Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies

Grid is an infrastructure that involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid applications often involve large amounts of data and/or computing resources that require secure resource sharing across organizational boundaries. This makes Grid application management and deployment a complex undertaking. Grid middlewares provide users with seamless computing ability and uniform access to resources in the heterogeneous Grid environment. Several software toolkits and systems have been developed, most of which are results of academic research projects, all over the world. This chapter will focus on four of these middlewares--UNICORE, Globus, Legion and Gridbus. It also presents our implementation of a resource broker for UNICORE as this functionality was not supported in it. A comparison of these systems on the basis of the architecture, implementation model and several other features is included.Comment: 19 pages, 10 figure

An Agent Approach to Spatial Information Grid Architecture Design

Spatial information grid (SIG) is a spatial information infrastructure that has the capability of providing services on-demand. SIG is a distributed network environment, which links spatial data resources, computing resources, storage resources, software, tools and users. SIG can integrate massive distributed heterogeneous spatial information resources, provides uniform management and process, and, furthermore, coordinate different resources to complete large-scale and complex spatial tasks and applications. In this paper, agent technology is adopted to construct a SIG framework, which contains three layers: users/applications layer, agent services layer and information layer. Different applications can get their spatial information via agent services, and agent services make the procedure of navigating and accessing spatial information transparent to users. Also, the implementation issues of the framework are discussed in detail, including Geo-Agents, an agent-based distributed GIS system, spatial information management, collaboration and parallel mechanism, load control strategy, and a sample

Large vario-scale datasets

In Chapter 3 the focus was on vario-scale data structure description. This was extended in Chapter 4, where generating better content for this structure was investigated. It showed how the structure has been developed and used in practice, and current technical limitations. One of them is processing really massive dataset with records in order of millions which do not fit in the main memory of computer. It is a notorious and challenging problem. This is especially true in the case of map generalization, where the relationships between (adjacent) features in the map must be considered. Therefore, this chapter presents our solution for automated generalization in vario-scale structure based on the idea of subdividing the workload according to a multi-level structure of the space, allowing parallel processing. More specifically: Section 5.1 specifies our goal. Section 5.2 presents related work and other options to handle large datasets. Section 5.3 explains the principles of our method in more detail. In Section 5.4 modifications of the process specific for road network generalization are introduced. Statistics and a test of real dataset with more than 800 thousand objects are given in Section 5.5, followed by conclusions and the future work related to processing large datasets in Section 5.6

Infrastructures and services for remote sensing data production management across multiple satellite data centers

With the number of satellite sensors and date centers being increased continuously, it is becoming a trend to manage and process massive remote sensing data from multiple distributed sources. However, the combination of multiple satellite data centers for massive remote sensing (RS) data collaborative processing still faces many challenges. In order to reduce the huge amounts of data migration and improve the efficiency of multi-datacenter collaborative process, this paper presents the infrastructures and services of the data management as well as workflow management for massive remote sensing data production. A dynamic data scheduling strategy was employed to reduce the duplication of data request and data processing. And by combining the remote sensing spatial metadata repositories and Gfarm grid file system, the unified management of the raw data, intermediate products and final products were achieved in the co-processing. In addition, multi-level task order repositories and workflow templates were used to construct the production workflow automatically. With the help of specific heuristic scheduling rules, the production tasks were executed quickly. Ultimately, the Multi-datacenter Collaborative Process System (MDCPS) were implemented for large-scale remote sensing data production based on the effective management of data and workflow. As a consequence, the performance of MDCPS in experiments environment showed that those strategies could significantly enhance the efficiency of co-processing across multiple data centers

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Large Scale In Silico Screening on Grid Infrastructures

Large-scale grid infrastructures for in silico drug discovery open opportunities of particular interest to neglected and emerging diseases. In 2005 and 2006, we have been able to deploy large scale in silico docking within the framework of the WISDOM initiative against Malaria and Avian Flu requiring about 105 years of CPU on the EGEE, Auvergrid and TWGrid infrastructures. These achievements demonstrated the relevance of large-scale grid infrastructures for the virtual screening by molecular docking. This also allowed evaluating the performances of the grid infrastructures and to identify specific issues raised by large-scale deployment.Comment: 14 pages, 2 figures, 2 tables, The Third International Life Science Grid Workshop, LSGrid 2006, Yokohama, Japan, 13-14 october 2006, to appear in the proceeding