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dc.contributor.advisorMedhi, Deepankar
dc.contributor.authorZhao, Shuai
dc.date.issued2017
dc.date.submitted2017 Summer
dc.descriptionTitle from PDF of title page viewed October 30, 2017
dc.descriptionDissertation advisor: Deep Medhi
dc.descriptionVita
dc.descriptionIncludes bibliographical references (pages 122-135)
dc.descriptionThesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017
dc.description.abstractThis dissertation investigates improvement in application performance. For applications, we consider two classes: Hadoop MapReduce and video streaming. The Hadoop MapReduce (M/R) framework has become the de facto standard for Big Data analytics. However, the lack of network-awareness of the default MapReduce resource manager in a traditional IP network can cause unbalanced job scheduling and network bottlenecks; such factors can eventually lead to an increase in the Hadoop MapReduce job completion time. Dynamic Video streaming over the HTTP (MPEG-DASH) is becoming the defacto dominating transport for today’s video applications. It has been implemented in today’s major media carriers such as Youtube and Netflix. It enables new video applications to fully utilize the existing physical IP network infrastructure. For new 3D immersive medias such as Virtual Reality and 360-degree videos are drawing great attentions from both consumers and researchers in recent years. One of the biggest challenges in streaming such 3D media is the high band width demands and video quality. A new Tile-based video is introduced in both video codec and streaming layer to reduce the transferred media size. In this dissertation, we propose a Software-Defined Network (SDN) approach in an Application-Aware Network (AAN) platform. We first present an architecture for our approach and then show how this architecture can be applied to two aforementioned application areas. Our approach provides both underlying network functions and application level forwarding logics for Hadoop MapReduce and video streaming. By incorporating a comprehensive view of the network, the SDN controller can optimize MapReduce work loads and DASH flows for videos by application-aware traffic reroute. We quantify the improvement for both Hadoop and MPEG-DASH in terms of job completion time and user’s quality of experience (QoE), respectively. Based on our experiments, we observed that our AAN platform for Hadoop MapReduce job optimization offer a significant improvement compared to a static, traditional IP network environment by reducing job run time by 16% to 300% for various MapReduce benchmark jobs. As for MPEG-DASH based video streaming, we can increase user perceived video bitrate by 100%.eng
dc.description.tableofcontentsIntroduction -- Research survey -- Proposed architecture -- AAN-SDN for Hadoop -- Study of User QoE Improvement for Dynamic Adaptive Streaming over HTTP (MPEG-DASH) -- AAN-SDN For MPEG-DASH -- Conclusion -- Appendix A. Mininet Topology Source Code For DASH Setup -- Appendix B. Hadoop Installation Source Code -- Appendix C. Openvswitch Installation Source Code -- Appendix D. HiBench Installation Guide
dc.format.extentxv, 136 pages
dc.identifier.urihttps://hdl.handle.net/10355/61850
dc.publisherUniversity of Missouri--Kansas Cityeng
dc.subject.lcshComputer networks -- Design
dc.subject.lcshHadoop (Computer program)
dc.subject.lcshStreaming video
dc.subject.otherDissertation -- University of Missouri--Kansas City -- Computer science
dc.titleApplication-Aware Network Design Using Software Defined Networking for Application Performance Optimization for Big Data and Video Streamingeng
dc.typeThesiseng
thesis.degree.disciplineTelecommunications and Computer Networking (UMKC)
thesis.degree.disciplineComputer Science (UMKC)
thesis.degree.grantorUniversity of Missouri--Kansas City
thesis.degree.levelDoctoral
thesis.degree.namePh.D.


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