Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-15277-1_40
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dc.titleA distributed market framework for large-scale resource sharing
dc.contributor.authorMihailescu, M.
dc.contributor.authorTeo, Y.M.
dc.date.accessioned2013-07-04T07:58:15Z
dc.date.available2013-07-04T07:58:15Z
dc.date.issued2010
dc.identifier.citationMihailescu, M.,Teo, Y.M. (2010). A distributed market framework for large-scale resource sharing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6271 LNCS (PART 1) : 418-430. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-15277-1_40" target="_blank">https://doi.org/10.1007/978-3-642-15277-1_40</a>
dc.identifier.isbn3642152767
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40171
dc.description.abstractCurrent distributed computing infrastructures, such as peer-to-peer networks, grids, and more recently clouds, make sharing and trading resources ubiquitous. In these large distributed systems, rational users are both providers and consumers of resources. Currently, there is growing interest in exploiting economic models for the allocation of shared computing resources that incentivize rational users. However, when the number of resource types and users increases, computational complexity of the allocation algorithms grows rapidly and efficiency deteriorates. In this paper, we propose a scalable distributed market framework for the allocation of shared resources in large distributed systems. We use mechanism design to create a pricing scheme that allocates a request for multiple resource types, by trading economic efficiency for computational efficiency, strategy-proof and budget-balance. To address scalability, our proposed framework leverages on a peer-to-peer overlay for resource discovery and management. We prototype our framework using FreePastry, a popular overlay network based on the Pastry protocol. We show that our scheme is efficient and scalable using both simulation experiments and results from the deployment on PlantLab. © 2010 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-15277-1_40
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-15277-1_40
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume6271 LNCS
dc.description.issuePART 1
dc.description.page418-430
dc.identifier.isiutNOT_IN_WOS
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