{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/62668"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/62668","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Adaptive Routing Using SDN and NFV","abstract":"In recent times, the primary focus of every ISP is to deliver high quality of service for multimedia applications. Due to ever-increasing network traffic, it is challenging for ISPs to accomplish optimal network conditions. To achieve high-quality services implementation of network monitoring and QoS routing algorithm is inevitable. Also, several modifications occur while network planning especially in managing physical devices to host network services required to achieve QoS routing. Dynamic routing in physical network hardware is not suitable for cost effective business models. Therefore, to lower operational and capital expenditure, we propose a technique called adaptive routing managed with the support of SDN (Software Defined Networking and NFV (Network Function Virtualization). We introduce an economical routing model which will assist in meeting the demands of the consumers and at the same time provide significant savings in product cost and usability. To perform adaptive routing, we designed a topology in a virtualized environment. We programmed RINA (Recursive InterNetwork Architecture) for SDN management which acts as a steady northbound API. To implement NFV, we hosted Intrusion detection service(IDS) as a Virtual Network Function (VNF). We created two scenarios where we execute shortest path routing and adaptive routing. To compare these different routing scenarios, we ran a client-server DASH (Dynamic Adaptive Streaming over HTTP) application. Furthermore, we injected Iperf traffic into the network categorized as unwanted traffic. Lastly, we performed adaptive routing applying control theoretic technique with the help of SDN and NFV. By comparing two routing scenarios, we observed a notable difference in throughput for DASH application.","abstract_html":"In recent times, the primary focus of every ISP is to deliver high quality of service for multimedia applications. Due to ever-increasing network traffic, it is challenging for ISPs to accomplish optimal network conditions. To achieve high-quality services implementation of network monitoring and QoS routing algorithm is inevitable. Also, several modifications occur while network planning especially in managing physical devices to host network services required to achieve QoS routing. Dynamic routing in physical network hardware is not suitable for cost effective business models. Therefore, to lower operational and capital expenditure, we propose a technique called adaptive routing managed with the support of SDN (Software Defined Networking and NFV (Network Function Virtualization). We introduce an economical routing model which will assist in meeting the demands of the consumers and at the same time provide significant savings in product cost and usability. To perform adaptive routing, we designed a topology in a virtualized environment. We programmed RINA (Recursive InterNetwork Architecture) for SDN management which acts as a steady northbound API. To implement NFV, we hosted Intrusion detection service(IDS) as a Virtual Network Function (VNF). We created two scenarios where we execute shortest path routing and adaptive routing. To compare these different routing scenarios, we ran a client-server DASH (Dynamic Adaptive Streaming over HTTP) application. Furthermore, we injected Iperf traffic into the network categorized as unwanted traffic. Lastly, we performed adaptive routing applying control theoretic technique with the help of SDN and NFV. By comparing two routing scenarios, we observed a notable difference in throughput for DASH application.","abstract_has_math":false,"creators":["Parab, Tejas Balkrishna"],"institution":"University of Missouri--Kansas City","degree_name":"M.S.","degree_level":"Masters","degree_discipline":"Electrical Engineering (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Medhi, Deepankar"],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-24T05:18:34Z","subjects":[],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/62668","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Medhi, Deepankar"]},{"key":"dc:creator","label":"Author","values":["Parab, Tejas Balkrishna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-01-24T19:13:18Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-01-24T19:13:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri--Kansas City"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Kansas City"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/62668"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page viewed January 31, 2018","Thesis advisor: Deepankar Medhi","Vita","Includes bibliographical references (pages 35-37)","Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017"]},{"key":"dc:description.abstract","label":"Abstract","values":["In recent times, the primary focus of every ISP is to deliver high quality of service for multimedia applications. Due to ever-increasing network traffic, it is challenging for ISPs to accomplish optimal network conditions. To achieve high-quality services implementation of network monitoring and QoS routing algorithm is inevitable. Also, several modifications occur while network planning especially in managing physical devices to host network services required to achieve QoS routing. Dynamic routing in physical network hardware is not suitable for cost effective business models. Therefore, to lower operational and capital expenditure, we propose a technique called adaptive routing managed with the support of SDN (Software Defined Networking and NFV (Network Function Virtualization). We introduce an economical routing model which will assist in meeting the demands of the consumers and at the same time provide significant savings in product cost and usability. To perform adaptive routing, we designed a topology in a virtualized environment. We programmed RINA (Recursive InterNetwork Architecture) for SDN management which acts as a steady northbound API. To implement NFV, we hosted Intrusion detection service(IDS) as a Virtual Network Function (VNF). We created two scenarios where we execute shortest path routing and adaptive routing. To compare these different routing scenarios, we ran a client-server DASH (Dynamic Adaptive Streaming over HTTP) application. Furthermore, we injected Iperf traffic into the network categorized as unwanted traffic. Lastly, we performed adaptive routing applying control theoretic technique with the help of SDN and NFV. By comparing two routing scenarios, we observed a notable difference in throughput for DASH application."]},{"key":"dc:title","label":"Title","values":["Adaptive Routing Using SDN and NFV"]}]}],"canonical_facts":{"dc:contributor.advisor":["Medhi, Deepankar"],"dc:creator":["Parab, Tejas Balkrishna"],"dc:date.accessioned":["2018-01-24T19:13:18Z"],"dc:date.available":["2018-01-24T19:13:18Z"],"dc:date.issued":["2017"],"dc:description":["Title from PDF of title page viewed January 31, 2018","Thesis advisor: Deepankar Medhi","Vita","Includes bibliographical references (pages 35-37)","Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017"],"dc:description.abstract":["In recent times, the primary focus of every ISP is to deliver high quality of service for multimedia applications. Due to ever-increasing network traffic, it is challenging for ISPs to accomplish optimal network conditions. To achieve high-quality services implementation of network monitoring and QoS routing algorithm is inevitable. Also, several modifications occur while network planning especially in managing physical devices to host network services required to achieve QoS routing. Dynamic routing in physical network hardware is not suitable for cost effective business models. Therefore, to lower operational and capital expenditure, we propose a technique called adaptive routing managed with the support of SDN (Software Defined Networking and NFV (Network Function Virtualization). We introduce an economical routing model which will assist in meeting the demands of the consumers and at the same time provide significant savings in product cost and usability. To perform adaptive routing, we designed a topology in a virtualized environment. We programmed RINA (Recursive InterNetwork Architecture) for SDN management which acts as a steady northbound API. To implement NFV, we hosted Intrusion detection service(IDS) as a Virtual Network Function (VNF). We created two scenarios where we execute shortest path routing and adaptive routing. To compare these different routing scenarios, we ran a client-server DASH (Dynamic Adaptive Streaming over HTTP) application. Furthermore, we injected Iperf traffic into the network categorized as unwanted traffic. Lastly, we performed adaptive routing applying control theoretic technique with the help of SDN and NFV. 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