial off-the-shelf hardware to within 15μs of each other under severe internal and external interference.

A number of modeling approaches combining dataflow and finite-state machines (FSMs) have been proposed to capture applications that combine streaming data with finite control. FSM-based scenario-aware dataflow (FSM-SADF) is such an FSM/dataflow hybrid that occupies a sweet spot in the tradeoff between analyzability and expressiveness. However, the model suffers from compactness issues when the number of scenarios increases. This hampers its use in analysis of applications exposing high levels of data-dependent dynamics. In this paper, we address this problem by combining parameterized dataflow with finite control of FSM-SADF. We refer to the generalization as FSM-based parameterized SADF (FSM-πSADF). We introduce the formal semantics of the model, in terms of maxplus algebra and in particular max-plus automata. Thereafter, by leveraging the existing results of FSM-SADF, we propose a worst-case performance analysis framework for FSM-πSADF. We show that by using FSM-πSADF and its analysis framework, one can, unlike with FSM-SADF, compactly capture streaming applications exhibiting high levels of data-dependent dynamics in presence of finite control. Furthermore, we show that for practical models our analysis typically yields tighter bounds on worst-case performance indicators such as throughput and latency than the existing techniques based on conservative FSM-SADF modeling (if such modeling can be applied at all). We evaluate our approach on a realistic case-study from the multimedia domain.

Dynamic dataflow models of computation (MoCs) have been introduced to provide designers with enough expressive power to capture increasing levels of dynamism in modern streaming applications. Among dynamic dataflow MoCs, parametrized dataflow MoCs hold an important place as they integrate dynamic parameters and run-time adaptation of parameters in a structured way. In this work, we analyze the temporal behaviour of an important class of parametrized dataflow MoCs based on synchronous dataflow (SDF). We refer to such models as SDF-based parametrized dataflow (SDF-PDF). We show that our analysis allows to derive tighter worst-case throughput guarantees than the existing techniques. To achieve this, we introduce the (max,+) algebraic semantics of the model. Thereafter, we model run-time parameter adaptation using the theory of (max,+) automata, where the maximum cycle mean (MCM) analysis of the (max,+) automaton structure immediately yields the worst-case throughput value. We evaluate our approach on a representative case study from the multimedia domain.

The FSM-based scenario-aware data ow (FSM-SADF) model of computation has been introduced to facilitate the analysis of dynamic streaming applications. FSM-SADF interprets application's execution as an execution of a sequence of static modes of operation called scenarios. Each scenario is modeled using a synchronous data ow (SDF) graph (SDFG), while a finite-state machine (FSM) is used to encode scenario occurrence patterns. However, FSM-SADF can precisely capture only those dynamic applications whose behaviors can be abstracted into a reasonably sized set of scenarios (coarse-grained dynamism). Nevertheless, in many cases, the application may exhibit thousands or even millions of behaviours (fine-grained dynamism). In this work, we generalize the concept of FSM-SADF to one that is able to model dynamic applications exhibiting fine-grained dynamism. We achieve this by applying parametrization to the FSM-SADF's base model, i.e. SDF, and defining scenarios over parametrized SDFGs. We refer to the extension as parametrized FSM-SADF (PFSM-SADF). Thereafter, we present a novel and a fully parametric analysis technique that allows us to derive tight worst-case performance (throughput and latency) guarantees for PFSM-SADF specifications. We evaluate our approach on a realistic case-study from the multimedia domain.

Multiprocessor systems are increasingly being used in real-time computing, and much research has been done on schedulability analysis of these systems. However, current schedulability analyses have only limited support for job-level parallelism (JLP): jobs are typically restricted to a simple parallel structure, and malleable jobs, where the number of processors assigned to a job is dynamic, is not widely supported. This paper presents a framework for analyzing systems with malleable jobs of an arbitrary parallel structure. A fair intra-job scheduler is assumed, allowing the state of a job to be represented by a scalar and its parallel structure to be modeled as a function. It is demonstrated that jobs executing their worst-case computations do not necessarily constitute a worst-case scenario with respect to schedulability. This implies that exact schedulability analysis will not be sustainable. Upper bounds on interference and demand are developed. This framework is then used to construct a pessimistic, but sustainable schedulability test for systems scheduled with EDF. The EDF test has poor worst-case performance, but does allow schedulability analysis for a class of systems for which no other analysis currently exists. We believe the framework itself could also be used to construct analyses with better performance.

A control system for a 7-joint manipulator with three intersecting shoulder axes is presented. A singularity avoidance scheme is developed, where the redundancy is resolved through a position transformation. The internal and external motions are decoupled using nonlinear feedback linearization in an augmented task space. It is then possible to use linear controllers both for the internal nullspace and for the end-effector coordinates. It is shown how optimal control theory can be applied to coordinate the motion in the arm, and how to do this so that the input generalized forces are minimized. This gives a coordination of internal and external motion which resembles the motion of the human arm; this is demonstrated by simulating a throwing motion with the manipulator.>