Control and Data Planes In Software Defined Data center Networks A Scalable and Resilient Approach

dc.contributor.guideKoolagudi , Shashidhar G and Swapan, Bhattacharya
dc.coverage.spatial
dc.creator.researcherHegde, Saumya
dc.date.accessioned2022-06-29T05:24:28Z
dc.date.available2022-06-29T05:24:28Z
dc.date.awarded
dc.date.completed2019
dc.date.registered
dc.description.abstractThe single central controller of Software Defined Network (SDN) eases network newlinemanagement, but leads to scalability problems. It is therefore ideal to have a newlinelogically centralized but physically distributed set of controllers. As part of this newlinework we developed a novel placement metric called subgraph-survivability and newlinedesigned an algorithm for controller placement using this metric, such that the newlinecontrol plane is not only scalable but also resilient to failure of the controller itself. newlineThe controller collects the network statistics information and also communicates newlinethe forwarding rules to the switches. This lead to the Edge-Core SDN architecture, newlinewhere the edge and core network have their own edge and core controller. For newlinesuch networks, we have developed a separate edge and core controller placement newlinealgorithms using suitable metrics for each. The scalability problem of the data newlineplane is due to the limited switch memory and increased size of SDN forwarding newlinerule. Using source routing to forward packets, not only alleviates this problem but newlinealso complements the Edge-Core SDN model. Here, we have proposed a source newlinerouting mechanism that is scalable, is fair to both elephant and mice traffic, and newlineis resilient to link failures, thus making the data plane scalable and resilient. newlineThe algorithm and routing mechanism are validated, through both analytical newlineand empirical methods. The performance metrics of Average Inverse Shortest newlinePath Length (AISPL) and Network Disconnectedness (ND) are used to evaluate newlineour placement algorithms. An improvement of 55.88% for the AISPL metric and newline49.22% for ND metric, was observed with our proposed algorithm as compared to newlinethe random controller placement. With our source routing mechanism we observe newlinea reduction, in the number of flow table entries and the flow set up time, that is newlineproportional to the number of hops along the path of the packet. newlineKeywords: SDN, Edge-Core SDN, Controller Placement, Source newlineRouting, Scalability, Reliability, Fairness newline
dc.description.note
dc.format.accompanyingmaterialNone
dc.format.dimensions
dc.format.extent
dc.identifier.urihttp://hdl.handle.net/10603/389507
dc.languageEnglish
dc.publisher.institutionDepartment of Computer Science and Engineering
dc.publisher.placeMangaluru
dc.publisher.universityNational Institute of Technology Karnataka
dc.relation
dc.rightsuniversity
dc.source.universityUniversity
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordEngineering and Technology
dc.titleControl and Data Planes In Software Defined Data center Networks A Scalable and Resilient Approach
dc.title.alternative
dc.type.degreePh.D.

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