A framework for network RTK data processing based on grid computing

Real-Time Kinematic (RTK) positioning is a technique used to provide precise positioning services at centimetre accuracy level in the context of Global Navigation Satellite Systems (GNSS). While a Network-based RTK (N-RTK) system involves multiple continuously operating reference stations (CORS), the simplest form of a NRTK system is a single-base RTK. In Australia there are several NRTK services operating in different states and over 1000 single-base RTK systems to support precise positioning applications for surveying, mining, agriculture, and civil construction in regional areas. Additionally, future generation GNSS constellations, including modernised GPS, Galileo, GLONASS, and Compass, with multiple frequencies have been either developed or will become fully operational in the next decade. A trend of future development of RTK systems is to make use of various isolated operating network and single-base RTK systems and multiple GNSS constellations for extended service coverage and improved performance. Several computational challenges have been identified for future NRTK services including: • Multiple GNSS constellations and multiple frequencies • Large scale, wide area NRTK services with a network of networks • Complex computation algorithms and processes • Greater part of positioning processes shifting from user end to network centre with the ability to cope with hundreds of simultaneous users’ requests (reverse RTK) There are two major requirements for NRTK data processing based on the four challenges faced by future NRTK systems, expandable computing power and scalable data sharing/transferring capability. This research explores new approaches to address these future NRTK challenges and requirements using the Grid Computing facility, in particular for large data processing burdens and complex computation algorithms. A Grid Computing based NRTK framework is proposed in this research, which is a layered framework consisting of: 1) Client layer with the form of Grid portal; 2) Service layer; 3) Execution layer. The user’s request is passed through these layers, and scheduled to different Grid nodes in the network infrastructure. A proof-of-concept demonstration for the proposed framework is performed in a five-node Grid environment at QUT and also Grid Australia. The Networked Transport of RTCM via Internet Protocol (Ntrip) open source software is adopted to download real-time RTCM data from multiple reference stations through the Internet, followed by job scheduling and simplified RTK computing. The system performance has been analysed and the results have preliminarily demonstrated the concepts and functionality of the new NRTK framework based on Grid Computing, whilst some aspects of the performance of the system are yet to be improved in future work.

Grid computing solutions for distributed repositories of protein folding and unfolding simulations

The P-found protein folding and unfolding simulation repository is designed to allow scientists to perform analyses across large, distributed simulation data sets. There are two storage components in P-found: a primary repository of simulation data and a data warehouse. Here we demonstrate how grid technologies can support multiple, distributed P-found installations. In particular we look at two aspects, first how grid data management technologies can be used to access the distributed data warehouses; and secondly, how the grid can be used to transfer analysis programs to the primary repositories --- this is an important and challenging aspect of P-found because the data volumes involved are too large to be centralised. The grid technologies we are developing with the P-found system will allow new large data sets of protein folding simulations to be accessed and analysed in novel ways, with significant potential for enabling new scientific discoveries.

Fault detection service architecture for grid computing systems

The ability to tolerate failures while effectively exploiting the grid computing resources in an scalable and transparent manner must be an integral part of grid computing infrastructure. Hence, fault-detection service is a necessary prerequisite to fault tolerance and fault recovery in grid computing. To this end, we present an scalable fault detection service architecture. The proposed fault-detection system provides services that monitors user applications, grid middlewares and the dynamically changing state of a collection of distributed resources. It reports summaries of this information to the appropriate agents on demand or instantaneously in the event of failures.

Autonomic job scheduling policy for grid computing

Autonomic middleware services will play an important role in the management of resources and distributed workloads in emerging distributed computing environments. In this paper, we address the problem of autonomic grid resource scheduling and propose a scheduling infrastructure that is capable of self-management in the face of dynamic behavior inherent to this kind of systems.

Job scheduling policy for high throughput grid computing

The growing computational power requirements of grand challenge applications has promoted the need for merging high throughput computing and grid computing principles to harness computational resources distributed across multiple organisations. This paper identifies the issues in resource management and scheduling in the emerging high throughput grid computing context. We also survey and study the performance of several space-sharing and time-sharing opportunistic scheduling policies that have been developed for high throughput computing.

Robust parallel job scheduling infrastructure for service-oriented grid computing systems

Recent trends in grid computing development is moving towards a service-oriented architecture. With the momentum gaining for the service-oriented grid computing systems, the issue of deploying support for integrated scheduling and fault-tolerant approaches becomes paramount importance. To this end, we propose a scalable framework that loosely couples the dynamic job scheduling approach with the hybrid replications approach to schedule jobs efficiently while at the same time providing fault-tolerance. The novelty of the proposed framework is that it uses passive replication approach under high system load and active replication approach under low system loads. The switch between these two replication methods is also done dynamically and transparently.

Grid accounting service infrastructure for service-oriented grid computing systems

In this paper, we propose an architecture of accounting and payment services for service-oriented grid computing systems. The proposed accounting and payment services provide the mechanisms for service providers to be paid for authorized use of their resources. It supports the recording of usage data, secure storage of that data, analysis of that data for purposes of billing and so forth. It allows a variety of payment methods, it is scalable, secure, convenient, and reduce the overall cost of payment processing while taking into account requirements of Grid computing systems.