An Introduction to the Prescience Lab

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An Introduction to thePrescience Lab

• Peter A. Dinda
• Prescience Lab
• Department of Computer Science
• Northwestern University
• http//plab.cs.northwestern.edu

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Outline

• Motivations
• Questions
• Projects
• Conclusions

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• How do we deliver arbitrary amounts of
  computational power to ordinary people?

Assumptions Shared computing environments, Li
mited utility of reservations
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Distributed and Parallel Computing

• How do we deliver arbitrary amounts of
  computational power to ordinary people?

Interactive Applications
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• 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?

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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

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Virtualized Audio (with Dong Lu, Curtis Barrett)
Distributed Computational Resources
Other Users or Audio Sources
Microphones, Headphones GPS, head-tracking Wireles
s connectivity Limited local computation
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Virtualized Audio Interactive Auralization
Listener
Performer
Room
Virtual Listening Room
Virtual Performer
Sound Field 2
Auralization
HRTF
Listener at Virtual Location
Headphones

• Auralization injects performer into listeners
  space
• Auralization adapts as listener moves or room
  changes
• Recomputes impulse responses

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Architecture of Interactive Auralization
Impulse response filters characterize users
space
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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?

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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

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Classic Virtual Machine VMWare
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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
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A Simple Layer 2 Virtual Network
Client
Server
VM monitor
SSH
Remote VM
Virtual NIC
Physical NIC
Physical NIC
Hostile Remote Network
Friendly Local Network
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A Simple Layer 2 Virtual Network
Client
Server
VM monitor
SSH
Remote VM
Virtual NIC
Physical NIC
Physical NIC
Hostile Remote Network
Friendly Local Network
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A Simple Layer 2 Virtual Network
Client
Server
VM monitor
Bridge
Bridge
SSH Tunnel
Remote VM
Virtual NIC
Physical NIC
Physical NIC
Hostile Remote Network
Friendly Local Network
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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

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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

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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

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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

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Example
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Time-bounded, non-deterministic queries
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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 dont have this
  translation yet
• Heuristics not yet incorporated
• Hopefully RDBMS-independent

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

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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)

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Measurement and Prediction
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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

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Multiresolution Prediction Example
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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

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The Tension
Video App
Sensor
Fine-grain measurement

Resource-appropriate measurement
Grid App
Resource Signal (periodic sampling) Example
host load
Course-grain measurement
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Proposed System
Application
Sensor
Network
Stream
Interval
Level 0
Level 0
Wavelet Transform
Inverse Wavelet Transform
Level L
Level M-1
Level M
Application receives levels based on its needs
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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.

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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

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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
• gt86 bits per packet possible
• 17 bits per packet verified

Zero Cost Information Dissemination
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Diffusion Implementation
App
App
Sensor
Consumer
Transport
Transport
Network
Network
Header Editing
Data Extraction
Data Link
Data Link
Physical
Physical
Sensor data piggybacked on application packets
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SpyTalk
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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

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Running Time Advisor
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Real-time Scheduling Advisor
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• 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
Distributed and Parallel Computing

• How do we deliver arbitrary amounts of
  computational power to ordinary people?

Interactive Applications
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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

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For MoreInformation

• Peter Dinda
• http//www.cs.northwestern.edu/pdinda
• Prescience Lab
• http//plab.cs.northwestern.edu