1. 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 Computing Georgia Tech schwan/eisen/mwolf@cc.gatech.edu http://www.cc.gatech.edu/systems/projects/IQECho/

2. GT Emory University Emory University Cluster Computer Terastream Server Cluster Computer Terastream Server TransformSpecialize High End Users and Displays Teragrid Atlanta Hub High Performance Data Streaming Scalable Services Data Cache capture, transport, filter, select, sample, re-route Large-scale Collaborative Applications on Heterogeneous Systems: Terastream Services and Teragrid Data Transport Visualization Caching, Recovery, Logging, Security Real-time Collaboration and Inspection Wireless Users and Displays Data Source (e.g., spallation neutron source) Instrumented Testbed/Facility Local Users ORNL Instrumented Testbeds/Facilities (e.g., for spallation neutron source) High End Users and Displays

3. 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 Undamaged Damaged Undamaged v s. W / and L /  interatomic spacing L W L W Mechanical Engineering Physics Chemistry Aerospace Engineering FC C Twinning Plane

4. 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/application response (IQ-RUDP)

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

6. 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

7. 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

8. 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

9. 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., Rao’s 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

10. Middleware Architecture

11. 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

12. 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

13. Conflicting Adaptations IQ-RUDP (on right) achieves lower avg delay (emulation results)

14. Example: Metadata-based Filtering IQ-RUDP (on right) achieves substantially higher frame rate (measured results)

15. 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

16. 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- > 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

17. 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

18. 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.