Computing and Information Systems - Research Publications
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ItemGridEmail: Economically Regulated Internet-based Interpersonal Communications(Nova Science Publishers, 2006)
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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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ItemAdvanced reservation-based scheduling of Task Graphs on clusters(SPRINGER-VERLAG BERLIN, 2006)
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ItemDecentralized media streaming infrastructure (DeMSI): An adaptive and high-performance peer-to-peer content delivery network(ELSEVIER SCIENCE BV, 2006)Hosting an on-demand media content streaming service has been a challenging task mainly because of the outrageously enormous network and server bandwidth required to deliver large amount of content data to users simultaneously. We propose an infrastructure that helps online media content providers offload their server and network resources for media streaming. Using application level resource diversity together with the peer-to-peer resource-sharing model is a feasible approach to decentralize the content storage, server and network bandwidth. Each subscriber is responsible for only a small fraction of such resources. Most importantly, the cost of maintaining the service can also be shared amongst subscribers, especially when the subscriber base is large. As a result, subscribers can be benefit from lower subscription cost. There have been a few solutions out there that focused only on sharing the load of network bandwidth by division of a streaming task to be carried out by multiple sources. However, existing solutions require that the content to be replicated in full and stored in each source, which is impractical for a subscriber as the owner of the storage resource that is of consumer capacity. Our solution focuses on the division of responsibility on both the network bandwidth and content storage such that each subscriber is responsible for only a small portion of the content. We propose a light-weighted candidate peer selection strategy based on avoidance of network congestion and an adaptive re-scheduling algorithm in order to enhance smoothness of the aggregated streaming rate perceived at the consumer side. Experiments show that the performance of our peer-selection strategy out performs the traditional strategy based on end-to-end streaming bandwidth.
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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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ItemGrid Programming Models and Environments(Nova Science Publishers, 2006)