214 research outputs found
Scheduling distributed data-intensive applications on global grids
No full text© 2006 Dr. Srikumar VenugopalThe next generation of scientific experiments and studies are being carried out by large collaborations of researchers distributed around the world engaged in analysis of huge collections of data generated by scientific instruments. Grid computing has emerged as an enabler for such collaborations as it aids communities in sharing resources to achieve common objectives. Data Grids provide services for accessing, replicating and managing data collections in these collaborations. Applications used in such Grids are distributed data-intensive, that is, they access and process distributed datasets to generate results. These applications need to transparently and efficiently access distributed data and computational resources. This thesis investigates properties of data-intensive computing environments and presents a software framework and algorithms for mapping distributed data-oriented applications to Grid resources. (For complete abstract open document
Will Siri ever dream of electric sheep?
No full textThe idea of a personal robot assistant, has always been a staple of science fiction, writes Srikumar Venugopal for The Conversation.
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The 1940s had Zolo to scare away officious mailmen and refresh bouquets, while the Jetsons had Rosie to deal with prickly bosses. HAL 9000, the most evil red light in filmdom, may not have been keen to “open the pod bay doors” but it could still belt out a mean rendition of Daisy Bell.
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A study in grid simulation and scheduling
No full textGrid computing is emerging as an essential tool for large scale analysis and problem solving in scientific and business domains. Whilst the idea of stealing unused processor cycles is as old as the Internet, we are still far from reaching a position where many distributed resources can be seamlessly utilised on demand. One major issue preventing this vision is deciding how to effectively manage the remote resources and how to schedule the tasks amongst these resources. This thesis describes an investigation into Grid computing, specifically the problem of Grid scheduling. This complex problem has many unique features making it particularly difficult to solve and as a result many current Grid systems employ simplistic, inefficient solutions. This work describes the development of a simulation tool, G-Sim, which can be used to test the effectiveness of potential Grid scheduling algorithms under realistic operating conditions. This tool is used to analyse the effectiveness of a simple, novel scheduling technique in numerous scenarios. The results are positive and show that it could be applied to current procedures to enhance performance and decrease the negative effect of resource failure. Finally a conversion between the Grid scheduling problem and the classic computing problem SAT is provided. Such a conversion allows for the possibility of applying sophisticated SAT solving procedures to Grid scheduling providing potentially effective solutions
Entrepreneurial marketing in subsistence marketplaces
No full textThere are more than a billion poverty-stricken entrepreneurs in the world who run micro-enterprises to meet basic consumption needs. This pervasive phenomenon presents an interesting theoretical conundrum - that of consumer-entrepreneur duality. This duality blurs the boundaries between consumption and entrepreneurship, which have traditionally been distinct domains of scholarly inquiry. The research reported in this dissertation aims to a) provide a theoretical foundation for the notion of consumer-entrepreneur duality and b) test the implications of the aforementioned duality empirically. A key insight flowing from the investigations is that factors in the consumption domain impact important outcomes in the entrepreneurial domain and vice versa.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-05-01The student, Srinivas Venugopal, accepted the attached license on 2016-04-18 at 08:47.The student, Srinivas Venugopal, submitted this Dissertation for approval on 2016-04-18 at 09:04.This Dissertation was approved for publication on 2016-04-19 at 08:14.DSpace SAF Submission Ingestion Package generated from Vireo submission #9286 on 2016-07-07 at 14:17:05Made available in DSpace on 2016-07-07T21:17:37Z (GMT). No. of bitstreams: 4
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Previous issue date: 2016-04-19Embargo set by: Seth Robbins for item 93274
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Efficient Node Bootstrapping for Decentralised Shared-Nothing Key-Value Stores
No full textPart 3: StorageInternational audienceDistributed key-value stores (KVSs) have become an important component for data management in cloud applications. Since resources can be provisioned on demand in the cloud, there is a need for efficient node bootstrapping and decommissioning, i.e. to incorporate or eliminate the provisioned resources as a members of the KVS. It requires the data be handed over and the load be shifted across the nodes quickly. However, the data partitioning schemes in the current-state shared nothing KVSs are not efficient in quick bootstrapping. In this paper, we have designed a middleware layer that provides a decentralised scheme of auto-sharding with a two-phase bootstrapping. We experimentally demonstrate that our scheme reduces bootstrap time and improves load-balancing thereby increasing scalability of the KVS
A Set Coverage-based Mapping Heuristic for Scheduling Distributed Data-Intensive Applications on Global Grids
No full textData-intensive Grid applications need access to large datasets that may each be replicated on different resources. Minimizing the overhead of transferring these datasets to the resources where the applications are executed requires that appropriate computational and data resources be selected. In this paper, we introduce a heuristic for the selection of resources based on a solution to the Set Covering Problem (SCP). We then pair this mapping heuristic with the well-known MinMin scheduling algorithm and conduct performance evaluation through extensive simulations
An Economy-based Algorithm for Scheduling Data-Intensive Applications on Global Grids
No full textData Grids have become the de facto platform for the next generation of eScience experiments that will be carried out through large collaborations spread around the world. As the number of entities within a data grid increases, scheduling of applications in order to make the most efficient use of the available resources such as computational, storage and network facilities becomes a challenge. Previous work has suggested a computational economy metaphor for resource management within compute and data grids. However, the issue of scheduling jobs that require distributed data within an economy-based data grid has not been studied in detail so far
Using reinforcement learning for controlling an elastic web application hosting platform
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The Gridbus Toolkit for Service Oriented Grid and Utility Computing: An Overview and Status Report
No full textGrids aim at exploiting synergies that result from cooperation of autonomous distributed entities. The synergies that result from grid cooperation include the sharing, exchange, selection, and aggregation of geographically distributed resources such as computers, data bases, software, and scientific instruments for solving large-scale problems in science, engineering, and commerce. For this cooperation to be sustainable, participants need to have economic incentive. Therefore, "incentive" mechanisms should be considered as one of key design parameters of Grid architectures. In this article, we present an overview and status of an open source Grid toolkit, called Gridbus, whose architecture is fundamentally driven by the requirements of Grid economy. Gridbus technologies provide services for both computational and data grids that power the emerging eScience and eBusiness applications
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