Alessandra Melani

Response-Time Analysis of Conditional DAG Tasks in Multiprocessor Systems

Different task models have been proposed to represent the parallel structure of real-time tasks executing on manycore platforms: fork/join, synchronous parallel, DAG-based, etc. Despite different schedulability tests and resource augmentation bounds are available for these task systems, we experience difficulties in applying such results to real application scenarios, where the execution flow of parallel tasks is characterized by multiple (and nested) conditional structures. When a conditional branch drives the number and size of sub-jobs to spawn, it is hard to decide which execution path to select for modeling the worst-case scenario. To circumvent this problem, we integrate control flow information in the task model, considering conditional parallel tasks (cp-tasks) represented by DAGs composed of both precedence and conditional edges. For this task model, we identify meaningful parameters that characterize the schedulability of the system, and derive efficient algorithms to compute them. A response time analysis based on these parameters is then presented for different scheduling policies. A set of simulations shows that the proposed approach allows efficiently checking the schedulability of the addressed systems, and that it significantly tightens the schedulability analysis of non-conditional (e.g., Classic DAG) tasks over existing approaches.

Exact Interference of Adaptive Variable-Rate Tasks under Fixed-Priority Scheduling

Engine control applications require the execution of tasks activated in relation to specific system variables, such as the crankshaft rotation angle. To prevent possible overload conditions at high rotation speeds, such tasks are designed to vary their functionality (hence their computational requirements) for different speed ranges. Modeling and analyzing such a type of tasks poses new research challenges in the schedulability analysis that are now being addressed in the real-time literature. This paper advances the state of the art by presenting a method for computing the exact worst-case interference of such adaptive variable-rate tasks under fixed priority scheduling, enabling a tight analysis and design of engine control applications.

Timing characterization of OpenMP4 tasking model

OpenMP is increasingly being supported by the newest high-end embedded many-core processors. Despite the lack of any notion of real-time execution, the latest specification of OpenMP (v4.0) introduces a tasking model that resembles the way real-time embedded applications are modeled and designed, i.e., as a set of periodic task graphs. This makes OpenMP4 a convenient candidate to be adopted in future real-time systems. However, OpenMP4 incorporates as well features to guarantee backward compatibility with previous versions that limit its practical usability in real-time systems. The most notable example is the distinction between tied and untied tasks. Tied tasks force all parts of a task to be executed on the same thread that started the execution, whereas a suspended untied task is allowed to resume execution on a different thread. Moreover, tied tasks are forbidden to be scheduled in threads in which other non-descendant tied tasks are suspended. As a result, the execution model of tied tasks, which is the default model in OpenMP to simplify the coexistence with legacy constructs, clearly restricts the performance and has serious implications on the response time analysis of OpenMP4 applications, making difficult to adopt it in real-time environments. In this paper, we revisit OpenMP design choices, introducing timing predictability as a new and key metric of interest. Our first results confirm that even if tied tasks can be timing analyzed, the quality of the analysis is much worse than with untied tasks. We thus reason about the benefits of using untied tasks, deriving a response time analysis for this model, and so allowing OpenMP4 untied model to be applied to real-time systems.

Response-time analysis of DAG tasks under fixed priority scheduling with limited preemptions

Limited preemptive (LP) scheduling has been demonstrated to effectively improve the schedulability of fully preemptive (FP) and fully non-preemptive (FNP) paradigms. On one side, LP reduces the preemption related overheads of FP; on the other side, it restricts the blocking effects of FNP. However, LP has been applied to multi-core scenarios only when completely sequential task systems are considered. This paper extends the current state-of-the-art response time analysis for global fixed priority scheduling with fixed preemption points by deriving a new response time analysis for DAG-based task-sets.

Memory-processor co-scheduling in fixed priority systems

A major obstacle towards the adoption of multi-core platforms for real-time systems is given by the difficulties in characterizing the interference due to memory contention. The simple fact that multiple cores may simultaneously access shared memory and communication resources introduces a significant pessimism in the timing and schedulability analysis. To counter this problem, predictable execution models have been proposed splitting task executions into two consecutive phases: a memory phase in which the required instruction and data are pre-fetched to local memory (M-phase), and an execution phase in which the task is executed with no memory contention (C-phase). Decoupling memory and execution phases not only simplifies the timing analysis, but it also allows a more efficient (and predictable) pipelining of memory and execution phases through proper co-scheduling algorithms. In this paper, we take a further step towards the design of smart co-scheduling algorithms for sporadic real-time tasks complying with the M/C (memory-computation) model. We provide a theoretical framework that aims at tightly characterizing the schedulability improvement obtainable with the adopted M/C task model on a single-core systems. We identify a tight critical instant for M/C tasks scheduled with fixed priority, providing an exact response-time analysis with pseudo-polynomial complexity. We show in our experiments that a significant schedulability improvement may be obtained with respect to classic execution models, placing an important building block towards the design of more efficient partitioned multi-core systems.

Optimal Design for Reservation Servers under Shared Resources

Modularity and hierarchical-based design are crucial features that need to be supported in complex embedded systems characterized by multiple applications with timing requirements.Resource reservation is a powerful scheduling mechanism for achieving such goals and providing temporal isolation among different real-time applications. When different applications share mutually exclusive resources, a precise feasibility analysis can still be performed in isolation, using specific resource access protocols, taking into account only the application features and the reservation parameters. This paper presents a methodology for selecting the parameters of each reservation in order to guarantee the feasibility of the served applications and minimize the required bandwidth.

Hard Constant Bandwidth Server: Comprehensive formulation and critical scenarios

The Constant Bandwidth Server (CBS) is one of the most used algorithms for implementing resource reservation upon deadline-based schedulers. Although many CBS variants are available in the literature, no proper formalization has been proposed for the CBS in the context of hard reservations, where it is essential to guarantee a bounded-delay service across applications. Existing formulations are affected by a problem that can expose the system to dangerous deadline misses in the presence of blocking. This paper analyzes such a problem and presents a comprehensive and consistent formulation of the CBS for hard reservation scenarios. An overview of the contexts in which a hard CBS can be applied is also provided, focusing on the impact that previous formulations can have on schedulability, when used in conjunction with specific resource sharing protocols or other scheduling mechanisms that may cause a server to block.

Solving ambiguities in MDS relative localization

Monitoring teams of mobile nodes is becoming crucial in a growing number of activities. Where it is not possible to use fixed references or external measurements, one of the possible solutions involves deriving relative positions from local communication. Well-known techniques such as trilateration and multilateration exist to locate a single node although such methods are not designed to locate entire teams. The technique of Multidimensional Scaling (MDS), however, allow us to find the relative coordinates of entire teams starting from the knowledge of the inter-node distances. However, like every relative-localization technique, it suffers from geometrical ambiguities including rotation, translation, and flip. In this work, we address such ambiguities by exploiting the node velocities to correlate the relative maps at two consecutive instants. In particular, we introduce a new version of MDS, called enhanced Multidimensional Scaling (eMDS), which is able to handle these types of ambiguities. The effectiveness of our localization technique is then validated by a set of simulation experiments and our results are compared against existing approaches.

A static scheduling approach to enable safety-critical OpenMP applications

Parallel computation is fundamental to satisfy the performance requirements of advanced safety-critical systems. OpenMP is a good candidate to exploit the performance opportunities of parallel platforms. However, safety-critical systems are often based on static allocation strategies, whereas current OpenMP implementations are based on dynamic schedulers. This paper proposes two OpenMP-compliant static allocation approaches: an optimal but costly approach based on an ILP formulation, and a sub-optimal but tractable approach that computes a worst-case makespan bound close to the optimal one.

Probabilistic Deadline Miss Analysis of Real-Time Systems Using Regenerative Transient Analysis

Quantitative evaluation of real-time systems demands for analysis frameworks that go beyond worst-case assumptions, since some parameters could be better characterized by a random variable than by a deterministic value. On the one hand, this opens notable issues on the safe estimation of probabilistic parameters starting from real measurements. On the other hand, this also requires modeling formalisms and solution techniques that can encompass stochastic temporal parameters with a non-Markovian distribution, thus breaking the limits of Markovian approaches. We propose a framework for modeling and evaluating periodic real-time tasks that may have a probabilistic Worst Case Execution Time (pWCET) and are scheduled by a fixed-priority non-preemptive policy. The methodology leverages the Extreme Value Theory (EVT) for the derivation of the pWCET of tasks by means of Erlang distributions. Evaluation is performed through regenerative transient analysis based on the method of stochastic state classes, supporting the derivation of quantitative measures on the time by which a deadline is missed. The approach is experimented on a case study including tasks with a pWCET derived from benchmarks and real system execution.