Cloud Service Selection System Approach based on QoS Model: A Systematic Review

The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects

CDOXplorer: Simulation-based genetic optimization of software deployment and reconfiguration in the cloud

Migrating existing enterprise software to cloud platforms involves the comparison of various cloud deployment options (CDOs). A CDO comprises a combination of a specific cloud environment, deployment architecture, and runtime reconfiguration rules for dynamic resource scaling. Our simulator CDOSim can evaluate CDOs, e.g., regarding response times and costs. However, the design space to be searched for well-suited solutions is very large. In this paper, we approach this optimization problem with the novel genetic algorithm CDOXplorer. It uses techniques of the search-based software engineering field and simulations with CDOSim to assess the fitness of CDOs. An experimental evaluation that employs, among others, the cloud environments Amazon EC2 and Microsoft Windows Azure, shows that CDOXplorer can find solutions that surpass those of other state-of-the-art techniques by up to 60\%. Our experiment code and data and an implementation of CDOXplorer are available as open source software

Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs

With the growing number of Web services on the internet, there is a challenge to select the best Web service which can offer more quality-of-service (QoS) values at the lowest price. Another challenge is the uncertainty of QoS values over time due to the unpredictable nature of the internet. In this paper, we modify the interval data envelopment analysis (DEA) models [Wang, Greatbanks and Yang (2005)] for QoS-aware Web service selection considering the uncertainty of QoS attributes in the presence of desirable and undesirable factors. We conduct a set of experiments using a synthesized dataset to show the capabilities of the proposed models. The experimental results show that the correlation between the proposed models and the interval DEA models is significant. Also, the proposed models provide almost robust results and represent more stable behavior than the interval DEA models against QoS variations. Finally, we demonstrate the usefulness of the proposed models for QoS-aware Web service composition. Experimental results indicate that the proposed models significantly improve the fitness of the resultant compositions when they filter out unsatisfactory candidate services for each abstract service in the preprocessing phase. These models help users to select the best possible cloud service considering the dynamic internet environment and they help service providers to improve their Web services in the marke

Evolutionary Inference from Admixed Genomes: Implications of Hybridization for Biodiversity Dynamics and Conservation

Hybridization as a macroevolutionary mechanism has been historically underappreciated among vertebrate biologists. Yet, the advent and subsequent proliferation of next-generation sequencing methods has increasingly shown hybridization to be a pervasive agent influencing evolution in many branches of the Tree of Life (to include ancestral hominids). Despite this, the dynamics of hybridization with regards to speciation and extinction remain poorly understood. To this end, I here examine the role of hybridization in the context of historical divergence and contemporary decline of several threatened and endangered North American taxa, with the goal to illuminate implications of hybridization for promoting—or impeding—population persistence in a shifting adaptive landscape. Chapter I employed population genomic approaches to examine potential effects of habitat modification on species boundary stability in co-occurring endemic fishes of the Colorado River basin (Gila robusta and G. cypha). Results showed how one potential outcome of hybridization might drive species decline: via a breakdown in selection against interspecific heterozygotes and subsequent genetic erosion of parental species. Chapter II explored long-term contributions of hybridization in an evolutionarily recent species complex (Gila) using a combination of phylogenomic and phylogeographic modelling approaches. Massively parallel computational methods were developed (and so deployed) to categorize sources of phylogenetic discordance as drivers of systematic bias among a panel of species tree inference algorithms. Contrary to past evidence, we found that hypotheses of hybrid origin (excluding one notable example) were instead explained by gene-tree discordance driven by a rapid radiation. Chapter III examined patterns of local ancestry in the endangered red wolf genome (Canis rufus) – a controversial taxon of a long-standing debate about the origin of the species. Analyses show how pervasive autosomal introgression served to mask signatures of prior isolation—in turn misleading analyses that led the species to be interpreted as of recent hybrid origin. Analyses also showed how recombination interacts with selection to create a non-random, structured genomic landscape of ancestries with, in the case of the red wolf, the ‘original’ species tree being retained only in low-recombination ‘refugia’ of the X chromosome. The final three chapters present bioinformatic software that I developed for my dissertation research to facilitate molecular approaches and analyses presented in Chapters I–III. Chapter IV details an in-silico method for optimizing similar genomic methods as used herein (RADseq of reduced representation libraries) for other non-model organisms. Chapter V describes a method for parsing genomic datasets for elements of interest, either as a filtering mechanism for downstream analysis, or as a precursor to targeted-enrichment reduced-representation genomic sequencing. Chapter VI presents a rapid algorithm for the definition of a ‘most parsimonious’ set of recombinational breakpoints in genomic datasets, as a method promoting local ancestry analyses as utilized in Chapter III. My three case studies and accompanying software promote three trajectories in modern hybridization research: How does hybridization impact short-term population persistence? How does hybridization drive macroevolutionary trends? and How do outcomes of hybridization vary in the genome? In so doing, my research promotes a deeper understanding of the role that hybridization has and will continue to play in governing the evolutionary fates of lineages at both contemporary and historic timescales

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Dynamics of Hybrid Zones at a Continental Scale

Hybridization has traditionally been viewed as a happenstance that negatively impacts populations, but is now recognized as an important evolutionary mechanism that can substantially impact the evolutionary trajectories of gene pools, influence adaptive capacity, and contravene or reinforce divergence. Physiographic processes are important drivers of dispersal, alternately funneling populations into isolation, promoting divergence, or facilitating secondary contact of diverged populations, increasing the potential for hybridization. In North America, glacial-interglacial cycles and geomorphological changes have provided a dynamic backdrop over the last two million years that promoted such oscillations of population contraction and expansion. These biogeographic processes have resulted in regional hybrid zones where hybridization spans generations Herein, I explored hybrid zones in two species complexes of reptiles across Eastern, Central, and Southwestern North America. Hybrid zones can influence evolutionary trajectories, and understanding the mechanisms underlying their formation is important for defining appropriate management strategies and can help avoid actions that would inadvertently lead to new hybrid zones. Chapter I assessed differential introgression in a complex of terrestrial turtles, the American Box Turtles (Terrapene spp.), from a contemporary hybrid zone in the southeastern United States. Transcriptomic loci were correlated with environmental predictors to evaluate mechanisms engendering maladapted hybrids and adaptive introgression. Selection against hybrids predominated for inter-specifics but directional introgression did so in conspecifics. Outlier loci also primarily correlated with temperature, reflecting the temperature dependency of ectotherms and underscoring their vulnerability to climate change. Chapter II performed a robust assessment of recently developed machine learning (M-L) approaches to delimit four Terrapene species and evaluate the impact of data filtering and M-L parameter choices. Parameter selections were varied to determine their effects in resolving clusters. The results provide necessary recommendations on using M-L for species delimitation in species complexes defined by secondary contact. These data exemplify usage of M-L software in a phylogenetically complex group. Chapter III describes an R package to visualize some of the analyses from Chapter I. Current software to generate genomic clines does not include functions to visualize the results. Thus, I wrote an API (application programming interface) that does so and also performs other genomic and geographic cline-related tasks. Chapter IV examines historical and contemporary phylogeographic patterns in the Massasaugas (Sistrurus spp.), a type of dwarf rattlesnake found across the Southwest and Central Great Plains. In the Southwest, S. tergeminus tergeminus and S. t. edwardsii putatively diverged in the absence of strong physiographic barriers and physical glaciers, suggesting primary divergence. In contrast, a disjunct population of S. t. tergeminus in Missouri reflects potentially historical secondary contact with S. catenatus. These taxa represent contrasting examples of divergence resulting from alternative phylogeographic processes and contextualizes evolutionarily significant and management units. Combined, the four chapters present population genomic data to elucidate impacts of phylogeographic processes on hybrid zones at a continental scale. The data will promote effective conservation management strategies, as many species in the focal regions have been affected by anthropogenic pressures. In this sense, the results can be extrapolated to co-distributed taxa with similar phylogeographic histories

Using Ontologies and Intelligent Systems for Traffic Accident Assistance in Vehicular Environments

A pesar de que las medidas de seguridad en los sistemas de transporte cada vez son mayores, el aumento progresivo del número de vehículos que circulan por las ciudades y carreteras en todo el mundo aumenta, sin duda, la probabilidad de que ocurra un accidente. En este tipo de situaciones, el tiempo de respuesta de los servicios de emergencia es crucial, ya que está demostrado que cuanto menor sea el tiempo transcurrido entre el accidente y la atención hospitalaria de los heridos, mayores son sus probabilidades de supervivencia. Las redes vehiculares permiten la comunicación entre los vehículos, así como la comunicación entre los vehículos y la infraestructura [4], lo que da lugar a una plétora de nuevas aplicaciones y servicios en el entorno vehicular. Centrándonos en las aplicaciones relacionadas con la seguridad vial, mediante este tipo de comunicaciones, los vehículos podrían informar en caso de accidente al resto de vehículos (evitando así colisiones en cadena) y a los servicios de emergencia (dando información precisa y rápida, lo que sin duda facilitaría las tareas de rescate). Uno de los aspectos importantes a determinar sería saber qué información se debe enviar, quién será capaz de recibirla, y cómo actuar una vez recibida. Actualmente los vehículos disponen de una serie de sensores que les permiten obtener información sobre ellos mismos (velocidad, posición, estado de los sistemas de seguridad, número de ocupantes del vehículo, etc.), y sobre su entorno (información meteorológica, estado de la calzada, luminosidad, etc.). En caso de accidente, toda esa información puede ser estructurada y enviada a los servicios de emergencia para que éstos adecúen el rescate a las características específicas y la gravedad del accidente, actuando en consecuencia. Por otro lado, para que la información enviada por los vehículos accidentados pueda llegar correctamente a los servicios de emergencias, es necesario disponer de una infraestructura capaz de dar cobertura a todos los vehículos que circulan por una determinada área. Puesto que la instalación y el mantenimiento de dicha infraestructura conllevan un elevado coste, sería conveniente proponer, implementar y evaluar técnicas consistentes en dar cobertura a todos los vehículos, reduciendo el coste total de la infraestructura. Finalmente, una vez que la información ha sido recibida por las autoridades, es necesario elaborar un plan de actuación eficaz, que permita el rápido rescate de los heridos. Hay que tener en cuenta que, cuando ocurre un accidente de tráfico, el tiempo de personación de los servicios de emergencia en el lugar del accidente puede suponer la diferencia entre que los heridos sobrevivan o fallezcan. Además, es importante conocer si la calle o carretera por la que circulaban los vehículos accidentados ha dejado de ser transitable para el resto de vehículos, y en ese caso, activar los mecanismos necesarios que permitan evitar los atascos asociados. En esta Tesis, se pretende gestionar adecuadamente estas situaciones adversas, distribuyendo el tráfico de manera inteligente para reducir el tiempo de llegada de los servicios de emergencia al lugar del accidente, evitando además posibles atascos.Barrachina Villalba, J. (2014). Using Ontologies and Intelligent Systems for Traffic Accident Assistance in Vehicular Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/39004TESI

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Evolutionary design of deep neural networks

Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of the topology of artificial neural networks, with most works focusing on very simple architectures. However, times have changed, and nowadays convolutional neural networks are the industry and academia standard for solving a variety of problems, many of which remained unsolved before the discovery of this kind of networks. Convolutional neural networks involve complex topologies, and the manual design of these topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to use neuroevolution in order to evolve the architecture of convolutional neural networks. To do so, we have decided to try two different techniques: genetic algorithms and grammatical evolution. We have implemented a niching scheme for preserving the genetic diversity, in order to ease the construction of ensembles of neural networks. These techniques have been validated against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%, and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275. Both results have proven very competitive when compared with the state of the art. Also, in all cases, ensembles have proven to perform better than individual models. Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced in 2017, which includes more samples and a set of letters for character recognition. Results have shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures can be reused across domains with similar characteristics. In summary, neuroevolution is an effective approach for automatically designing topologies for convolutional neural networks. However, it still remains as an unexplored field due to hardware limitations. Current advances, however, should constitute the fuel that empowers the emergence of this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917. This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Francisco Javier Segovia Pérez.- Vocal: Simon Luca