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Jerry Adams 1 , Bradley Hittle 2 , Eliot Prokop 3 , Ronny Antequera 3 , Dr.Prasad Calyam 3

Improving Remote Access of a Data-Intensive Computing Application: Effects of Encoding and GPU Virtualization. Jerry Adams 1 , Bradley Hittle 2 , Eliot Prokop 3 , Ronny Antequera 3 , Dr.Prasad Calyam 3

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Jerry Adams 1 , Bradley Hittle 2 , Eliot Prokop 3 , Ronny Antequera 3 , Dr.Prasad Calyam 3

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  1. Improving Remote Access of a Data-Intensive Computing Application: Effects of Encoding and GPU Virtualization Jerry Adams1, Bradley Hittle2, Eliot Prokop3, Ronny Antequera3, Dr.Prasad Calyam3 University of Hawaii-West Oahu1, The Ohio State University2, University of Missouri-Columbia3 • Results • Encoding: • Subjective results show that Tight, Ultra or ZlibHex are the best choice for high latency or low bandwidth networks • Objective results also show the same 3 encoding types are among those that have the fastest response time • Encoding • VNC (which uses RFB protocol) is used to connect to server • Encoding refers to the encoding of image data pixels that are generated by RFB and transported by VNC • 10 Encoding types: Tight, ZRLE, Zlib, ZlibHex, Ultra, Hextile, RRE, ZYWRLE, CoRRE and Raw • Introduction • Remote “Desktop” Access (RDA) with exceptional user Quality of Experience (QoE) is needed for data-intensive computing • Impractical to carry or download for computation • A decision tree model uses context awareness through feedback loops by adjusting Quality of Application (QoA) and Quality of Service (QoS) to improve the overall user QoE • QoE improvements this project explores: • Encoding types • Graphic Processing Unit (GPU) Virtualization • The data-intensive application used as a case study is RIVVIR that is being used in the field of Small Animal Imaging Figure 2: Lossless RLE pixel encoding (python.dzone.com) Case Study: RIVVIR application Ohio State U. • GPU Virtualization • Hypervisor virtualizes a physical GPU to support multiple virtual desktops • Application and user feel as if they have a dedicated GPU • Remote Interactive Volume Visualization Infrastructure for Researchers (RIVVIR) • Remote access via thin-clients • GPU-based data-intensive volume visualization for MRI viewing (typical file size is ~0.5 GB) Figure 4: Objective results from wired network experiment; Tight encoding has least bandwidth consumption at .45 Mbps • GPU Virtualization: • Results show that RIVVIR appears capable of handling five clients simultaneously without loss of QoE U. of Missouri Figure 3: GPU virtualization setup on RIVVIR using a 3D X Server and VNC Server • Experiments • Encoding: Testing of encoding types on different networks • Objective metrics: Bandwidth Consumed in Mbps • Subjective metric: User QoE in tournament model • GPU Virtualization: Variety of end devices connected to virtual desktop displays • Objective Metrics: GPU Utilization %, Concurrent User Load Internet This material is based upon work supported by National Science Foundation under award numbers CNS-1205658 and CNS-1359125. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of National Science Foundation. Figure 1: End devices at Uni. Of Missouri access MRI’s over the Internet with RIVVIR hosted at The Ohio State University

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