42 research outputs found
Stochastic models for quality of service of component connectors
The intensifying need for scalable software has motivated modular development and using systems distributed over networks to implement large-scale applications. In Service-oriented Computing, distributed services are composed to provide large-scale services with a specific functionality. In this way, reusability of existing services can be increased. However, due to the heterogeneity of distributed software systems, software composition is not easy and requires additional mechanisms to impose some form of a coordination on a distributed software system. Besides functional correctness, a composed service must satisfy various quantitative requirements for its clients, which are generically called its quality of service (QoS). Particularly, it is tricky to obtain the overall QoS of a composed service even if the QoS information of its constituent distributed services is given. In this thesis, we propose Stochastic Reo to specify software composition with QoS aspects and its compositional semantic models. They are also used as intermediate models to generate their corresponding stochastic models for practical analysis. Based on this, we have implemented the tool Reo2MC. Using Reo2MC, we have modeled and analyzed an industrial software, the ASK system. Its analysis results provided the best cost-effective resource utilization and some suggestions to improve the performance of the system.UBL - phd migration 201
Stochastic models for quality of service of component connectors
The intensifying need for scalable software has motivated modular development and using systems distributed over networks to implement large-scale applications. In Service-oriented Computing, distributed services are composed to provide large-scale services with a specific functionality. In this way, reusability of existing services can be increased. However, due to the heterogeneity of distributed software systems, software composition is not easy and requires additional mechanisms to impose some form of a coordination on a distributed software system. Besides functional correctness, a composed service must satisfy various quantitative requirements for its clients, which are generically called its quality of service (QoS). Particularly, it is tricky to obtain the overall QoS of a composed service even if the QoS information of its constituent distributed services is given. In this thesis, we propose Stochastic Reo to specify software composition with QoS aspects and its compositional semantic models. They are also used as intermediate models to generate their corresponding stochastic models for practical analysis. Based on this, we have implemented the tool Reo2MC. Using Reo2MC, we have modeled and analyzed an industrial software, the ASK system. Its analysis results provided the best cost-effective resource utilization and some suggestions to improve the performance of the system.UBL - phd migration 201
Parameter dependencies for reusable performance specifications of software components
To avoid design-related performance problems, model-driven performance prediction methods analyse the response times, throughputs, and resource utilizations of software architectures before and during implementation. This thesis proposes new modeling languages and according model transformations, which allow a reusable description of usage profile dependencies to the performance of software components. Predictions based on this new methods can support performance-related design decisions
Spatial representation for planning and executing robot behaviors in complex environments
Robots are already improving our well-being and productivity in
different applications such as industry, health-care and indoor
service applications. However, we are still far from developing (and
releasing) a fully functional robotic agent that can autonomously
survive in tasks that require human-level
cognitive capabilities. Robotic systems on the market, in fact, are
designed to address specific applications, and can only run
pre-defined behaviors to robustly repeat few tasks (e.g., assembling
objects parts, vacuum cleaning). They internal representation of the
world is usually constrained to the task they are performing, and
does not allows for generalization to other
scenarios. Unfortunately, such a paradigm only apply to a very
limited set of domains, where the environment can be assumed to be
static, and its dynamics can be handled before
deployment. Additionally, robots configured in this way will
eventually fail if their "handcrafted'' representation of the
environment does not match the external world.
Hence, to enable more sophisticated cognitive skills, we investigate
how to design robots to properly represent the environment and
behave accordingly. To this end, we formalize a representation of
the environment that enhances the robot spatial knowledge to
explicitly include a representation of its own actions. Spatial
knowledge constitutes the core of the robot understanding of the
environment, however it is not sufficient to represent what the
robot is capable to do in it. To overcome such a limitation, we
formalize SK4R, a spatial knowledge representation for robots which
enhances spatial knowledge with a novel and "functional"
point of view that explicitly models robot actions. To this end, we
exploit the concept of affordances, introduced to express
opportunities (actions) that objects offer to an agent. To encode
affordances within SK4R, we define the "affordance
semantics" of actions that is used to annotate an environment, and
to represent to which extent robot actions support goal-oriented
behaviors.
We demonstrate the benefits of a functional representation of the
environment in multiple robotic scenarios that traverse and
contribute different research topics relating to: robot knowledge
representations, social robotics, multi-robot systems and robot
learning and planning. We show how a domain-specific representation,
that explicitly encodes affordance semantics, provides the robot
with a more concrete understanding of the environment and of the
effects that its actions have on it. The goal of our work is to
design an agent that will no longer execute an action, because of
mere pre-defined routine, rather, it will execute an actions because
it "knows'' that the resulting state leads one step closer to
success in its task
Simulation and Verification in a Process Calculus for Spatially-Explicit Ecological Models
We propose PALPS, a Process Algebra with Locations for Population Systems. PALPS allows us to produce spatially-explicit individual-based ecological models and to reason about their behavior. PALPS has two abstraction levels: At the first level, we may define the behavior of an individual of a population and, at the second level, we may specify a system as the collection of individuals of various species located in space. In PALPS, the individuals move through their life cycle while changing their location and interact with each other in various ways such as predation, infection or mating. Furthermore, we propose a translation of a subset of PALPS into the probabilistic model checker PRISM. We illustrate our framework via models of dispersal in metapopulations and by applying PRISM on PALPS models for verifying temporal logic properties and conducting reachability and steady-state analysis
Biomodelkit - a framework for modular biomodel-engineering
Otto-von-Guericke-Universität Magdeburg, Fakultät für Naturwissenschaften, Dissertation, 2017von Dipl.-Ing. Mary-Ann BlätkeLiteraturverzeichnis: Seite [177]-18
Modelling Biological Systems From Molecular Interactions to Population Dynamics
Biological systems are examples of complex systems, which consist of several interacting components. Understanding the behaviour of such systems requires a multidisciplinary approach that encompasses biology, mathematics, computer science, physiscs and chemistry. New research areas are emerging as the result of this multidisciplinarity, such as bioinformatics, systems biology and computational biology. Computer science plays an important role in the newly emerging research areas by offerring techniques, algorithms, languages and software to solve research problems efficiently. On the other hand, the efforts to solve these research problems stimulate the development of new and better computer science techniques, algorithms, languages and software.
This thesis describes our approach in modelling biological systems as a way to better understand their complex behaviours. Our approach is based on the Calculi of Looping Sequences, a class of formalisms originally developed to model biological systems involving cells and their membrane-based structures. We choose Stochastic CLS and Spatial CLS, two variants of the calculi that support quantitative analysis of the model, and define an approach that support simulation, statistical model-checking and visualisation as analysis techniques. Moreover, we found out that this class of formalisms can be easily extended to model population dynamics of animals, a kind of biological systems that does not involve membrane-based structures