An Introduction to the Prescience Lab Peter A. Dinda Prescience Lab Department of Computer Science...

An Introduction to thePrescience Lab

Peter A. DindaPrescience 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,Limited utility of reservations

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Distributed and Parallel Computing

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)

DistributedComputationalResources

Other Users orAudio Sources

Microphones, HeadphonesGPS, head-trackingWireless connectivityLimited local computation

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Virtualized Audio: Interactive Auralization

Listener atVirtual LocationHeadphones

AuralizationSound Field 2

Virtual Performer

HRTF

Listener Performer Room Virtual Listening Room

•Auralization injects performer into listener’s space•Auralization adapts as listener moves or room changes

•Recomputes impulse responses

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Architecture of Interactive Auralization

Client

Scalable Real-time Simulation Server

Master filtering server

Mixing server

Mixing server

Filtering server

Filtering server

Filtering server

Filtering server

Streaming AudioService

Source 1

Source 2

Source 3

Source 4

Filtering server

Filtering server

Source n

Filter configuration

Left Channel

Right Channel

Scalable Audio Filtering Service

Parallel FD Simulation






Filter generation

Binaural AudioOutput

Current Spatial Modeland source/sink positions

User-driven Immersive Audio Client

Impulse response filters characterize user’s 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

Remote VM

PhysicalNIC

VM monitor

VirtualNIC

PhysicalNIC

SSH

Hostile Remote NetworkFriendly Local Network

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Client Server

Remote VM

PhysicalNIC

VM monitor

VirtualNIC

PhysicalNIC

SSH


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Client Server

Remote VM

PhysicalNIC

VM monitorBridge Bridge

VirtualNIC

PhysicalNIC

SSH Tunnel


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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 select host1.name, host2.name, host3.name, host4.name, hd1.mem+hd2.mem+hd3.mem+hd4.mem as TotalMem,

from hosts as host1, hostdata as hd1, hosts as host2, hostdata as hd2, hosts as host3, hostdata as hd3,hosts as host4, hostdata as hd4

where host1.ip=hd1.ip and host2.ip=hd2.ip and

host3.ip=hd3.ip and host4.ip=hd4.ip andhd1.mem+hd2.mem+hd3.mem+hd4.mem>=512 and hd1.mem+hd2.mem+hd3.mem+hd4.mem<=1024 and host1.ip!=host2.ip and host1.ip!=host3.ip and

host1.ip!=host4.ip and host2.ip!=host3.ip andhost2.ip!=host4.ip and host3.ip!=host4.ip

order by TotalMem desc

limit 10

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Time-bounded, non-deterministic queries

select nondeterministicallyhost1.name, host2.name, host3.name, host4.name, hd1.mem+hd2.mem+hd3.mem+hd4.mem as TotalMem,

from hosts as host1, hostdata as hd1, hosts as host2, hostdata as hd2, hosts as host3, hostdata as hd3,hosts as host4, hostdata as hd4

where host1.ip=hd1.ip and host2.ip=hd2.ip and

host3.ip=hd3.ip and host4.ip=hd4.ip andhd1.mem+hd2.mem+hd3.mem+hd4.mem>=512 and hd1.mem+hd2.mem+hd3.mem+hd4.mem<=1024 and host1.ip!=host2.ip and host1.ip!=host3.ip and

host1.ip!=host4.ip and host2.ip!=host3.ip andhost2.ip!=host4.ip and host3.ip!=host4.ip

order by TotalMem desc

limit 10

inlessthan5 seconds

usingheuristicprefer_depth_first

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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 don’t have this translation yet

• Heuristics not yet incorporated• Hopefully RDBMS-independent

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RGIS1 Non-deterministic Query Performance

Find n hosts with a total memory of 1 GB of memory

0.01

0.1

1

1

10

100

1000

1 2 3 4 5Number of Hosts In Join

Query Time

Number ofResults

100,000 hosts

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RGIS1 Non-deterministic Query Performance

Find 2 hosts with a total memory of 1 GB of memory

0.1

1

10

100

1000

1

10

100

1000

10000

100000

1000000

10000000

0.0005 0.001 0.01 0.1Selection Probability

Query Time

Number ofResults

100,000 hosts

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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

0

0.05

0.1

0.15

0.2

0.25

0.3

0.1 1 10 100 1000Bin Size (Seconds)

last

bm(8)

ma(8)

ar(8)

ar(32)

arma(4,4)

arima(4,1,4)

arima(4,2,4)

arfima(4,-1,4)

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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

Sensor

Video App

Network

Course-grain measurement

Resource-appropriate

measurement

Fine-grain measurement

Grid App

…

Resource Signal (periodic sampling)Example: host load

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Proposed System

WaveletTransform

Level 0

Sensor

InverseWavelet

Transform

Application

Level M-1

Level M

Level 0

Level L

Network

Application receives levels based on its needs

Stream Interval

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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• >=86 bits per packet possible• 17 bits per packet verified

Zero CostInformationDissemination

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Diffusion Implementation

App

Transport

Network

Data Link

Physical

App

Transport

Network

Data Link

Physical

Sensor

Header Editing

Consumer

DataExtraction

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?

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Distributed and Parallel Computing

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

An Introduction to the Prescience Lab Peter A. Dinda Prescience Lab Department of Computer Science...

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Transcript of An Introduction to the Prescience Lab Peter A. Dinda Prescience Lab Department of Computer Science...