IQ-ECho: Middleware Principles for Real-time Interaction Across Heterogeneous Hardware/Software Platforms

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IQ-ECho Middleware Principles for Real-time
Interaction Across Heterogeneous
Hardware/Software Platforms

• Karsten Schwan
• Greg Eisenhauer
• Matt Wolf
• Mustaq Ahamad
• (Nagi Rao - ORNL
• Constantinos Dovrolis)
• College of ComputingGeorgia Tech
• schwan/eisen/mwolf_at_cc.gatech.edu
• http//www.cc.gatech.edu/systems/projects/IQECho/

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High End Users and Displays
Large-scale Collaborative Applications on
Heterogeneous Systems Terastream Services and
Teragrid
Wireless Users and Displays
GT
High Performance Data Streaming
Data Transport
capture, transport, filter, select, sample,
re-route
Data Source (e.g., spallation neutron source)
Transform
Specialize

ORNL
Teragrid Atlanta Hub
Cluster Computer Terastream Server
High End Users and Displays
Instrumented Testbeds/Facilities (e.g., for
spallation neutron source)
Emory University
Visualization
Scalable Services
Real-time Collaboration and Inspection
Data Cache
Instrumented Testbed/Facility
Local Users
Caching, Recovery, Logging, Security
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Real-time Collaboration Molecular Dynamics

• Requirements
• Multiple collaborators explore common data space
• Personalized views, with ability to annotate and
  manipulate
• Real-time sharing of data, even between different
  representations

• Mechanical Engineering
• Physics

• Chemistry
• Aerospace Engineering

Twinning Plane
FCC
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IQ-ECho Middleware Principles for Network-aware
Collaboration

• Adaptive Peer-to-Peer Data Exchange
• IQ-ECho High performance events
• Event-based peer-to-peer streaming data
  communications -binary data exchanges (PBIO) for
  interactive apps (steering, real-time
  collaboration, )
• Source-based filtering IQ-services deployed to
  meet required application QoS, i.e., by
  disposition of application-specific code into
  remote sites and underlying platform
• Dynamic quality attributes coordinated
  adaptation of platform (e.g., communication
  protocols) and of interactive applications
• Network-awareness adaptive communications (with
  Nagi Rao/ORNL, Constantinos Dovrolis/GT)
• Runtime detection of congestion
• Runtime response adaptation re-routing,
  concurrent paths, coordinated protocol/applicatio
  n response (IQ-RUDP)

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Real-time Collaboration with IQ-ECho
Filters
Adaptive Source-based Filtering
Dynamic Quality Attributes
Multiple Event Types
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Types of adaptation

• Middleware- and/or Network-level
• Frequency
• Same amount of data but different rate
• Resolution
• Same rate, different amount
• Reliability
• Changing proportion of discardable packets
• Multiple Connections
• Protecting critical connections from large-data
  traffic

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Adaptive Communication

• Adapt what?
• Congestion windows data rates
• Issues
• Transport cannot delegate all adaptation choices
  to applications and still be fair to the network
• Applications cannot delegate all adaptation to
  the transport without limiting their choices or
  incurring difficulties (e.g., QoS translation)
• Goal
• provide a mechanism to allow effective
  application adaptations while remaining
  network-friendly

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Coordinated Adaptation

• Use quality attributes to share information
  across middleware/protocol - IQ-Services
• Coordination methods Services/Protocol to
  address
• Conflicting adaptations
• Combined effect of adaptation that may lead to
  overreaction
• Limited application adaptation granularity
• Others, ...
• Problems important in networks where (delay
  bandwidth) is large
• cost of adaptation
• delay before correction of mistakes

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Middleware/Protocol Interactions

• IQ-Services in Middleware
• Application-relevant data manipulation
• Data prioritizers, data filters, downsamplers
• Controlled by dynamic quality attributes
• On-line Network Measurement
• e.g., Raos TCP-based methods
• Using an Instrumented Protocol IQ-RUDP extends
  Reliable UDP
• TCP-friendly congestion control (LDA algorithm)
• Exposes network performance metrics
• Supports application-registered callbacks
• Application-controlled adaptive reliability

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Middleware Architecture
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Evaluation of Coordinated Adaptation

• How effective is coordination in two-layer
  adaptations?
• Metric is smoothness of delay over time
• Evaluate three cases where coordination is
  necessary
• Hold application traffic pattern constant, vary
  network bandwidth
• iperf used to generate background traffic
• Hold network bandwidth constant, vary application
  traffic
• Emulate content delivery server using MBONE trace
• Drive adaptations using callbacks on error ratio

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Example Conflicting Adaptations

• No Coordination
• Transport unaware of adaptation
• All packets sent regardless of priority
• More unmarked packets delivered
• Larger delay for marked packets
• Coordination
• Transport can drop non-priority packets
• Better delay/jitter for high priority packets
• Average delay improves due to spacing

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Conflicting Adaptations

• IQ-RUDP (on right) achieves lower avg delay
• (emulation results)

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Example Metadata-based Filtering

• IQ-RUDP (on right) achieves substantially
• higher frame rate (measured results)

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Conclusions and Status

• Key technologies
• Adaptive, lightweight middleware services
• software release of IQ-ECho available soon
  (installation at ORNL in progress)
• Coordinated middleware/network (re)actions
  (through quality attributes)
• generalizes to other network efforts (e.g.,
  Net100)
• Heterogeneous, distributed collaboration with
  high end data streams
• Smartpointer (MD - SC2002)
• Evaluation on wide area networks
• Internet, GT/ORNL link (yet to come)
• Focus on integration
• MxN services, AG 2.x

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Ongoing Efforts and Leverage

• Deployment and Evaluation (Year 3)
• Realistic applications and testbeds
• deploy remote collaboration infrastructure (with
  ORNL) and experiment across ORNL/GT Gigabit
  Testbed (with N. Rao, ORNL)
• experiment with other data sets (e.g.,
  spallation neutron source), other protocols,
  other network measurement methods (NSF/DOE)
• CCA/OGSI integration
• CCA integration use MxN service as challenge
  example (joint with James Kohl - ORNL)
• OGSI integration challenge example remote
  graphics services for AG-gt OGSI, directory
  services
• Leverage CERCS and GT/ORNL efforts
• NSF Netreact project
• integrated network measurement - w. Dovrolis, Rao
• NSF XML project - dynamic metadata
• Teragrid and GT/ORNL and GT/NRL high end network
  links

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Future Work

• Platform resources effective deployment
• Servers real-time data transformation with the
  Terastream server (utilizing end points!)
• Networks
• application-specific processing on programmable
  routers
• utilizing high end links, e.g.,Teragrid
• Dynamic data interoperability
• heterogeneous data, using XML markups
• automating XML/binary translations
• Protected services
• controlling IQ-service execution

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Publications

• Qi He and Karsten Schwan, IQ-RUDP Coordinating
  Application Adaptation with Network Transport,
  High Performance Distributed Computing (HPDC-11),
  July 2002.
• Matt Wolf, Zhongtang Cai, Weiyun Huang, Karsten
  Schwan, SmartPointers Personalized Scientific
  Data Portals in Your Hand'', Supercomputing 2002.
• Fabian Bustamante, Patrick Widener, Karsten
  Schwan, Scalable Directory Services Using
  Proactivity'', Supercomputing 2002.
• Patrick Widener, Greg Eisenhauer, Karsten
  Schwan, and Fabián E. Bustamante, "Open Metadata
  Formats Efficient XML-Based Communication for
  High Performance Computing", Cluster Computing
  The Journal of Networks, Software Tools, and
  Applications, 2003.
• Greg Eisenhauer, Fabián Bustamante and Karsten
  Schwan, "Native Data Representation An
  Efficient Wire Format for High-Performance
  Computing", IEEE Transactions on Parallel and
  Distributed Systems, 2003.