Geographical information systems are ideal candidates for the application of parallel programming techniques, mainly because they usually handle large data sets. To help us deal with complex calculations over such data sets, we investigated the performance constraints of a classic master–worker parallel paradigm over a message-passing communication model. To this end, we present a new approach that employs an external database in order to improve the calculation–communication overlap, thus reducing the idle times for the worker processes. The presented approach is implemented as part of a parallel radio-coverage prediction tool for the Geographic Resources Analysis Support System GRASS environment. The prediction calculation employs digital elevation models and land-usage data in order to analyze the radio coverage of a geographical area. We provide an extended analysis of the experimental results, which are based on real data from an Long Term Evolution LTE network currently deployed in Slovenia. Based on the results of the experiments, which were performed on a computer cluster, the new approach exhibits better scalability than the traditional master–worker approach. We successfully tackled real-world-sized data sets, while greatly reducing the processing time and saturating the hardware utilization.
Some years ago, grid computing received the attention of the research community as a way of accessing the extra computational power needed for the spatial analysis of large data sets @cite_36 @cite_43 @cite_42 . However, several obstacles are still preventing this technology from being more widely used. Namely, its adoption requires not only hardware and software compromises with respect to the involved parts, but also a behavioral change at the human level @cite_36 .
@cite_36 @cite_43 @cite_42
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High performance computing has undergone a radical transformation during the past decade. Though monolithic supercomputers continue to be built with significantly increased computing power, geographically distributed computing resources are now routinely linked using high-speed networks to address a broad range of computationally complex problems. These confederated resources are referred to collectively as a computational Grid. Many geographical problems exhibit characteristics that make them candidates for this new model of computing. As an illustration, we describe a spatial statistics problem and demonstrate how it can be addressed using Grid computing strategies. A key element of this application is the development of middleware that handles domain decomposition and coordinates computational functions. We also discuss the development of Grid portals that are designed to help researchers and decision makers access and use geographic information analysis tools. Research into grid computing has been driven by the need to solve large-scale, increasingly complex problems for scientific applications. Yet the applications of grid computing for business and casual users did not begin to emerge until the development of the concept of cloud computing, fueled by advances in virtualization techniques, coupled with the increased availability of ever-greater Internet bandwidth. The appeal of this new paradigm is mainly based on its simplicity, and the affordable price for seamless access to both computational and storage resources. This timely text reference introduces the fundamental principles and techniques underlying grids, clouds and virtualization technologies, as well as reviewing the latest research and expected future developments in the field. Readers are guided through the key topics by internationally recognized experts, enabling them to develop their understanding of an area likely to play an ever more significant role in coming years. Topics and features: presents contributions from an international selection of experts in the field; provides a thorough introduction and overview of existing technologies in grids, clouds and virtualization, including a brief history of the field; examines the basic requirements for performance isolation of virtual machines on multi-core servers, analyzing a selection of system virtualization technologies; examines both business and scientific applications of grids and clouds, including their use in the life sciences and for high-performance computing; explores cloud building technologies, architectures for enhancing grid infrastructures with cloud computing, and cloud performance; discusses energy aware grids and clouds, workflows on grids and clouds, and cloud and grid programming models. This useful text will enable interested readers to familiarize themselves with the key topics of grids, clouds and virtualization, and to contribute to new advances in the field. Researchers, undergraduate and graduate students, system designers and programmers, and IT policy makers will all benefit from the material covered. Grid computing gives users access to widely distributed networks of computing resources to solve large-scale tasks such as scientific computation. These tasks are defined as standalone components that can be combined to process the data in various ways. We have implemented a planning system to generate task workflows for the Grid automatically, allowing the user to specify the desired data products in simple terms. The planner uses heuristic control rules and searches a number of alternative complete plans in order to find a high-quality solution. We describe an implemented test case in gravitational wave interferometry and show how the planner is integrated in the Grid environment. We discuss promising future directions of this work. We believe AI planning will play a crucial role in developing complex application workflows for the Grid. Grid computing represents the main solution to integrate distributed and heterogeneous resources in global scale. However, the infrastructure necessary for maintaining a global grid in production is huge. Such fact has led to excessive power consumption. On the other hand, most green strategies for data centers are DVS (Dynamic Voltage Scaling)-based and become difficult to implement them in global grids. This paper proposes the HGreen heuristic (Heavier Tasks on Maximum Green Resource) and defines a workflow scheduling algorithm in order to implement it on global grids. HGreen algorithm aims to prioritize energy-efficient resources and explores workflow application profiles. Simulation results have shown that the proposed algorithm can significantly reduce the power consumption in global grids.