{"id":{"repo_id":"ncsu","oai_identifier":"oai:repository.lib.ncsu.edu:1840.16/3496"},"canonical_url":"https://search.dev.ndltd.org/etd/ncsu/oai:repository.lib.ncsu.edu:1840.16/3496","repository":{"repo_id":"ncsu","name":"North Carolina State University","base_url":"https://repository.lib.ncsu.edu/server/oai/request"},"display":{"title":"Efficient Evaluation of Highly Available Services: Fast Simulation and Testing","abstract":"Modern technologies have provided us with highly available services. Systems such as optical backbone networks, robust web servers, and reliable software can provide a service with unavailability probability lower than 10ˆ−6. Although rare, service unavailability can cause serious problems such as significant performance drop, or violation of Service Level Agreements (SLA). Moreover, providers of these services need to know the value of service unavailability probability so they can provide reasonable SLAs and corresponding Quality of Service (QoS). However, due to the extremely low values of the service unavailability probabilities, estimating them using traditional simulation or testing methods can require a vast amount of time to obtain a satisfactory confidence interval. As a result, efficient evaluation techniques are necessary. In this dissertation, we propose efficient evaluation methods based on importance sampling (IS). For fast simulation, we introduce several types of IS tuning methods: Our static IS method, which is based on asymptotically efficient IS biasing methods for a single queue, is proven to have bounded relative error. Our adaptive IS method, which is based on guidelines of \"optimal biasing\", is efficient and can be widely employed. Moreover, IS methods that are stochastically optimized by simulated annealing can be used when the system is complicated, or when the knowledge of the system is limited. Finally, for performance evaluation and optimization of a system under various parameter settings, we propose a framework based on IS and metamodeling methodologies. All of these methods provided in this dissertation are verified by either proof or simulation to be both accurate and efficient.","abstract_html":"Modern technologies have provided us with highly available services. Systems such as optical backbone networks, robust web servers, and reliable software can provide a service with unavailability probability lower than 10ˆ−6. Although rare, service unavailability can cause serious problems such as significant performance drop, or violation of Service Level Agreements (SLA). Moreover, providers of these services need to know the value of service unavailability probability so they can provide reasonable SLAs and corresponding Quality of Service (QoS). However, due to the extremely low values of the service unavailability probabilities, estimating them using traditional simulation or testing methods can require a vast amount of time to obtain a satisfactory confidence interval. As a result, efficient evaluation techniques are necessary. In this dissertation, we propose efficient evaluation methods based on importance sampling (IS). For fast simulation, we introduce several types of IS tuning methods: Our static IS method, which is based on asymptotically efficient IS biasing methods for a single queue, is proven to have bounded relative error. Our adaptive IS method, which is based on guidelines of "optimal biasing", is efficient and can be widely employed. Moreover, IS methods that are stochastically optimized by simulated annealing can be used when the system is complicated, or when the knowledge of the system is limited. Finally, for performance evaluation and optimization of a system under various parameter settings, we propose a framework based on IS and metamodeling methodologies. All of these methods provided in this dissertation are verified by either proof or simulation to be both accurate and efficient.","abstract_has_math":false,"creators":["Hsu, Chih-Chieh"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Harry G. Perros, Committee Member","Stephen D. Roberts, Committee Member","Michael Devetsikiotis, Committee Chair","Yannis Viniotis, Committee Member","Do Young Eun, Committee Member"],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007-05-07","date_published":"2007-05-07","updated_at":"2026-08-21T22:21:56Z","subjects":["Performance Optimization","Performance Evaluation","Variance Reduction","Highly Available Services","Networks","Fast Simulation"],"languages":[],"rights":["I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-01012007-005135"],"render_values":[{"text":"etd-01012007-005135","href":null,"code":true}]}]},"links":{"outbound_url":"http://www.lib.ncsu.edu/resolver/1840.16/3496","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://repository.lib.ncsu.edu/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Arepository.lib.ncsu.edu%3A1840.16%2F3496","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Harry G. Perros, Committee Member","Stephen D. Roberts, Committee Member","Michael Devetsikiotis, Committee Chair","Yannis Viniotis, Committee Member","Do Young Eun, Committee Member"]},{"key":"dc:creator","label":"Author","values":["Hsu, Chih-Chieh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2010-04-02T18:30:51Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2010-04-02T18:30:51Z"]},{"key":"dc:date.issued","label":"Date","values":["2007-05-07"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Performance Optimization","Performance Evaluation","Variance Reduction","Highly Available Services","Networks","Fast Simulation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-01012007-005135"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://www.lib.ncsu.edu/resolver/1840.16/3496"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["North Carolina State University Theses Electrical and Computer Engineering."]},{"key":"dc:description.abstract","label":"Abstract","values":["Modern technologies have provided us with highly available services. Systems such as optical backbone networks, robust web servers, and reliable software can provide a service with unavailability probability lower than 10ˆ−6. Although rare, service unavailability can cause serious problems such as significant performance drop, or violation of Service Level Agreements (SLA). Moreover, providers of these services need to know the value of service unavailability probability so they can provide reasonable SLAs and corresponding Quality of Service (QoS). However, due to the extremely low values of the service unavailability probabilities, estimating them using traditional simulation or testing methods can require a vast amount of time to obtain a satisfactory confidence interval. As a result, efficient evaluation techniques are necessary. In this dissertation, we propose efficient evaluation methods based on importance sampling (IS). For fast simulation, we introduce several types of IS tuning methods: Our static IS method, which is based on asymptotically efficient IS biasing methods for a single queue, is proven to have bounded relative error. Our adaptive IS method, which is based on guidelines of \"optimal biasing\", is efficient and can be widely employed. Moreover, IS methods that are stochastically optimized by simulated annealing can be used when the system is complicated, or when the knowledge of the system is limited. Finally, for performance evaluation and optimization of a system under various parameter settings, we propose a framework based on IS and metamodeling methodologies. All of these methods provided in this dissertation are verified by either proof or simulation to be both accurate and efficient."]},{"key":"dc:format","label":"Dc Format","values":["Thesis (Ph.D.)--North Carolina State University."]},{"key":"dc:title","label":"Title","values":["Efficient Evaluation of Highly Available Services: Fast Simulation and Testing"]}]}],"canonical_facts":{"dc:contributor.advisor":["Harry G. Perros, Committee Member","Stephen D. Roberts, Committee Member","Michael Devetsikiotis, Committee Chair","Yannis Viniotis, Committee Member","Do Young Eun, Committee Member"],"dc:creator":["Hsu, Chih-Chieh"],"dc:date.accessioned":["2010-04-02T18:30:51Z"],"dc:date.available":["2010-04-02T18:30:51Z"],"dc:date.issued":["2007-05-07"],"dc:description":["North Carolina State University Theses Electrical and Computer Engineering."],"dc:description.abstract":["Modern technologies have provided us with highly available services. Systems such as optical backbone networks, robust web servers, and reliable software can provide a service with unavailability probability lower than 10ˆ−6. Although rare, service unavailability can cause serious problems such as significant performance drop, or violation of Service Level Agreements (SLA). Moreover, providers of these services need to know the value of service unavailability probability so they can provide reasonable SLAs and corresponding Quality of Service (QoS). However, due to the extremely low values of the service unavailability probabilities, estimating them using traditional simulation or testing methods can require a vast amount of time to obtain a satisfactory confidence interval. As a result, efficient evaluation techniques are necessary. In this dissertation, we propose efficient evaluation methods based on importance sampling (IS). For fast simulation, we introduce several types of IS tuning methods: Our static IS method, which is based on asymptotically efficient IS biasing methods for a single queue, is proven to have bounded relative error. Our adaptive IS method, which is based on guidelines of \"optimal biasing\", is efficient and can be widely employed. Moreover, IS methods that are stochastically optimized by simulated annealing can be used when the system is complicated, or when the knowledge of the system is limited. Finally, for performance evaluation and optimization of a system under various parameter settings, we propose a framework based on IS and metamodeling methodologies. All of these methods provided in this dissertation are verified by either proof or simulation to be both accurate and efficient."],"dc:format":["Thesis (Ph.D.)--North Carolina State University."],"dc:identifier.other":["etd-01012007-005135"],"dc:identifier.uri":["http://www.lib.ncsu.edu/resolver/1840.16/3496"],"dc:rights":["I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report."],"dc:subject":["Performance Optimization","Performance Evaluation","Variance Reduction","Highly Available Services","Networks","Fast Simulation"],"dc:title":["Efficient Evaluation of Highly Available Services: Fast Simulation and Testing"]},"updated_at":"2026-08-21T22:21:56Z"}