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, 2008-04-01)
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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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ItemNo Preview AvailableA grid workflow environment for brain imaging analysis on distributed systems(WILEY, 2009-11-01)
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ItemPortfolio and investment risk analysis on global grids(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2007-12-01)
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ItemHarnessing Cloud Technologies for a Virtualized Distributed Computing Infrastructure(IEEE COMPUTER SOC, 2009-09-01)
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ItemOn incorporating differentiated levels of network service into GridSim(ELSEVIER, 2007-05-01)
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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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ItemMetaCDN: Harnessing 'Storage Clouds' for high performance content delivery(ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 2009-09-01)