El nuevo estándar internacional OGC-WMTS. Oportunidades de aplicación y rendimiento versus OGC-WMS

El uso masivo de servicios Web Map Service (WMS) y la sencillez del estándar ha comportado algunos problemas debido a que la mayor parte de implementaciones tratan las peticiones de manera independiente sin aprovechar las respuestas anteriores para despachar las nuevas rápidamente. Algunos fabricantes vieron estas ineficiencias y aparecieron diversas estrategias que discretizan el espacio. En 2007, el Open Geospatial Consortium (OGC) inició un estudio para estandarizar y unificar todos estos productos que se concretó, en 2010, en el nuevo estándar Web Map Tile Service (WMTS). El OGC-WMTS describe una geometría de malla regular de teselas para un conjunto de escalas conocidas; introduciendo la capacidad de obtener una tesela de manera compatible con el uso de los mecanismos de caché en Internet. Sin embargo, el WMTS podría no ser adecuado para todas las situaciones. Esta comunicación describe las principales características de WMTS y ahonda en las estrategias para soportar con éxito situaciones como el prerenderizado, los clusters de servidores de balanceo de carga de red y los servicios de mapas frecuentemente actualizados. También se discuten las posibles estrategias de mejora de rendimiento aprovechando los mecanismos de caducidad del HTTP y de notificaciones de actualización de contenido.The massive use of Web Map Service (WMS) and the simplicity of this standard has resulted in some problems due to the fact that most implementations deal with requests independently without taking advantage of previous responses to dispatch the new ones faster. Some manufacturers saw these inefficiencies and various strategies based on a discretized space were applied. In 2007, Open Geospatial Consortium (OGC) started a process to standardize and unify all these products that, in 2010, crystallized in the new Web Map Tile Service (WMTS) standard. OGC-WMTS describes a regular grid of tiles geometry for a known set of scales; introducing the ability to get a tile compatible with Internet caching mechanisms. However, WMTS may not be suitable for all situations. This paper describes the main features of WMTS and emphasizing in strategies to successfully withstand conditions such as pre-rendered, network load balanced services clusters and services with frequently updated maps. We also discuss possible strategies for performance improvement by taking advantage of HTTP expiration mechanisms and content update notifications

Management of Infrastructure For Water and Petroleum Demand in KSA By GIS

The purpose of this paper  is showing, how Geographical  Information Systems (GIS ) can be used to support infrastructure  planners and analyst  on water and petroleum demand of a local area in the Kingdom of Saudi Arabia (KSA). The first part of this work  discusses the issue of analysis, design and creating  the geodatabase system  of KSA land and infrastructure  using Stylus Studio XML editor, describing the components of the whole system of Subareas in Saudi  Arabia  affecting local infrastructure  planning and analyzing which include of specific  area and facilities management. The second part defines the creation of the GIS  application of the discussed field having the GIS  functions of the infrastructure discusses the  geodatabase of the application of GIS  In infrastructure   in Saudi Arabia districts. The third part defines the  results of the statistics analysis populations  in the Subareas, specify the relation between water resources and the elevations of subareas, the data of the layers of roads, railroads existing in Saudi Arabia  specially in the eastern area where most petroleum s wells are found.   Using Google earth to show the elevation of the subareas and the relation with the water resources. Three groups of GIS  forms was produced  they are the geodatabase of the Saudi Arabia (area, subareas and main cities) ,water resources  layers (water  in land , water  area and land cover ) , roads, railroads  and elevations layers. The main contribution in the paper, discussed the infrastructure and the  results of the statistics analysis populations  in the subareas, specify the relation between water resources and the elevations of subareas of the data layers of roads, railroads existing in Saudi Arabia, especially in the eastern area where most petroleum's wells are found production and exploration of  petroleum including the geodatabase of wells of petroleum distributed in Saudi Arabia  finding the locations using Google earth map, satellites to locate the areas of producing petroleum. Keywords: GIS, Water in land , Water area, Railroad, Elevation ,XML Schema

Load Index Metrics for an Optimized Management of Web Services: A Systematic Evaluation

The lack of precision to predict service performance through load indices may lead to wrong decisions regarding the use of web services, compromising service performance and raising platform cost unnecessarily. This paper presents experimental studies to qualify the behaviour of load indices in the web service context. The experiments consider three services that generate controlled and significant server demands, four levels of workload for each service and six distinct execution scenarios. The evaluation considers three relevant perspectives: the capability for representing recent workloads, the capability for predicting near-future performance and finally stability. Eight different load indices were analysed, including the JMX Average Time index (proposed in this paper) specifically designed to address the limitations of the other indices. A systematic approach is applied to evaluate the different load indices, considering a multiple linear regression model based on the stepwise-AIC method. The results show that the load indices studied represent the workload to some extent; however, in contrast to expectations, most of them do not exhibit a coherent correlation with service performance and this can result in stability problems. The JMX Average Time index is an exception, showing a stable behaviour which is tightly-coupled to the service runtime for all executions. Load indices are used to predict the service runtime and therefore their inappropriate use can lead to decisions that will impact negatively on both service performance and execution cost

Parallelization of web processing services on cloud computing: A case study of Geostatistical Methods

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.In the last decade the publication of geographic information has increased in Internet, especially with the emergence of new technologies to share information. This information requires the use of technologies of geoprocessing online that use new platforms such as Cloud Computing. This thesis work evaluates the parallelization of geoprocesses on the Cloud platform Amazon Web Service (AWS), through OGC Web Processing Services (WPS) using the 52North WPS framework. This evaluation is performed using a new implementation of a Geostatistical library in Java with parallelization capabilities. The geoprocessing is tested by incrementing the number of micro instances on the Cloud through GridGain technology. The Geostatistical library obtains similar interpolated values compared with the software ArcGIS. In the Inverse Distance Weight (IDW) and Radial Basis Functions (RBF) methods were not found differences. In the Ordinary and Universal Kriging methods differences have been found of 0.01% regarding the Root Mean Square (RMS) error.The parallelization process demonstrates that the duration of the interpolation decreases when the number of nodes increases. The duration behavior depends on the size of input dataset and the number of pixels to be interpolated. The maximum reduction in time was found with the largest configuration used in the research (1.000.000 of pixels and a dataset of 10.000 points). The execution time decreased in 83% working with 10 nodes in the Ordinary Kriging and IDW methods. However, the differences in duration working with 5 nodes and 10 nodes were not statistically significant. The reductions with 5 nodes were 72% and 71% in the Ordinary Kriging and IDW methods respectively. Finally, the experiments show that the geoprocessing on Cloud Computing is feasible using the WPS interface. The performance of the geostatistical methods deployed through the WPS services can improve by the parallelization technique. This thesis proves that the parallelization on the Cloud is viable using a Grid configuration. The evaluation also showed that parallelization of geoprocesses on the Cloud for academic purposes is inexpensive using Amazon AWS platform

Automatic Scaling Hadoop in the Cloud for Efficient Process of Big Geospatial Data

Efficient processing of big geospatial data is crucial for tackling global and regional challenges such as climate change and natural disasters, but it is challenging not only due to the massive data volume but also due to the intrinsic complexity and high dimensions of the geospatial datasets. While traditional computing infrastructure does not scale well with the rapidly increasing data volume, Hadoop has attracted increasing attention in geoscience communities for handling big geospatial data. Recently, many studies were carried out to investigate adopting Hadoop for processing big geospatial data, but how to adjust the computing resources to efficiently handle the dynamic geoprocessing workload was barely explored. To bridge this gap, we propose a novel framework to automatically scale the Hadoop cluster in the cloud environment to allocate the right amount of computing resources based on the dynamic geoprocessing workload. The framework and auto-scaling algorithms are introduced, and a prototype system was developed to demonstrate the feasibility and efficiency of the proposed scaling mechanism using Digital Elevation Model (DEM) interpolation as an example. Experimental results show that this auto-scaling framework could (1) significantly reduce the computing resource utilization (by 80% in our example) while delivering similar performance as a full-powered cluster; and (2) effectively handle the spike processing workload by automatically increasing the computing resources to ensure the processing is finished within an acceptable time. Such an auto-scaling approach provides a valuable reference to optimize the performance of geospatial applications to address data- and computational-intensity challenges in GIScience in a more cost-efficient manner