Computing and Information Systems - Research Publications
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ItemScheduling parameter sweep applications on global Grids: A deadline and budget constrained cost-time optimization algorithm(Wiley, 2005)Computational Grids and peer-to-peer (P2P) networks enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. The management and composition of resources and services for scheduling applications, however, becomes a complex undertaking. We have proposed a computational economy framework for regulating the supply of and demand for resources and allocating them for applications based on the users' quality-of-service requirements. The framework requires economy-driven deadline- and budget-constrained (DBC) scheduling algorithms for allocating resources to application jobs in such a way that the users' requirements are met. In this paper, we propose a new scheduling algorithm, called the DBC cost-time optimization scheduling algorithm, that aims not only to optimize cost, but also time when possible. The performance of the cost-time optimization scheduling algorithm has been evaluated through extensive simulation and empirical studies for deploying parameter sweep applications on global Grids.
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ItemAn SCP-based heuristic approach for scheduling distributed data-intensive applications on global grids(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2008-04)
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ItemA taxonomy of data grids for distributed data sharing, management, and processing(Association for Computing Machinery (ACM), 2006)Data Grids have been adopted as the next generation platform by many scientific communities that need to share, access, transport, process, and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this article, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks, and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation, and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration.
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ItemPortfolio and investment risk analysis on global grids(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2007-12)
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ItemA toolkit for modelling and simulating data Grids: an extension to GridSim(WILEY, 2008-09-10)
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ItemInterGrid:: a case for internetworking islands of Grids(WILEY, 2008-06-10)
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ItemA grid service broker for scheduling e-Science applications on global data grids(John Wiley & Sons, 2006)The next generation of scientific experiments and studies, popularly called e-Science, is carried out by large collaborations of researchers distributed around the world engaged in the analysis of huge collections of data generated by scientific instruments. Grid computing has emerged as an enabler for e-Science as it permits the creation of virtual organizations that bring together communities with common objectives. Within a community, data collections are stored or replicated on distributed resources to enhance storage capability or the efficiency of access. In such an environment, scientists need to have the ability to carry out their studies by transparently accessing distributed data and computational resources. In this paper, we propose and develop a Grid broker that mediates access to distributed resources by: (a) discovering suitable data and computational resources sources for a given analysis scenario; (b) optimally mapping analysis jobs to resources; (c) deploying and monitoring job execution on selected resources; (d) accessing data from local or remote data sources during job execution; and (e) collating and presenting results. The broker supports a declarative and dynamic parametric programming model for creating Grid applications. We have used this model in Grid-enabling a high-energy physics analysis application (the Belle Analysis Software Framework). The broker has been used in deploying Belle experimental data analysis jobs on a Grid testbed, called the Belle Analysis Data Grid, having resources distributed across Australia interconnected through GrangeNet.
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ItemNeuroscience instrumentation and distributed analysis of brain activity data: A case for eScience on global Grids(John Wiley & Sons, 2005)The distribution of knowledge (by scientists) and data sources (advanced scientific instruments), and the need for large-scale computational resources for analyzing massive scientific data are two major problems commonly observed in scientific disciplines. Two popular scientific disciplines of this nature are brain science and high-energy physics. The analysis of brain-activity data gathered from the MEG (magnetoencephalography) instrument is an important research topic in medical science since it helps doctors in identifying symptoms of diseases. The data needs to be analyzed exhaustively to efficiently diagnose and analyze brain functions and requires access to large-scale computational resources. The potential platform for solving such resource intensive applications is the Grid. This paper presents the design and development of MEG data analysis system by leveraging Grid technologies, primarily Nimrod-G, Gridbus, and Globus. It describes the composition of the neuroscience (brain-activity analysis) application as parameter-sweep application and its on-demand deployment on global Grids for distributed execution. The results of economic-based scheduling of analysis jobs for three different optimizations scenarios on the world-wide Grid testbed resources are presented along with their graphical visualization.