Electronic Visualization Laboratory University of Illinois at Chicago EMERGE Deep Tech Mtg Oliver Yu, Jason Leigh, Alan Verlo

Electronic Visualization Laboratory University of Illinois at Chicago Performance Parameters Latency= Recv Time - Send Time Note: Recv Host and Send Host are synchronized. Jitter = E [{L i - E [L] }] –Note: E [ ] is the expection of data set. L is the set of 100 most recent Latency samples. Packet Loss Rate

Electronic Visualization Laboratory University of Illinois at Chicago

Forward error correction scheme for low- latency delivery of error sensitive data Ray Fang, Dan Schonfeld, Rashid Ansari Transmit redundant data over high bandwidth networks that can be used for error correcting UDP streams to achieve lower latency than TCP. Transmit redundant data to improve quality of streamed video by correcting for lost packets.

Electronic Visualization Laboratory University of Illinois at Chicago FEC Experiments EVL to SARA- Amsterdam (40Mb/s 200ms RT latency) Broader Ques: –Can FEC provide a benefit? How much? –Tradeoff between redundancy and benefit? Specific Ques: –TCP vs UDP vs FEC/UDP –How much jitter does FEC introduce? –High thru put UDP vs FEC/UDP to observe loss & recovery –UDP vs FEC with background traffic –FEC over QoS: WFQ or WRED congestion management- hypothesis: WRED is bad for FEC

Electronic Visualization Laboratory University of Illinois at Chicago UDP vs TCP vs FEC/UDP with 3:1 redundancy UDP Latency (ms) TCP Latency (ms) FEC over UDP Latency (ms) B Packet size (bytes)

Electronic Visualization Laboratory University of Illinois at Chicago FEC greatest benefit is in small packets. Larger packets impose greater overhead. As redundancy decreases FEC approaches UDP.

Electronic Visualization Laboratory University of Illinois at Chicago Packet Loss over UDP vs FEC/UDP Data Rate (bits/s) Packet Size (Bytes) Packet Loss Rate in UDP (%) Packet Loss Rate in FEC over UDP (%) 1M M M M M M UDP FEC

Electronic Visualization Laboratory University of Illinois at Chicago Application Level Experiments Two possible candidates for instrumentation and testing over EMERGE: –Teleimmersive Data Explorer (TIDE) – Nikita Sawant, Chris Scharver –Collaborative Image Based Rendering Viewer (CIBR View) – Jason Leigh, Steve Lau [LBL]

Electronic Visualization Laboratory University of Illinois at Chicago TIDE

Electronic Visualization Laboratory University of Illinois at Chicago CIBR View

Electronic Visualization Laboratory University of Illinois at Chicago Common Characteristics of both Teleimmersive Applications

Electronic Visualization Laboratory University of Illinois at Chicago Research Goal: –Hope to see improved performance with QoS and/or TCP tuning enabled. –Monitor applications and characterize their network characteristics as it stands over non-QoS enabled networks. –Idenitfy & remove bottlenecks in the application. –Monitor again to verify bottlenecks removed. –Monitor over QoS enabled networks. –End result is a collection of techniques and tools to help tune similar classes of collaborative distributed applications. Instrumentation: Time, Info (to identify a flow), Event (to mark a special event), Inter-msg delay, 1-way latency, Read bw, Send bw, Total read, Total sent TIME= INFO=Idesk_cray_avatar EVENT=new_avatar_entered MIN_IMD= AVG_IMD= MAX_IMD= INST_IMD= MIN_LAT= AVG_LAT= MAX_LAT= INST_LAT= AVG_RBW= INST_RBW= AVG_SBW= INST_SBW= TOTAL_READ=19019 TOTAL_SENT=110033

Electronic Visualization Laboratory University of Illinois at Chicago Characterization of TIDE & CIBRview streams Estimated bandwidth (bits/s) DiffServ TypesBurstiness Latency sensitive Jitter sensitive Error sensitive UDP avatar 6K x n (15fps) Interactive Real-time ConstantYYN UDP audio stream 64K x nBriefYYN UDP video stream 10M (2-way only) ConstantYYYN UDP stream With Playback depends Non- interactive Real-time ConstantYNYN TCP control data 7K x nReliableBriefYN Y TCP bulk data dependsBest Effort Sustained burst NNY

Electronic Visualization Laboratory University of Illinois at Chicago QoSiMoto: QoS Internet Monitoring Tool Kyoung Park Reads Netlogger data sets from file or from netlogger daemon. CAVE application runs on SGI and Linux Information Visualization problem. How to leverage 3D. Averaging of data points over long traces.