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

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

Using Graph Transformation Systems to Specify and Verify Data Abstractions

This paper proposes an approach for the specification of the behavior of software components that implement data abstractions. By generalizing the approach of behavior models using graph transformation, we provide a concise specification for data abstractions that describes the relationship between the internal state, represented in a canonical form, and the observers of the component. Graph transformation also supports the generation of behavior models that are amenable to verification. To this end, we provide a translation approach into an LTL model on which we can express useful properties that can be model-checked with a SAT solver

Development and Validation of a Spike Detection and Classification Algorithm Aimed at Implementation on Hardware Devices

Neurons cultured in vitro on MicroElectrode Array (MEA) devices connect to each other, forming a network. To study electrophysiological activity and long term plasticity effects, long period recording and spike sorter methods are needed. Therefore, on-line and real time analysis, optimization of memory use and data transmission rate improvement become necessary. We developed an algorithm for amplitude-threshold spikes detection, whose performances were verified with (a) statistical analysis on both simulated and real signal and (b) Big O Notation. Moreover, we developed a PCA-hierarchical classifier, evaluated on simulated and real signal. Finally we proposed a spike detection hardware design on FPGA, whose feasibility was verified in terms of CLBs number, memory occupation and temporal requirements; once realized, it will be able to execute on-line detection and real time waveform analysis, reducing data storage problems

Syntax-driven program verification of matching logic properties

We describe a novel approach to program verification and its application to verification of C programs, where properties are expressed in matching logic. The general approach is syntax-directed: semantic rules, expressed according to Knuths attribute grammars, specify how verification conditions can be computed. Evaluation is performed by interplaying attribute computation and propagation through the syntax tree with invocation of a solver of logic formulae. The benefit of a general syntax-driven approach is that it provides a reusable reference scheme for implementing verifiers for different languages. We show that the instantiation of a general approach to a specific language does not penalize the efficiency of the resulting verifier. This is done by comparing our C verifier for matching logic with an existing tool for the same programming language and logic. A further key advantage of the syntax-directed approach is that it can be the starting point for an incremental verifier -- which is our long-term research target