4,801 research outputs found
Modelling and Analysing Highly-Configurable Services
Since the emergence of XaaS and Cloud Computing paradigms, the
number and complexity of available services have been increasing
enormously. These services usually o er a plethora of con guration
options, which can even include additional services provided as
a bundled o er. In this scenario, usual tasks, such as description,
discovery and selection, become increasingly complex due to the
variability of the decision space. The notion of Highly-Con gurable
Service (HCS) has been coined to identify such group of services
that can be con gured and bundled together to perform demanding
computing tasks. In this paper we characterize HCSs by means of an
abstract model and a text-based, human-readable notation named
SYNOPSIS that facilitates the execution of various service tasks. In
particular, we validate the usefulness of our model when checking
the validity of HCSs descriptions in SYNOPSIS, as well as selecting
the optimal con guration with regards to user requirements and
preferences by providing a prototype implementation.Junta de Andalucía P12-TIC-1867Ministerio de Economía y Competitividad TIN2015-70560-RScience Foundation Ireland Research. Centre grant 13/RC/2094ERC Advanced Grant no. 29165
Automated analysis of feature models: Quo vadis?
Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186
Densifying the sparse cloud SimSaaS: The need of a synergy among agent-directed simulation, SimSaaS and HLA
Modelling & Simulation (M&S) is broadly used in real scenarios where making
physical modifications could be highly expensive. With the so-called Simulation
Software-as-a-Service (SimSaaS), researchers could take advantage of the huge
amount of resource that cloud computing provides. Even so, studying and
analysing a problem through simulation may need several simulation tools, hence
raising interoperability issues. Having this in mind, IEEE developed a standard
for interoperability among simulators named High Level Architecture (HLA).
Moreover, the multi-agent system approach has become recognised as a convenient
approach for modelling and simulating complex systems. Despite all the recent
works and acceptance of these technologies, there is still a great lack of work
regarding synergies among them. This paper shows by means of a literature
review this lack of work or, in other words, the sparse Cloud SimSaaS. The
literature review and the resulting taxonomy are the main contributions of this
paper, as they provide a research agenda illustrating future research
opportunities and trends
Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm
Industry 4.0 aims at achieving mass customization at a
mass production cost. A key component to realizing this is accurate
prediction of customer needs and wants, which is however a
challenging issue due to the lack of smart analytics tools. This
paper investigates this issue in depth and then develops a predictive
analytic framework for integrating cloud computing, big data
analysis, business informatics, communication technologies, and
digital industrial production systems. Computational intelligence
in the form of a cluster k-means approach is used to manage
relevant big data for feeding potential customer needs and wants
to smart designs for targeted productivity and customized mass
production. The identification of patterns from big data is achieved
with cluster k-means and with the selection of optimal attributes
using genetic algorithms. A car customization case study shows
how it may be applied and where to assign new clusters with
growing knowledge of customer needs and wants. This approach
offer a number of features suitable to smart design in realizing
Industry 4.0
FraudDroid: Automated Ad Fraud Detection for Android Apps
Although mobile ad frauds have been widespread, state-of-the-art approaches
in the literature have mainly focused on detecting the so-called static
placement frauds, where only a single UI state is involved and can be
identified based on static information such as the size or location of ad
views. Other types of fraud exist that involve multiple UI states and are
performed dynamically while users interact with the app. Such dynamic
interaction frauds, although now widely spread in apps, have not yet been
explored nor addressed in the literature. In this work, we investigate a wide
range of mobile ad frauds to provide a comprehensive taxonomy to the research
community. We then propose, FraudDroid, a novel hybrid approach to detect ad
frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI
state transition graphs and collects their associated runtime network traffics,
which are then leveraged to check against a set of heuristic-based rules for
identifying ad fraudulent behaviours. We show empirically that FraudDroid
detects ad frauds with a high precision (93%) and recall (92%). Experimental
results further show that FraudDroid is capable of detecting ad frauds across
the spectrum of fraud types. By analysing 12,000 ad-supported Android apps,
FraudDroid identified 335 cases of fraud associated with 20 ad networks that
are further confirmed to be true positive results and are shared with our
fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure
Flexible Decision Support in Dynamic Interorganizational Networks
An effective Decision Support System (DSS) should help its users improve decision-making in complex, information-rich, environments. We present a feature gap analysis that shows that current decision support technologies lack important qualities for a new generation of agile business models that require easy, temporary integration across organisational boundaries. We enumerate these qualities as DSS Desiderata, properties that can contribute both effectiveness and flexibility to users in such environments. To address this gap, we describe a new design approach that enables users to compose decision behaviours from separate, configurable components, and allows dynamic construction of analysis and modelling tools from small, single-purpose evaluator services. The result is what we call an “evaluator service network” that can easily be configured to test hypotheses and analyse the impact of various choices for elements of decision processes. We have implemented and tested this design in an interactive version of the MinneTAC trading agent, an agent designed for the Trading Agent Competition for Supply Chain Management
Migrating to the Cloud: a Software Product Line Based Analysis
Identifying which part of a local system should be migrated to a public Cloud environment is often a difficult and error prone process. With the significant (and increasing) number of commercial Cloud providers, choosing a provider whose capability best meets requirements is also often difficult. Most Cloud service providers offer large amounts of configurable resources, which can be combined in a number of different ways. in the case of small and medium companies, finding a suitable configuration with the minimum cost is often an essential requirement to migrate, or even to initiate the decision process for migration. We interpret this need as a problem associated with variability management and analysis. Variability techniques and models deal with large configuration spaces, and have been proposed previously to support configuration processes in industrial cases. Furthermore, this is a mature field which has a large catalog of analysis operations to extract valuable information in an automated way. Some of these operations can be used and tailored for Cloud environments. We focus in this work on Amazon Cloud services, primarily due to the large number of possible configurations available by this service provider and its popularity. Our approach can also be adapted to other providers offering similar capabilities
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