{"id":{"repo_id":"trento","oai_identifier":"oai:iris.unitn.it:11572/367825"},"canonical_url":"https://search.dev.ndltd.org/etd/trento/oai:iris.unitn.it:11572/367825","repository":{"repo_id":"trento","name":"Università degli Studi di Trento","base_url":"https://iris.unitn.it/oai/request"},"display":{"title":"GOssiping Optimization Framework (GOOF): A decentralized P2P architecture for function optimization","abstract":"This thesis discusses the implementation of function optimization algorithms through distributed and decentralized processing in a peer-to-peer fashion. Our research is focused on a fully decentralized, general purpose P2P environment, with no special or ad-hoc facility for executing optimization tasks. Relevant information is exchanged among nodes by means of epidemic protocols, exploiting the overlay network topology formed by peers. A key issue in such a context is the relationship between the solution quality and the amount/kind of exchanged information among the various running instances. We propose and detail novel heuristics and hyper-heuristics. Experimental results obtained both in simulated and real P2P environments are presented and discussed as well. Distributed optimization has a long and rich history, but little has been done to make it exploit the (potentially) large computing facilities a reliable P2P network can provide. We propose a novel framework that aims at easing the burden of performing function optimization tasks in a decentralized P2P network of solvers. Our †̃GOssiping Optimization Frameworkâ€TM (GOOF) bridges the gap between P2P services that can provide large reliable networks of interconnected nodes and the needs of optimization practitioners who are often not able to find a reasonably simple way to run their algorithms in such a distributed environment.","abstract_html":"This thesis discusses the implementation of function optimization algorithms through distributed and decentralized processing in a peer-to-peer fashion. Our research is focused on a fully decentralized, general purpose P2P environment, with no special or ad-hoc facility for executing optimization tasks. Relevant information is exchanged among nodes by means of epidemic protocols, exploiting the overlay network topology formed by peers. A key issue in such a context is the relationship between the solution quality and the amount/kind of exchanged information among the various running instances. We propose and detail novel heuristics and hyper-heuristics. Experimental results obtained both in simulated and real P2P environments are presented and discussed as well. Distributed optimization has a long and rich history, but little has been done to make it exploit the (potentially) large computing facilities a reliable P2P network can provide. We propose a novel framework that aims at easing the burden of performing function optimization tasks in a decentralized P2P network of solvers. Our †̃GOssiping Optimization Frameworkâ€TM (GOOF) bridges the gap between P2P services that can provide large reliable networks of interconnected nodes and the needs of optimization practitioners who are often not able to find a reasonably simple way to run their algorithms in such a distributed environment.","abstract_has_math":false,"creators":["Biazzini, Marco"],"institution":"Università degli studi di Trento","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Montresor, Alberto"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010","date_published":"2010","updated_at":"2026-07-24T05:04:26Z","subjects":[],"languages":["eng"],"rights":["info:eu-repo/semantics/openAccess","license:Tutti i diritti riservati (All rights reserved)"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["http://dx.doi.org/10.15168/11572_367825","10.15168/11572_367825"],"render_values":[{"text":"http://dx.doi.org/10.15168/11572_367825","href":"http://dx.doi.org/10.15168/11572_367825","code":true},{"text":"10.15168/11572_367825","href":"https://doi.org/10.15168/11572_367825","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/11572/367825","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Biazzini, Marco","Montresor, Alberto"]},{"key":"dc:creator","label":"Author","values":["Biazzini, Marco"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2010"]},{"key":"dc:publisher","label":"Institution","values":["Università degli studi di Trento","place:TRENTO"]},{"key":"dc:relation","label":"Dc Relation","values":["firstpage:1","lastpage:141","numberofpages:141"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess","license:Tutti i diritti riservati (All rights reserved)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/11572/367825","http://dx.doi.org/10.15168/11572_367825","10.15168/11572_367825"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis discusses the implementation of function optimization algorithms through distributed and decentralized processing in a peer-to-peer fashion. Our research is focused on a fully decentralized, general purpose P2P environment, with no special or ad-hoc facility for executing optimization tasks. Relevant information is exchanged among nodes by means of epidemic protocols, exploiting the overlay network topology formed by peers. A key issue in such a context is the relationship between the solution quality and the amount/kind of exchanged information among the various running instances. We propose and detail novel heuristics and hyper-heuristics. Experimental results obtained both in simulated and real P2P environments are presented and discussed as well. Distributed optimization has a long and rich history, but little has been done to make it exploit the (potentially) large computing facilities a reliable P2P network can provide. We propose a novel framework that aims at easing the burden of performing function optimization tasks in a decentralized P2P network of solvers. Our †̃GOssiping Optimization Frameworkâ€TM (GOOF) bridges the gap between P2P services that can provide large reliable networks of interconnected nodes and the needs of optimization practitioners who are often not able to find a reasonably simple way to run their algorithms in such a distributed environment."]},{"key":"dc:title","label":"Title","values":["GOssiping Optimization Framework (GOOF): A decentralized P2P architecture for function optimization"]}]}],"canonical_facts":{"dc:contributor":["Biazzini, Marco","Montresor, Alberto"],"dc:creator":["Biazzini, Marco"],"dc:date":["2010"],"dc:description":["This thesis discusses the implementation of function optimization algorithms through distributed and decentralized processing in a peer-to-peer fashion. Our research is focused on a fully decentralized, general purpose P2P environment, with no special or ad-hoc facility for executing optimization tasks. Relevant information is exchanged among nodes by means of epidemic protocols, exploiting the overlay network topology formed by peers. A key issue in such a context is the relationship between the solution quality and the amount/kind of exchanged information among the various running instances. We propose and detail novel heuristics and hyper-heuristics. Experimental results obtained both in simulated and real P2P environments are presented and discussed as well. Distributed optimization has a long and rich history, but little has been done to make it exploit the (potentially) large computing facilities a reliable P2P network can provide. We propose a novel framework that aims at easing the burden of performing function optimization tasks in a decentralized P2P network of solvers. Our †̃GOssiping Optimization Frameworkâ€TM (GOOF) bridges the gap between P2P services that can provide large reliable networks of interconnected nodes and the needs of optimization practitioners who are often not able to find a reasonably simple way to run their algorithms in such a distributed environment."],"dc:identifier":["https://hdl.handle.net/11572/367825","http://dx.doi.org/10.15168/11572_367825","10.15168/11572_367825"],"dc:language":["eng"],"dc:publisher":["Università degli studi di Trento","place:TRENTO"],"dc:relation":["firstpage:1","lastpage:141","numberofpages:141"],"dc:rights":["info:eu-repo/semantics/openAccess","license:Tutti i diritti riservati (All rights reserved)"],"dc:title":["GOssiping Optimization Framework (GOOF): A decentralized P2P architecture for function optimization"],"dc:type":["info:eu-repo/semantics/doctoralThesis"]},"updated_at":"2026-07-24T05:04:26Z"}