65,806 research outputs found
Self-Modeling Based Diagnosis of Software-Defined Networks
Networks built using SDN (Software-Defined Networks) and NFV (Network
Functions Virtualization) approaches are expected to face several challenges
such as scalability, robustness and resiliency. In this paper, we propose a
self-modeling based diagnosis to enable resilient networks in the context of
SDN and NFV. We focus on solving two major problems: On the one hand, we lack
today of a model or template that describes the managed elements in the context
of SDN and NFV. On the other hand, the highly dynamic networks enabled by the
softwarisation require the generation at runtime of a diagnosis model from
which the root causes can be identified. In this paper, we propose finer
granular templates that do not only model network nodes but also their
sub-components for a more detailed diagnosis suitable in the SDN and NFV
context. In addition, we specify and validate a self-modeling based diagnosis
using Bayesian Networks. This approach differs from the state of the art in the
discovery of network and service dependencies at run-time and the building of
the diagnosis model of any SDN infrastructure using our templates
Composition and Self-Adaptation of Service-Based Systems with Feature Models
The adoption of mechanisms for reusing software in pervasive systems has not yet become standard practice. This is because the use of pre-existing software requires the selection, composition and adaptation of prefabricated software parts, as well as the management of some complex problems such as guaranteeing high levels of efficiency and safety in critical domains. In addition to the wide variety of services, pervasive systems are composed of many networked heterogeneous devices with embedded software. In this work, we promote the safe reuse of services in service-based systems using two complementary technologies, Service-Oriented Architecture and Software Product Lines. In order to do this, we extend both the service discovery and composition processes defined in the DAMASCo framework, which currently does not deal with the service variability that constitutes pervasive systems. We use feature models to represent the variability and to self-adapt the services during the composition in a safe way taking context changes into consideration. We illustrate our proposal with a case study related to the driving domain of an Intelligent Transportation System, handling the context information of the environment.Work partially supported by the projects TIN2008-05932,
TIN2008-01942, TIN2012-35669, TIN2012-34840 and CSD2007-0004 funded by
Spanish Ministry of Economy and Competitiveness and FEDER; P09-TIC-05231 and
P11-TIC-7659 funded by Andalusian Government; and FP7-317731 funded by EU. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec
Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies
Robots are increasingly entering uncertain and unstructured environments.
Within these, robots are bound to face unexpected external disturbances like
accidental human or tool collisions. Robots must develop the capacity to
respond to unexpected events. That is not only identifying the sudden anomaly,
but also deciding how to handle it. In this work, we contribute a recovery
policy that allows a robot to recovery from various anomalous scenarios across
different tasks and conditions in a consistent and robust fashion. The system
organizes tasks as a sequence of nodes composed of internal modules such as
motion generation and introspection. When an introspection module flags an
anomaly, the recovery strategy is triggered and reverts the task execution by
selecting a target node as a function of a state dependency chart. The new
skill allows the robot to overcome the effects of the external disturbance and
conclude the task. Our system recovers from accidental human and tool
collisions in a number of tasks. Of particular importance is the fact that we
test the robustness of the recovery system by triggering anomalies at each node
in the task graph showing robust recovery everywhere in the task. We also
trigger multiple and repeated anomalies at each of the nodes of the task
showing that the recovery system can consistently recover anywhere in the
presence of strong and pervasive anomalous conditions. Robust recovery systems
will be key enablers for long-term autonomy in robot systems. Supplemental info
including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl
Context modeling and constraints binding in web service business processes
Context awareness is a principle used in pervasive services
applications to enhance their exibility and adaptability to
changing conditions and dynamic environments. Ontologies
provide a suitable framework for context modeling and reasoning. We develop a context model for executable business processes { captured as an ontology for the web services domain. A web service description is attached to a service context profile, which is bound to the context ontology. Context instances can be generated dynamically at services runtime and are bound to context constraint services. Constraint services facilitate both setting up constraint properties and constraint checkers, which determine the dynamic validity of context instances. Data collectors focus on capturing context instances. Runtime integration of both constraint services and data collectors permit the business process to achieve dynamic business goals
Context for goal-level product line derivation
Product line engineering aims at developing a family of products and facilitating the derivation of product variants from it. Context can be a main factor in determining what products to derive. Yet, there is gap in incorporating context with variability models. We advocate that, in the first place, variability originates from human intentions and choices even before software systems are constructed, and context influences variability at this intentional level before the functional one. Thus, we propose to analyze variability at an early phase of analysis adopting the intentional ontology of goal models, and studying how context can influence such variability. Below we present a classification of variation points on goal models, analyze their relation with context, and show the process of constructing and maintaining the models. Our approach is illustrated with an example of a smarthome for people with dementia problems. 1
McRunjob: A High Energy Physics Workflow Planner for Grid Production Processing
McRunjob is a powerful grid workflow manager used to manage the generation of
large numbers of production processing jobs in High Energy Physics. In use at
both the DZero and CMS experiments, McRunjob has been used to manage large
Monte Carlo production processing since 1999 and is being extended to uses in
regular production processing for analysis and reconstruction. Described at
CHEP 2001, McRunjob converts core metadata into jobs submittable in a variety
of environments. The powerful core metadata description language includes
methods for converting the metadata into persistent forms, job descriptions,
multi-step workflows, and data provenance information. The language features
allow for structure in the metadata by including full expressions, namespaces,
functional dependencies, site specific parameters in a grid environment, and
ontological definitions. It also has simple control structures for
parallelization of large jobs. McRunjob features a modular design which allows
for easy expansion to new job description languages or new application level
tasks.Comment: CHEP 2003 serial number TUCT00
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