Martina Maggio

Carlo A. Furia, Alberto Leva, Martina Maggio et al.

This paper presents a novel methodology to develop scheduling algorithms. The scheduling problem is phrased as a control problem, and control-theoretical techniques are used to design a scheduling algorithm that meets specific requirements. Unlike most approaches to feedback scheduling, where a controller integrates a "basic" scheduling algorithm and dynamically tunes its parameters and hence its performances, our methodology essentially reduces the design of a scheduling algorithm to the synthesis of a controller that closes the feedback loop. This approach allows the re-use of control-theoretical techniques to design efficient scheduling algorithms; it frames and solves the scheduling problem in a general setting; and it can naturally tackle certain peculiar requirements such as robustness and dynamic performance tuning. A few experiments demonstrate the feasibility of the approach on a real-time benchmark.

Rieke de Maeyer, Holger Hermanns, Martina Maggio

A hard real-time system cannot miss any deadline. A weakly-hard real-time system, on the contrary, is designed to tolerate a specific number of deadline misses. For instance, the AnyMiss(2, 300) weakly-hard constraint stipulates that in every window of 300 consecutive jobs, at most 2 deadlines are missed. The weakly-hard model is the state-of-the-art for industrial dependability-by-design of control systems that tolerate deterministic failures. Weakly-hard constraints correspond to regular languages. The size of the minimal finite state machine that recognizes whether a string satisfies the constraint (about 45k states for AnyMiss(2, 300)) is a notorious impediment for the verification of control system properties. This paper discusses an over-approximation of the language that allows us to provide sound safety guarantees for control systems under deadline misses that would be out of reach using the minimal finite state machine. We present a compressed language acceptor and prove that it simulates the original finite state machine. We study language cardinality properties, and report on empirical results that show how the new acceptor can be embedded in the control design workflow, leading to verifying safety for systems for which the state-of-the-art tools do not provide answers.

Marc Seidel, Martina Maggio, Frank Allgöwer

Deadline misses are more common in real-world systems than one may expect. The weakly-hard task model has become a standard abstraction to describe and analyze how often these misses occur, and has been especially used in control applications. Most existing control approaches check whether a controller manages to stabilize the system it controls when its implementation occasionally misses deadlines. However, they usually do not incorporate deadline-overrun knowledge during the controller synthesis process. In this paper, we present a framework that explicitly integrates weakly-hard constraints into the control design. Our method supports various overrun handling strategies and guarantees stability and performance under weakly-hard constraints. We validate the synthesized controllers on a Furuta pendulum, a representative control benchmark. The results show that constraint-aware controllers significantly outperform traditional designs, demonstrating the benefits of proactive and informed synthesis for overrun-aware real-time control.

Alexandru Iosup, Xiaoyun Zhu, Arif Merchant et al.

Cloud applications today deliver an increasingly larger portion of the Information and Communication Technology (ICT) services. To address the scale, growth, and reliability of cloud applications, self-aware management and scheduling are becoming commonplace. How are they used in practice? In this chapter, we propose a conceptual framework for analyzing state-of-the-art self-awareness approaches used in the context of cloud applications. We map important applications corresponding to popular and emerging application domains to this conceptual framework, and compare the practical characteristics, benefits, and drawbacks of self-awareness approaches. Last, we propose a roadmap for addressing open challenges in self-aware cloud and datacenter applications.