1. An Introduction to the Prescience Lab Peter A. Dinda Prescience Lab Department of Computer Science Northwestern University http://plab.cs.northwestern.edu

2. 2 Outline Motivations Questions Projects Conclusions

3. 3 How do we deliver arbitrary amounts of computational power to ordinary people? Assumptions: Shared computing environments, Limited utility of reservations

4. 4 How do we deliver arbitrary amounts of computational power to ordinary people? Distributed and Parallel Computing Interactive Applications

5. 5 How do we build adaptive distributed interactive applications effectively? How does the demand for resources in these applications vary over time? How does the supply of resources vary over time? How can we use the adaptation mechanisms exposed by an application to match its resource demand with resource supply?

6. 6 How do we build adaptive distributed interactive applications effectively? Applications –Virtualized Audio Immersive audio –Interactive visualization of massive datasets Frameworks –Virtuoso Grid computing using virtual machines –Dv

7. 7 Virtualized Audio (with Dong Lu, Curtis Barrett) Distributed Computational Resources Other Users or Audio Sources Microphones, Headphones GPS, head-tracking Wireless connectivity Limited local computation

8. 8 Virtualized Audio: Interactive Auralization Listener at Virtual Location Headphones Auralization Sound Field 2 Virtual Performer HRTF ListenerPerformerRoom Virtual Listening Room Auralization injects performer into listener’s space Auralization adapts as listener moves or room changes Recomputes impulse responses

9. 9 Architecture of Interactive Auralization Client Scalable Real-time Simulation Server Master filtering server Mixing server Filtering server Streaming Audio Service Source 1 Source 2 Source 3 Source 4 Filtering server Source n Filter configuration Left Channel Right Channel Scalable Audio Filtering Service Parallel FD Simulation Filter generation Binaural Audio Output Current Spatial Model and source/sink positions User-driven Immersive Audio Client Impulse response filters characterize user’s space

10. 10 Adaptation in Virtualized Audio Numerous mechanisms Sampling rate, impulse response length, algorithm for computing impulse response, filter approximations, server selection, … Can vary computational load over many orders of magnitude Compute/communicate ratio is huge How do we use these mechanisms to achieve consistent real-time response?

11. 11 Virtuoso (with Renato Figueiredo, Jose Fortes, Ananth Sundararaj, Ashish Gupta) Make Grids like PCs –User gets raw machine(s) –Machine appears to be on his network –User can install what he needs as owner Lower level of abstraction –Classic virtual machine monitors –Virtual networking Middleware support –Instantiation, migration of machines –Connectivity to remote files, machines –Resource control

12. 12 Classic Virtual Machine: VMWare

13. 13 Why Virtual Networking? A machine running is suddenly plugged into your network. What happens? –Does it get an IP address? –Is it a routeable address? –Does firewall let its traffic through? –To any port? Virtual machine hostile environment

14. 14 A Simple Layer 2 Virtual Network ClientServer Remote VM Physical NIC VM monitor Virtual NIC Physical NIC SSH Hostile Remote NetworkFriendly Local Network

15. 15 A Simple Layer 2 Virtual Network ClientServer Remote VM Physical NIC VM monitor Virtual NIC Physical NIC SSH Hostile Remote NetworkFriendly Local Network

16. 16 A Simple Layer 2 Virtual Network ClientServer Remote VM Physical NIC VM monitorBridge Virtual NIC Physical NIC SSH Tunnel Hostile Remote NetworkFriendly Local Network

17. 17 Bootstrapping the Virtual Network Star topology always possible TCP session from client must have been possible Better topology may be possible Depends on security at each site Topology may change Virtual machines can migrate Bootstrap to higher layers Virtual filesystems

18. 18 How does the demand for resources vary over time? How does the supply of resources vary over time? Resource demand in interactive applications –Instrumented games, preceding applications, … –Not much is known here Resource supply in distributed environments –URGIS Grid Information based on the relational data model –GridG –Clairvoyance Online resource prediction for hosts and networks –Tsunami Wavelet-based approaches to information dissemination –Diffusion Zero-cost information dissemination

19. 19 URGIS (with Beth Plale, Dong Lu) Unified Relational Grid Information Services –GIS based on the relational data model –Leverage results from database community –Northwestern work: MySQL, Oracle RDBMSes Compositional queries –Application-specific information aggregration –Like decision support queries (TPC-H) Support for information of varying dynamicity –Varying update rates and freshness requirements –Seamless inclusion of streaming data A common data model and query language –Powerful, high level, declarative, easy-to-optimize

20. 20 Compositional Queries “Find four different hosts with a total memory between 512 MB and 1 GB” “Find all available sensors and predictors that provide information about the network path between a and b” “Tell me when the load on any of these four hosts diverges from the average by more than 50%”

21. 21 Example select host1.name, host2.name, host3.name, host4.name, hd1.mem+hd2.mem+hd3.mem+hd4.memasTotalMem, from hosts as host1,hostdataas hd1, hosts as host2,hostdataas hd2, hosts as host3,hostdataas hd3, hosts as host4,hostdataas hd4 where host1.ip=hd1.ipand host2.ip=hd2.ipand host3.ip=hd3.ipand host4.ip=hd4.ipand hd1.mem+hd2.mem+hd3.mem+hd4.mem>=512 and hd1.mem+hd2.mem+hd3.mem+hd4.mem<=1024 and host1.ip!=host2.ipand host1.ip!=host3.ipand host1.ip!=host4.ipand host2.ip!=host3.ipand host2.ip!=host4.ipand host3.ip!=host4.ip order by TotalMem desc limit 10

22. 22 Time- bounded, non- deterministic queries select nondeterministically host1.name, host2.name, host3.name, host4.name, hd1.mem+hd2.mem+hd3.mem+hd4.memasTotalMem, from hosts as host1,hostdataas hd1, hosts as host2,hostdataas hd2, hosts as host3,hostdataas hd3, hosts as host4,hostdataas hd4 where host1.ip=hd1.ipand host2.ip=hd2.ipand host3.ip=hd3.ipand host4.ip=hd4.ipand hd1.mem+hd2.mem+hd3.mem+hd4.mem>=512 and hd1.mem+hd2.mem+hd3.mem+hd4.mem<=1024 and host1.ip!=host2.ipand host1.ip!=host3.ipand host1.ip!=host4.ipand host2.ip!=host3.ipand host2.ip!=host4.ipand host3.ip!=host4.ip order by TotalMem desc limit 10 inlessthan 5 seconds usingheuristic prefer_depth_first

23. 23 Implementation of Non-deterministic, Time-bounded Queries Random number associated with each row in each table (or insert) Query is rewritten to incorporate a random ranges on the input tables Range lengths chosen to meet deadline –This is not trivial and we don’t have this translation yet Heuristics not yet incorporated Hopefully RDBMS-independent

24. 24 RGIS1 Non-deterministic Query Performance Find n hosts with a total memory of 1 GB of memory 100,000 hosts

25. 25 RGIS1 Non-deterministic Query Performance Find 2 hosts with a total memory of 1 GB of memory 100,000 hosts

26. 26 Clairvoyance (with Jason Skicewicz, Yi Qiao) Measure, Characterize, Predict, and Disseminate information about dynamic resource supply Resource signals –Discrete-time signals strongly correlated with resource supply –Currently, univariate, working on multivariate –Currently Host load Windows performance counters (using WatchTower) Network flow bandwidth and latency (using Remos) Any text-based source Online predictive modeling –Simple models (MEAN, BESTMEAN, BESTMEDIAN, LAST…) –Box/Jenkins Models (AR, MA, ARMA, ARIMA,…) –Fractional ARIMAs –Nonlinear modeling (TARs, Wavelet-decompositions)

27. 27 RPS Toolkit Extensible toolkit for implementing resource signal prediction systems [CMU-CS-99-138] Growing: RTA, RTSA, Wavelets, GUI, etc Easy “buy-in” for users C++ and sockets (no threads) Prebuilt prediction components Libraries (sensors, time series, communication)

28. 28 Measurement and Prediction

29. 29 Multiscale Network Prediction Large, recent study of predictability Hundreds of NLANR and other traces –Mostly WANs Different resolutions –Binning and low-pass via wavelets Sweet Spot –Predictability often maximized at particular resolution

30. 30 Multiresolution Prediction Example

31. 31 Tsumami (with Jason Skicewicz) Efficient dissemination of resource signals Wavelet-based methods for characterization, modeling, and prediction Tsumani toolkit will ship with the next RPS release

32. 32 The Tension Sensor Video App Course-grain measurement Resource- appropriate measurement Fine-grain measurement Grid App … Resource Signal (periodic sampling) Example: host load

33. 33 Proposed System Wavelet Transform Level 0 Sensor Inverse Wavelet Transform Application Level M-1 Level M Level 0 Level L Network Application receives levels based on its needs StreamInterval

34. 34 Delay Transforms introduce sample delay –Depends on number of levels and type of filter used –Exponential in the number of levels –Affects both streaming and block transforms –Seemingly inherent for wavelets Exploit prediction –Limited success Exploit “wavelet-like” decompositions –Trade-off between reconstruction accuracy and delay –Existing theory. Our evaluation not done yet.

35. 35 Wavelets and Prediction Predict each level of transformed signal separately –“Detail signals” Surprisingly ineffective in practice Whitens the signal –“Approximation signals” Smoothing, used in network prediction work discussed earlier Reasonably effective, worth pursuing

36. 36 Diffusion (with Brian Cornell, Jack Lange) Efficient dissemination of resource signals Piggyback additional information on existing packet transfers –No additional packets –Packet size unchanged Evaluations with traces, Minet Implementation as Linux kernel module >=86 bits per packet possible 17 bits per packet verified Zero Cost Information Dissemination

37. 37 Diffusion Implementation App Transport Network Data Link Physical App Transport Network Data Link Physical Sensor Header Editing Consumer Data Extraction Sensor data piggybacked on application packets

38. 38 SpyTalk

39. 39 How can we use the adaptation mechanisms exposed by an application to match its resource demand with resource supply? Application-level performance predictions –Running Time Advisor Confidence interval for running time of a task on a particular host –Message Time Advisor Confidence interval for transfer time of a message Adaptation advisors –Real-time Scheduling Advisor Choose which host of a set on which a task is most likely to meet its deadline Real-time  responsiveness requirement Service for interactive applications

40. 40 Running Time Advisor

41. 41 Real-time Scheduling Advisor

42. 42 How do we build adaptive distributed interactive applications effectively? How does the demand for resources in these applications vary over time? How does the supply of resources vary over time? How can we use the adaptation mechanisms exposed by an application to match its resource demand with resource supply?

43. 43 How do we deliver arbitrary amounts of computational power to ordinary people? Distributed and Parallel Computing Interactive Applications

44. 44 Future Directions Continue pushing on projects discussed New directly related projects –Interactive hierarchical visualization of huge datasets –Resource demand characterization, modeling, and prediction Other directions –Intrusion detection using signal processing

45. 45 For More Information Peter Dinda –http://www.cs.northwestern.edu/~pdinda Prescience Lab –http://plab.cs.northwestern.edu