dc.contributor.advisor | Medhi, Deepankar | |
dc.contributor.author | Zhao, Shuai | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017 Summer | |
dc.description | Title from PDF of title page viewed October 30, 2017 | |
dc.description | Dissertation advisor: Deep Medhi | |
dc.description | Vita | |
dc.description | Includes bibliographical references (pages 122-135) | |
dc.description | Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017 | |
dc.description.abstract | This 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.tableofcontents | Introduction -- 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.extent | xv, 136 pages | |
dc.identifier.uri | https://hdl.handle.net/10355/61850 | |
dc.publisher | University of Missouri--Kansas City | eng |
dc.subject.lcsh | Computer networks -- Design | |
dc.subject.lcsh | Hadoop (Computer program) | |
dc.subject.lcsh | Streaming video | |
dc.subject.other | Dissertation -- University of Missouri--Kansas City -- Computer science | |
dc.title | Application-Aware Network Design Using Software Defined Networking for Application Performance Optimization for Big Data and Video Streaming | eng |
dc.type | Thesis | eng |
thesis.degree.discipline | Telecommunications and Computer Networking (UMKC) | |
thesis.degree.discipline | Computer Science (UMKC) | |
thesis.degree.grantor | University of Missouri--Kansas City | |
thesis.degree.level | Doctoral | |
thesis.degree.name | Ph.D. | |