9,071 research outputs found
Discrete-time dynamic modeling for software and services composition as an extension of the Markov chain approach
Discrete Time Markov Chains (DTMCs) and Continuous Time Markov Chains (CTMCs) are often used to model various types of phenomena, such as, for example, the behavior of software products. In that case, Markov chains are widely used to describe possible time-varying behavior of “self-adaptive” software systems, where the transition from one state to another represents alternative choices at the software code level, taken according to a certain probability distribution. From a control-theoretical standpoint, some of these probabilities can be interpreted as control signals and others can just be observed. However, the translation between a DTMC or CTMC model and a corresponding first principle model, that can be used to design a control system is not immediate. This paper investigates a possible solution for translating a CTMC model into a dynamic system, with focus on the control of computing systems components. Notice that DTMC models can be translated as well, providing additional information
A compositional method for reliability analysis of workflows affected by multiple failure modes
We focus on reliability analysis for systems designed as workflow based compositions of components. Components are characterized by their failure profiles, which take into account possible multiple failure modes. A compositional calculus is provided to evaluate the failure profile of a composite system, given failure profiles of the components. The calculus is described as a syntax-driven procedure that synthesizes a workflows failure profile. The method is viewed as a design-time aid that can help software engineers reason about systems reliability in the early stage of development. A simple case study is presented to illustrate the proposed approach
Forward modelling of magnetic anomalies in archaeological geophysics: A new software tool
We present a new software tool for the analysis and interpretation of archaeological magnetic anomalies, based on classical algorithms of forward modelling and a technique of error assessment. The proposed methodology allows us determining geometry, physical properties, and location of buried archaeological features, as well as the occurrence of fires or other historical events that may have affected the observed magnetic signal. In our approach, the acquisition of total field data, usually in a regular grid arrangement, their reduction to archaeological magnetic anomalies, and the subsequent computer-assisted structural interpretation are accomplished according to a specific workflow that conserves the physical meaning of the magnetic anomalies and excludes any kind of data processing that may change the nature of the anomaly field. In particular, the reduction of the observed total field data to magnetic anomalies is performed subtracting a polynomial representation of the regional field on the basis of a rigorous criterion that separates archaeological anomalies from geological (crustal) contributions, while procedures like line levelling or some kinds of data filtering are excluded to ensure a strict correspondence between magnetic data and the real distribution of magnetisation in the ground
Attractions between charged colloids at water interfaces
The effective potential between charged colloids trapped at water interfaces
is analyzed. It consists of a repulsive electrostatic and an attractive
capillary part which asymptotically both show dipole--like behavior. For
sufficiently large colloid charges, the capillary attraction dominates at large
separations.
The total effective potential exhibits a minimum at intermediate separations
if the Debye screening length of water and the colloid radius are of comparable
size.Comment: 8 pages, 1 figure, revised version (one paragraph added) accepted in
JPC
A Metric Encoding for Bounded Model Checking (extended version)
In Bounded Model Checking both the system model and the checked property are
translated into a Boolean formula to be analyzed by a SAT-solver. We introduce
a new encoding technique which is particularly optimized for managing
quantitative future and past metric temporal operators, typically found in
properties of hard real time systems. The encoding is simple and intuitive in
principle, but it is made more complex by the presence, typical of the Bounded
Model Checking technique, of backward and forward loops used to represent an
ultimately periodic infinite domain by a finite structure. We report and
comment on the new encoding technique and on an extensive set of experiments
carried out to assess its feasibility and effectiveness
MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation
An architectural approach to self-adaptive systems involves runtime change of
system configuration (i.e., the system's components, their bindings and
operational parameters) and behaviour update (i.e., component orchestration).
Thus, dynamic reconfiguration and discrete event control theory are at the
heart of architectural adaptation. Although controlling configuration and
behaviour at runtime has been discussed and applied to architectural
adaptation, architectures for self-adaptive systems often compound these two
aspects reducing the potential for adaptability. In this paper we propose a
reference architecture that allows for coordinated yet transparent and
independent adaptation of system configuration and behaviour
Run-time efficient probabilistic model checking
Since the inception of discontinuous Galerkin (DG) methods for elliptic problems, there has existed a question of whether DG methods can be made more computationally efficient than continuous Galerkin (CG) methods. Fewer degrees of freedom, approximation properties for elliptic problems together with the number of optimization techniques, such as static condensation, available within CG framework made it challenging for DG methods to be competitive until recently. However, with the introduction of a static-condensation-amenable DG method—the hybridizable discontinuous Galerkin (HDG) method—it has become possible to perform a realistic comparison of CG and HDG methods when applied to elliptic problems. In this work, we extend upon an earlier 2D comparative study, providing numerical results and discussion of the CG and HDG method performance in three dimensions. The comparison categories covered include steady-state elliptic and time-dependent parabolic problems, various element types and serial and parallel performance. The postprocessing technique, which allows for superconvergence in the HDG case, is also discussed. Depending on the direct linear system solver used and the type of the problem (steady-state vs. time-dependent) in question the HDG method either outperforms or demonstrates a comparable performance when compared with the CG method. The HDG method however falls behind performance-wise when the iterative solver is used, which indicates the need for an effective preconditioning strategy for the method
A formal approach to adaptive software: continuous assurance of non-functional requirements
Abstract
Modern software systems are increasingly requested to be adaptive to changes in the environment in which they are embedded. Moreover, adaptation often needs to be performed automatically, through self-managed reactions enacted by the application at run time. Off-line, human-driven changes should be requested only if self-adaptation cannot be achieved successfully. To support this kind of autonomic behavior, software systems must be empowered by a rich run-time support that can monitor the relevant phenomena of the surrounding environment to detect changes, analyze the data collected to understand the possible consequences of changes, reason about the ability of the application to continue to provide the required service, and finally react if an adaptation is needed. This paper focuses on non-functional requirements, which constitute an essential component of the quality that modern software systems need to exhibit. Although the proposed approach is quite general, it is mainly exemplified in the paper in the context of service-oriented systems, where the quality of service (QoS) is regulated by contractual obligations between the application provider and its clients. We analyze the case where an application, exported as a service, is built as a composition of other services. Non-functional requirements—such as reliability and performance—heavily depend on the environment in which the application is embedded. Thus changes in the environment may ultimately adversely affect QoS satisfaction. We illustrate an approach and support tools that enable a holistic view of the design and run-time management of adaptive software systems. The approach is based on formal (probabilistic) models that are used at design time to reason about dependability of the application in quantitative terms. Models continue to exist at run time to enable continuous verification and detection of changes that require adaptation.</jats:p
Supporting self-adaptation via quantitative verification and sensitivity analysis at run time
Modern software-intensive systems often interact with an environment whose behavior changes over time, often unpredictably. The occurrence of changes may jeopardize their ability to meet the desired requirements. It is therefore desirable to design software in a way that it can self-adapt to the occurrence of changes with limited, or even without, human intervention. Self-adaptation can be achieved by bringing software models and model checking to run time, to support perpetual automatic reasoning about changes. Once a change is detected, the system itself can predict if requirements violations may occur and enable appropriate counter-actions. However, existing mainstream model checking techniques and tools were not conceived for run-time usage; hence they hardly meet the constraints imposed by on-the-fly analysis in terms of execution time and memory usage. This paper addresses this issue and focuses on perpetual satisfaction of non-functional requirements, such as reliability or energy consumption. Its main contribution is the description of a mathematical framework for run-time efficient probabilistic model checking. Our approach statically generates a set of verification conditions that can be efficiently evaluated at run time as soon as changes occur. The proposed approach also supports sensitivity analysis, which enables reasoning about the effects of changes and can drive effective adaptation strategies
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