Efficient runtime quantitative verification using caching, lookahead, and nearly-optimal reconfiguration

Gerasimou, Simos; Călinescu, Radu; Banks, Alec

doi:10.1145/2593929.2593932

Cited by 40 publications

(53 citation statements)

References 27 publications

(39 reference statements)

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“…PRISM parametric Markov models are increasingly used to model software architectures, e.g. in the emerging field of self-adaptive software [34]- [37]. Another threat might occur if our method generated a Pareto front that approached the actual Pareto front insufficiently, producing only low quality designs or designs that did not satisfy the required quality constraints.…”

Section: Case Studiesmentioning

confidence: 99%

Designing Robust Software Systems through Parametric Markov Chain Synthesis

Călinescu

Češka

Gerasimou

et al. 2017

2017 IEEE International Conference on Software Architecture (ICSA)

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Abstract-We present a method for the synthesis of software system designs that satisfy strict quality requirements, are Paretooptimal with respect to a set of quality optimisation criteria, and are robust to variations in the system parameters. To this end, we model the design space of the system under development as a parametric continuous-time Markov chain (pCTMC) with discrete and continuous parameters that correspond to alternative system architectures and to the ranges of possible values for configuration parameters, respectively. Given this pCTMC and required tolerance levels for the configuration parameters, our method produces a sensitivity-aware Pareto-optimal set of designs, which allows the modeller to inspect the ranges of quality attributes induced by these tolerances, thus enabling the effective selection of robust designs. Through application to two systems from different domains, we demonstrate the ability of our method to synthesise robust designs with a wide spectrum of useful tradeoffs between quality attributes and sensitivity.Keywords-software performance and reliability engineering; probabilistic model synthesis; multi-objective optimisation I. INTRODUCTION Evaluating the performance, reliability and other quality attributes of alternative designs is essential for the cost-effective engineering of software [1], [2]. Delaying this evaluation until integration or system testing can greatly increase engineering costs, as defects identified late in the development lifecycle require much more effort to fix [3]. A common method to avoid this delay uses model-based simulation [4] or formal verification [5] to predict the quality attributes of alternative designs. Models that meet the quality requirements of the system under development are then used as a basis for its implementation. Models based on queueing networks [6], probabilistic models [2], [5] and timed automata [7] have been used for this purpose, together with tools for their simulation (e.g. Palladio [8]) and verification (e.g. PRISM [9]). Furthermore, recently proposed approaches automate the search for suitable designs. Probabilistic model repair [10], [11] automatically modifies the transition probabilities of Markov models that violate a quality requirement, generating new models that meet the requirement. Precise parameter synthesis [12] identifies transition rates that enable continuous Markov models to satisfy a quality requirement or to optimise a quality attribute of the modelled system. Finally, probabilistic model synthesis [13] starts from a design template that captures alternative system designs, and uses multiobjective optimisation to generate the Pareto-optimal set of Markov models associated with the quality requirements of the system.

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Section: Case Studiesmentioning

confidence: 99%

Designing Robust Software Systems through Parametric Markov Chain Synthesis

Călinescu

Češka

Gerasimou

et al. 2017

2017 IEEE International Conference on Software Architecture (ICSA)

Self Cite

View full text Add to dashboard Cite

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“…Given a Markovian model of the analysed system, probabilistic model checking tools, such as PRISM (Kwiatkowska et al, 2011) and MRMC (Katoen et al, 2011), use efficient symbolic algorithms to efficiently examine its entire state space, producing results that are guaranteed to be correct. The technique has been successfully used to analyse nonfunctional properties of systems ranging from cloud infrastructure (Calinescu et al, 2012) and service-based systems (Calinescu et al, 2013) to unmanned vehicles (Gerasimou et al, 2014). Typical properties that can be established using the probabilistic model checking include: 'What is the probability that the agent will safely reach the goal area?'…”

Section: Preliminariesmentioning

confidence: 99%

Assured Reinforcement Learning with Formally Verified Abstract Policies

Călinescu¹,

Kudenko²,

Banks³

2017

Proceedings of the 9th International Conference on Agents and Artificial Intelligence

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Markov Decision Processes. Abstract:We present a new reinforcement learning (RL) approach that enables an autonomous agent to solve decision making problems under constraints. Our assured reinforcement learning approach models the uncertain environment as a high-level, abstract Markov decision process (AMDP), and uses probabilistic model checking to establish AMDP policies that satisfy a set of constraints defined in probabilistic temporal logic. These formally verified abstract policies are then used to restrict the RL agent's exploration of the solution space so as to avoid constraint violations. We validate our RL approach by using it to develop autonomous agents for a flag-collection navigation task and an assisted-living planning problem.

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“…The formal technique presented in this paper is called continual verification and is proposed to ensure reliability and performance requirements of safety-critical systems, even when they evolve. The run-time quantitative verification (RQV) technique has been proposed in [21] to make systems self-adaptive to changing workloads, environments, and goals. The approach is targeted to self-adaptive systems used in safety-critical and business critical applications, characterized by the need to comply with strict non-functional requirements.…”

Section: Modeling Systems Goals and Requirementsmentioning

confidence: 99%

Run-time architectural modeling for future internet applications

Mongiello

Colucci

Vogli

et al. 2016

Complex Intell. Syst.

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Interoperability, flexibility, and adaptability are key requirements of future internet applications. Convergence of contents, services, things, and networks can be the cornerstone to fulfill these requirements. Such rich and composite sources of data and processing capabilities call for a structured and formal approach that manages and capitalizes heterogeneous information. This paper proposes an approach to the run-time composition of software system architectures, aimed at addressing goals revealed at runtime. The approach is grounded on a graph model characterized by two control levels: a metamodeling and an instantiation level. At metamodeling level, the graph describes facts that may occur in a scenario of interest, processes triggered by facts, and technologies available to execute processes. The actual occurrence of facts, together with the deriving processes and technologies, is managed at instantiation level, with reference to an application-specific model. In particular, the paper proposes an algorithm that determines an optimal way to manage a change in the run-time environment, by finding a minimum cost path in the model. The usefulness of the proposed approach and its applicability to actual scenarios have been validated in an example smart home environment.

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Efficient runtime quantitative verification using caching, lookahead, and nearly-optimal reconfiguration

Cited by 40 publications

References 27 publications

Designing Robust Software Systems through Parametric Markov Chain Synthesis

Designing Robust Software Systems through Parametric Markov Chain Synthesis

Assured Reinforcement Learning with Formally Verified Abstract Policies

Run-time architectural modeling for future internet applications

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