7,617 research outputs found
Model Checking Probabilistic Real-Time Properties for Service-Oriented Systems with Service Level Agreements
The assurance of quality of service properties is an important aspect of
service-oriented software engineering. Notations for so-called service level
agreements (SLAs), such as the Web Service Level Agreement (WSLA) language,
provide a formal syntax to specify such assurances in terms of (legally
binding) contracts between a service provider and a customer. On the other
hand, formal methods for verification of probabilistic real-time behavior have
reached a level of expressiveness and efficiency which allows to apply them in
real-world scenarios. In this paper, we suggest to employ the recently
introduced model of Interval Probabilistic Timed Automata (IPTA) for formal
verification of QoS properties of service-oriented systems. Specifically, we
show that IPTA in contrast to Probabilistic Timed Automata (PTA) are able to
capture the guarantees specified in SLAs directly. A particular challenge in
the analysis of IPTA is the fact that their naive semantics usually yields an
infinite set of states and infinitely-branching transitions. However, using
symbolic representations, IPTA can be analyzed rather efficiently. We have
developed the first implementation of an IPTA model checker by extending the
PRISM tool and show that model checking IPTA is only slightly more expensive
than model checking comparable PTA.Comment: In Proceedings INFINITY 2011, arXiv:1111.267
Big Data Analytics for QoS Prediction Through Probabilistic Model Checking
As competitiveness increases, being able to guaranting QoS of delivered
services is key for business success. It is thus of paramount importance the
ability to continuously monitor the workflow providing a service and to timely
recognize breaches in the agreed QoS level. The ideal condition would be the
possibility to anticipate, thus predict, a breach and operate to avoid it, or
at least to mitigate its effects. In this paper we propose a model checking
based approach to predict QoS of a formally described process. The continous
model checking is enabled by the usage of a parametrized model of the monitored
system, where the actual value of parameters is continuously evaluated and
updated by means of big data tools. The paper also describes a prototype
implementation of the approach and shows its usage in a case study.Comment: EDCC-2014, BIG4CIP-2014, Big Data Analytics, QoS Prediction, Model
Checking, SLA compliance monitorin
Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking
We investigate the suitability of statistical model checking techniques for
analysing quantitative properties of software product line models with
probabilistic aspects. For this purpose, we enrich the feature-oriented
language FLan with action rates, which specify the likelihood of exhibiting
particular behaviour or of installing features at a specific moment or in a
specific order. The enriched language (called PFLan) allows us to specify
models of software product lines with probabilistic configurations and
behaviour, e.g. by considering a PFLan semantics based on discrete-time Markov
chains. The Maude implementation of PFLan is combined with the distributed
statistical model checker MultiVeStA to perform quantitative analyses of a
simple product line case study. The presented analyses include the likelihood
of certain behaviour of interest (e.g. product malfunctioning) and the expected
average cost of products.Comment: In Proceedings FMSPLE 2015, arXiv:1504.0301
Runtime model checking for sla compliance monitoring and qos prediction
Sophisticated workflows, where multiple parties cooperate towards the achievement of a shared goal are today common. In a market-oriented setup, it is key that effective mechanisms be available for providing accountability within the business process. The challenge is to be able to continuously monitor the progress of the business process, ideally,anticipating contract breaches and triggering corrective actions. In this paper we propose a novel QoS prediction approach which combines runtime monitoring of the real system with probabilistic model-checking on a parametric system model. To cope with the huge amount of data generated by the monitored system, while ensuring that parameters are extracted in a timing fashion, we relied on big data analytics solutions. To validate the proposed approach, a prototype of the QoS prediction framework has been developed, and an experimental campaign has been conducted with respect to a case study in the field of Smart Grids
Towards critical event monitoring, detection and prediction for self-adaptive future Internet applications
The Future Internet (FI) will be composed of a multitude of diverse types of services that offer flexible, remote access to software features, content, computing resources, and middleware solutions through different cloud delivery models, such as IaaS, PaaS and SaaS. Ultimately, this means that loosely coupled Internet services will form a comprehensive base for developing value added applications in an agile way. Unlike traditional application development, which uses computing resources and software components under local administrative control, FI applications will thus strongly depend on third-party services. To maintain their quality of service, those applications therefore need to dynamically and autonomously adapt to an unprecedented level of changes that may occur during runtime. In this paper, we present our recent experiences on monitoring, detection, and prediction of critical events for both software services and multimedia applications. Based on these findings we introduce potential directions for future research on self-adaptive FI applications, bringing together those research directions
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A survey on online monitoring approaches of computer-based systems
This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use
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