Giotto a time-triggered language for embedded programming

Weakly-hard systems are real-time systems that can tolerate occasional deadline misses in a bounded manner. Compared with traditional systems with hard deadline constraints, they provide more scheduling flexibility, and thus expand the design space for system configuration and reconfiguration. A key question for such a system is precisely to what degree it can tolerate deadline misses while still meeting its functional requirements. In this paper, we provide a formal treatment to the verification problem of a general class of weakly-hard systems. We discuss relaxation and over-approximation techniques for managing the complexity of reachability analysis, and develop algorithms based upon these for verifying the safety of weakly-hard systems. Experiments demonstrate the effectiveness of our approach in understanding the impact of and guiding the selection among different weakly-hard constraints.

https://par.nsf.gov/servlets/purl/10109979

Formal verification of weakly-hard systems

This paper deals with the real-time implementation of feedback controllers In particular, it provides an analysis of the stability property of closed-loop systems that include a controller that can sporadically miss deadlines In this context, the weakly hard m-K computational model has been widely adopted and researchers used it to design and verify controllers that are robust to deadline misses Rather than using the m-K model, we focus on another weakly-hard model, the number of consecutive deadline misses, showing a neat mathematical connection between real-time systems and control theory We formalise this connection using the joint spectral radius and we discuss how to prove stability guarantees on the combination of a controller (that is unaware of deadline misses) and its system-level implementation We apply the proposed verification procedure to a synthetic example and to an industrial case study

https://drops.dagstuhl.de/opus/volltexte/2020/12384/pdf/LIPIcs-ECRTS-2020-21.pdf/

Control System Stability under Consecutive Deadline Misses Constraints

Many cyber-physical applications including sensing and control operations can tolerate a certain degree of timing violations as long as the number of the violations are predictably bounded. The notion of weakly-hard real-time systems has been studied to capture this effect, but existing work reveals limitations for practical use due the restrictions imposed on timing model and the high complexity of analysis. In this paper, we propose a new job-class-level fixed-priority preemptive scheduler and its schedulability analysis framework for sporadic tasks with weakly-hard real-time constraints. Our proposed scheduler employs the meet-oriented classification of jobs of a task in order to reduce the worst-case temporal interference imposed on other tasks. Under this approach, each job is associated with a "job-class" that is determined by the number of deadlines previously met (with a bounded number of consecutively-missed deadlines). This approach also allows decomposing the complex weakly-hard schedulability problem into two sub-problems that are easier to solve: (1) analyzing the response time of a job with each job-class, which can be done by an extension of the existing task-level analysis, and (2) finding possible job-class patterns, which can be modeled as a simple reachability tree. Experimental results indicate that our scheduler outperforms prior work in terms of task schedulability and analysis time complexity. We have also implemented a prototype of a job-class-level scheduler in the Linux kernel running on Raspberry Pi with acceptably-small runtime overhead.

Job-Class-Level Fixed Priority Scheduling of Weakly-Hard Real-Time Systems

The real-time implementation of periodic controllers requires solving a co-design problem, in which the choice of the controller sampling period is a crucial element. Classic design techniques limit the period exploration to safe values, that guarantee the correct execution of the controller alongside the remaining real-time load, i.e., ensuring that the controller worst-case response time does not exceed its deadline. This paper presents DMAC: the first formally-grounded controller design strategy that explores shorter periods, thus explicitly taking into account the possibility of missing deadlines. The design leverages information about the probability that specific sub-sequences of deadline misses are experienced. The result is a fixed controller, that on average works as the ideal clairvoyant time-varying controller that possesses knowledge of deadline hits and misses. We obtain a safe estimate of the hit and miss events using the scenario theory, that allows us to provide probabilistic guarantees. The paper analyzes controllers implemented using the Logical Execution Time paradigm and three different strategies to handle deadline miss events: killing the job, letting the job continue but skipping the next activation, and letting the job continue using a limited queue of jobs. Our experimental results show that our design proposal – i.e., that exploring the space where deadline can be missed and handled with different strategies – greatly outperforms classical control design techniques.

/pdf/dmac-deadline-miss-aware-control-3vlr5c2btx.pdf

DMAC: Deadline-Miss-Aware Control

Most works in schedulability analysis theory are based on the assumption that constraints on the performance of the application can be expressed by a very limited set of timing constraints (often simply hard deadlines) on a task model. This model is insufficient to represent a large number of systems in which deadlines can be missed, or in which late task responses affect the performance, but not the correctness of the application. For systems with a possible temporary overload, models like the m-K deadline have been proposed in the past. However, the m-K model has several limitations since it does not consider the state of the system and is largely unaware of the way in which the performance is affected by deadline misses (except for critical failures). In this paper, we present a state-based representation of the evolution of a system with respect to each deadline hit or miss event. Our representation is much more general (while hopefully concise enough) to represent the evolution in time of the performance of time-sensitive systems with possible time overloads. We provide the theoretical foundations for our model and also show an application to a simple system to give examples of the state representations and their use. 2012 ACM Subject Classification Computer systems organization → Embedded software

Beyond the Weakly Hard Model: Measuring the Performance Cost of Deadline Misses.

Beyond the Weakly Hard Model: Measuring the Performance Cost of Deadline Misses (Artifact).

In this paper, we revisit the problem of learning a stabilizing controller from a finite number of demonstrations by an expert. By focusing on feedback linearizable systems, we show how to combine expert demonstrations into a stabilizing controller, provided that demonstrations are sufficiently long and there are at least $n+1$ of them, where $n$ is the number of states of the system being controlled. The results are experimentally demonstrated on a CrazyFlie 2.0 quadrotor.

Watch and Learn: Learning to control feedback linearizable systems from expert demonstrations

This paper constitutes an industrial experience re-port about the use of data center optimization strategies for softwarized network services within the Vodafone resource man-agement unit for the management of virtualized network infras-tructures. The problem of optimum virtual machine placement as needed in the network operator context is detailed, and different solving strategies are proposed and discussed, including heuristics based on genetic optimization. Also, experimental results are presented that compare these strategies with one another from the standpoint of optimality and execution times, using a data-set made of some of the real problems that had to be solved in the past few years by Vodafone, in order to optimize its capacity planning decisions. The presented experimental results highlight that an optimum solver leads to excessively high computation times for large problems, whereas simple heuristics may exhibit significant loss in optimality at reduced computation times. Genetic optimization, on the other hand, constitutes a very interesting trade-off between these two extremes. The data-set used for the provided results is published under an open data license, for possible reuse in future research works on the topic.

Optimum VM Placement for NFV Infrastructures

This paper provides an overview of the research activities in the area of Artificial Intelligence applied to Network Function Virtualization (NFV), carried out by Vodafone, jointly with Scuola Superiore Sant’Anna of Pisa. Artificial Intelligence techniques have been used on system-level data gathered from Virtual Machines (VMs) composing a multitude of Virtualized Network Functions (VNFs), to tackle a number of problems: from traffic forecasting for capacity planning and optimization, to the off-line analysis of the daily behavior of metrics to identify possible anomalous patterns, to a Near Real time (NRT) approach for metric prediction and anomaly detection, so to trigger prompt reaction of operators of the infrastructure and services. These problems become particularly challenging in the context of the Vodafone infrastructure, spanning across several data centers for NFV throughout a dozen European Countries.

Luigi Pannocchi

Papers

Beyond the Weakly Hard Model: Measuring the Performance Cost of Deadline Misses.

Beyond the Weakly Hard Model: Measuring the Performance Cost of Deadline Misses (Artifact).

Watch and Learn: Learning to control feedback linearizable systems from expert demonstrations

Optimum VM Placement for NFV Infrastructures

Artificial Intelligence in virtualized networks: a journey