User and Network Interplay in Internet Telemicroscopy

Prasad Calyam (Presenter)Nathan Howes, Mark Haffner, Abdul Kalash

Ohio Supercomputer Center, The Ohio State University

IMMERSCOM, October 11th 2007

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Topics of Discussion

Telemicroscopy OverviewMotivationUse-casesSolutions

Telemicroscopy Session ModelUser and Network Interplay

Testbed for Experiments to Characterize Model ParametersPerformance AnalysisOSC’s Remote Instrumentation Collaboration Environment (RICE)

FeaturesDemo Video

Conclusion

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Telemicroscopy OverviewAcademia and Industry use computer-controlled scientific instruments

Electron Microscopes, NMR, Raman Spectrometers, Nuclear AcceleratorFor research and training purposes

Cancer Cure, Material Science, NanotechnologyInstruments are expensive ($450K - $ 4Million) and need dedicated staff to maintain

+) Remote instrumentation benefits Access to users who cannot afford to buy instrumentsReturn on Investment (ROI) for instrument labsAvoids duplication of instrument investments for funding agencies (NSF, OBOR)Useful when physical presence of humans around sample is undesirable

-) Remote instrumentation drawbacksImproper operation can cause physical damages that are expensive to repair

Telemicroscopy is remote instrumentation of electron microscopes

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Telemicroscopy Use-casesTele-observation versus Tele-operation

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Telemicroscopy SolutionsHardware-based: KVM over IP (KVMoIP)

Encoder-Decoder pair for frame-differencing based video image transfersPros: High quality video and optimal response timesCons: Expensive, Special hardware and high-end bandwidth requirements

Software-based: VNC – remote desktop softwareRaw or copy-rectangle or JPEG/MPEG encoded video image transfersPros: Inexpensive, Easily deployableCons: Improper PC hardware or network congestion can degrade video quality and optimal control response times

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Related WorkTelemicroscopy over Internet2

Gemini ObservatoryNanoManipulator

Telescience Project – National Center for Microscopy and Imaging Research, UC San DiegoUltrahigh Voltage Electron Microscope Research Center – Osaka UniversityCommon Instrument Middleware Architecture (CIMA) – Indiana UniversityTele-presence Microscopy – Argonne National Lab’s Advanced Analytical Electron Microscope facility

+) Novel applications for controlling instruments

+) All said “it works” over XYZ network paths and listed challenges they overcame

-) None have quantified performance in terms of network effects

-) None have considered user Quality of Experience (QoE)

Study Motivation: Understanding User and Network interplay can help us improve reliability and efficiency of Telemicroscopy and thus deliver optimum user QoE

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→ user-activity (key strokes and mouse clicks) during a session involving n microscope functions

→ average video image transfer rate at the microscope end→ network connection quality→ input-output scaling factor; unique to a microscope function→ seed image transfer rate; for quick screen refresh→ average video image transfer rate at the user end→ system-state control parameter dependent on user

behavior; causes ± feedback in the control system

Telemicroscopy Session Model

(a) Session Model Parameters (b) Closed-loop Control System Representation

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Telemicroscopy Session Model

(a) Session Model Parameters (b) Closed-loop Control System Representation

(c) Transfer Function

(d) End-user QoE relation in a Telemicroscopy session

Demand – Effort the user had to expend to perform n actionsSupply – Perceivable video image quality during the n actions

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Telemicroscopy System States(Effects of H parameter)

(a) State Transitions

(b) System Supply-Demand Performance

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Case Study: OSC Collaboration with OSU CAMMOSU Center for Accelerated Maturation of Materials (CAMM) has acquired high-end Electron Microscopes

Used for materials modeling studies at sub-angstrom levelOSC providing systems and networking support for Telemicroscopy

OSCnet supporting end-to-end bandwidth requirementsImage processing of samples (automation with MATLAB) for Analytics service

Telemicroscopy DemonstrationsSupercomputing, Tampa, FL (Nov 2006)Internet2 Fall Member Meeting, Chicago, IL (Dec 2006)Stark State University/Timken, Canton, OH (Mar 2007)

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Telemicroscopy Testbed

Experiments to characterize session model parametersTest cases with different network connections – CAMM requirements

(a) 1 Gbps LAN (Direct connection to Users in neighboring room)(b) Isolated LAN (Users in the same building )(c) Public LAN (Users in different buildings on campus)(d) WAN (Users on the Internet)

Performance analysis goalsBandwidth, latency and packet loss levels for optimum user QoETraffic characterization for studying inter-play between user control (TCP traffic) and microscope response (UDP traffic)

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WAN Testbed

(a) Setup

(b) WAN Path Performance

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Performance Measurements CollectedEnd-user QoE Measurements (Subjective Metrics)

Mean Opinion Scores (MOS) of “Novice” and “Expert” UsersTime for completion of “basic” and “advanced” Tele-microscopy tasks by Novice and Expert Users

Network Measurements (Objective Metrics)Collected using Ethereal/TCPdump and OSC ActiveMon

Metrics: Data rate, Protocols Summary

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Network Connection Quality (ψnet) and User QoE (qmos)

qmos notably decreases with decrease in network connection qualityUser QoE is highly sensitive to network health fluctuations

Novice more liberal than ExpertTime taken to complete a task increases with decrease in network connection quality

NOTE: qmos of 5 corresponds to “at the microscope” QoE

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Network Connection Quality (ψnet) and User Control (bin)Mouse and Keyboard traffic is TCP trafficHigher TCP throughput on poor network connections

Increased user effort with keyboard and mouse on poor connections“Congestion begets more congestion”

Task-1 Task-2 Task-3

Task-1 Task-2 Task-3Task-1 Task-2 Task-3

1 Gbps LAN – Expert

Public 100 Mbps LAN – Expert 100 Mbps WAN – Expert

User expends minimum effort with keyboard and

mouse to complete use-caseUser expends notably more

effort with keyboard and mouse to complete use-case

User expends a “lot” of effort with keyboard and mouse to

complete use-case

1400 B/s

140 s

900 B/s

100 s

60 B/s

60 s

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Network Connection Quality (ψnet) and Image Transfer Rate (∆bout)

“At the microscope” QoE requires ~30 Mbps between user and microscope endsOther WAN tests at SC06 (Tampa) and Internet2 FallMM (Chicago) to microscopes at CAMM (Columbus)

Usable on ~(10-25) Mbps WAN connectionsUsable if one-way network delays within ~50ms; as much as ~20% UDP packet losstolerable if adequate bandwidth provisioned

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OSC’s Remote Instrumentation CollabrationEnvironment (RICE)

Leverages our user and network interplay studies for “reliable” and “efficient”Telemicroscopy sessions and thus delivers optimum user QoECustomizable software on custom server-side hardware for Telemicroscopy

Best of VNC and KVMoIP worldsRICE Features

Network-aware video encodingOptimizes frame rates based on available network bandwidth Manual video-quality adjustment slider

Network-status and user-action blocking Warns user of network congestion that leads to unstable session state Blocks user-actions during extreme congestion scenarios and prevents breakdown

Collaboration tools VoIP, Chat, Annotation, Command-abstraction

Multi-user support Control-lock passing, collaborators presence, colored-text chat conference

Workflow and Image management Simultaneously connects to multiple PCs, transfers images and transparently switches between them

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RICE Demo Video

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RICE use-cases for online learningRemote students can view instructor (also remote!) controlling different types of scientific instruments

Efficiently – with the appropriate video frames to match last mile network capabilitiesReliably – without worrying about damaging the instrumentMulti-party VoIP and Chat collaborationImage Annotation

Instructor can pass control to students - train them to operate the instrument during the classStudents can conduct lab sessions at their assigned slots on theinstrumentsStudents image files can be organized and hosted at a central server

Analytics can be supported using a web-service to analyze the image data sets

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Future WorkShared instrumentation uses OSC’s state-wide resources

Networking, Storage, HPC, Analytics

Cyberinfrastructure for Shared Instrumentation

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Shared Instrumentation @ OSC

Plans underway to support shared instrumentation for -Ohio State University: CAMM Electron Microscopes, Chemistry Department Spectrometers and Diffractometers, Astronomy Department TelescopesMiami University: Electron Microscopes, EPR SpectrometersOhio University: Nuclear Accelerator

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Thank you for your attention!☺

Any Questions?