Dynamic Mapping of Activation Trees

Thesis ProposalJanuary 29, 1998

Peter A. Dinda

Committee

David O’Hallaron (chair)

Thomas Gross

Peter Steenkiste

Jaspal Subhlok

David Bakken (BBN)

2

Outline

• Responsive interactive applications

• Best effort real-time service

• Dynamic mapping problem

• History-based prediction approach

• Other approaches and related work

• Current results on simplified problem

• Proposed thesis work

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Interactive Application Model

AperiodicUser Action

MessageMessageHandler

Feedback

Activation tree

mouse_click()

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Acoustic Room Modeling

PhysicalSimulation

of Wave Eqn

impulse responses

Frequency response plots

Room model

Modify model

Speakers

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Other Applications• Image editing

– The Adobe Photoshop universe

• Computer aided design– Quake design optimization (Malcevic-97)

• Computational steering– CUMULUS (Geist-96), CAVE (Disz-95), ...

• Collaboration– Collaborative Planning (Zinky-DUTC-95), ...

• Securities trading– (Wolfe-Lau-95)

• Games– DIS (DIS-94)

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Responsiveness

• Timely feedback to individual user actions– Bound: response time tmax

• Jitter bound and resource usage hint– Bound: response time tmin

• Example: image editor drawing tool

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A Best Effort Real-time Service

• Execute the activation tree rooted at procedure() so that tmintexectmax

• No guarantees

• Responsiveness spec: bounds [tmin,tmax]

• Performance metric: fraction of trees that meet their bounds

MAP procedure() IN [tmin, tmax]

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Machine Model• Hosts on a LAN

– No centralized or coordinated scheduling, or reservations

– Other unrelated traffic exists– We are only a user

• Remote execution facility– Can execute any procedure on any host – RPC, DSM, DCE, CORBA, DCOM, ...

• Measurable - at least a good real-time clock exists (<1ms)

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Execution Model

• Dynamically map nodes of the unfolding activation tree to the hosts

• At each procedure call, choose which host is best suited to execute the call in order to meet the bounds on the tree

[tmin,tmax]

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Dynamic Mapping Problem

How do we map the nodes of the trees to the hosts so that the fraction of trees that satisfy their bounds is maximized?

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Aspects of My Approach

• History-based prediction

• Decomposition of bounds

• Adaptation of mapping algorithms during tree traversal

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History-based Prediction

• For each host, H0 predicts whether it can meet the bounds, based on past local history and then chooses one where it is possible

• Execution times include both communication for remote call and the actual computation

H0H1

H2

[t’min,t’max]

time, duration, boundstime, duration, bounds

...time, duration, bounds

time, duration, bounds...time, duration, bounds

time, duration, bounds...

foo()

bar()

H0

foo() is executing on H0

and calls bar(), which can be mapped to H0, H1, or H2

H0 has a local history of execution times of bar() on each of the other hosts

[tmin,tmax]

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Decomposition of Bounds

• Choice of [t’min,t’max] for bar() depends on unvisited portion of the tree

• Collect history of what fraction of time spent in foo() subtree was spent in bar() subtree

• Choose fraction of bounds to give to bar() based on that history and current time

[tmin,tmax]

[t’min,t’max]?

partially executed, known

unexecuted, known unexecuted, unknown

unexecuted, unknown

foo()

bar()

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Adaptation of Mapping Algorithms During Tree Traversal

• Tune strategy to how deep we are in the tree and how far along in the traversal

• Explore more aggresively early in the traversal, when the effect of a bad decision is easiest to overcome– Find interesting new hosts

• Spend less time making mapping decision deep in the tree– More likely to remain on single host

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Thesis Statement

Dynamic mapping of activation trees using history-based prediction and traversal-based adaptation is an effective way to build a best effort real-time service for responsive interactive applications running in conventional environments.

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Other Approaches

• Distributed soft real-time system– System modifications

• Dynamic load balancing system– Different goals

• Even distribution of load (OS-centric)

• Minimization of exec times (app-centric)

• Resource reservation system– System modifications

• Shared measurement system– Information level and dissemination

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Current Results

• Load trace collection and analysis

• Algorithms and evaluation for simplified dynamic mapping problem

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Algorithms and Evaluation for Simplified Problem

•Map only leaf nodes

•Ignore communication

for I=1 to N doMAP leaf_procedure() IN [tmin,tmax]

end

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RangeCounter(W): A Near Optimal Algorithm

• Each host has a quality level Q and a window of the last W execution times (W is small)

• Choose host with highest quality level, and age quality levels of all hosts: Q=Q-1

• If bounds are met, increase host’s quality level by the inverse of our confidence in it:

• If bounds are not met, reduce host’s quality level by half: Q=Q/2

Q Q

N t dtt

t

min

max

1

2( , , )

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Load Trace-based Simulation• Exec time computed from load trace

using a simple, validated model

• Mapping algorithms are given bounds, select a host, then are told exec time

• Simulator computes performance of– Algorithm under test– Optimal (precognizant) algorithm– Random mapping– Individual host mappings

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Scope of Evaluation

• 9 mapping algorithms

• 6 different groups of hosts– Chosen from 39 hosts– 1 week, 1 Hz load trace from each host

• 648 different cases– Combinations of nominal time and bounds

• 100,000 calls for each case

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0

10

20

30

40

50

60

70

80

90

100

0.01 0.1 1 10 100

tnominal (seconds)

Random

Best Individual Host

Optimal (RC)

8MM

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0

10

20

30

40

50

60

70

80

90

100

0.01 0.1 1 10 100

tnominal (seconds)

Random

Best Individual Host

RangeCounter(50)

Optimal (RC)

8MM

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0

10

20

30

40

50

60

70

80

90

100

1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3

tmax/tnominal

Random

Best Individual Host

Optimal(RC)

8MM, tnominal=100ms

25

0

10

20

30

40

50

60

70

80

90

100

1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3

tmax/tnominal

Random

Best Individual Host

RangeCounter(50)

Optimal(RC)

8MM, tnominal=100ms

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Proposed Work

• Extend simulation environment to include communication and activation trees• Trace collection (Activation trees, network)

» A trace for everything

• Trace characterization• Simulator extension

• Develop algorithm• Evaluate with benchmarks• Incorporate into real system

Extend current results to the full dynamic mapping problem

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Activation Tree Traces• Collect activation trees where each

node is annotated with compute time, and what data it references

• Goal is to instrument off-the-shelf MS Windows programs

• Other options exist

Contributions: Instrumentation tools/methodology, Activation tree trace database

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Network Traces• Realistic communication times

• Packet traces on Ethernet with tcpdump– Simple broadcast networks seem too

limiting

• Remos

• Existing trace databases

Contributions: Methodology,Trace database

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Trace Characterization

• Classify traces into families, from which we can draw benchmarks for evaluation

• Ideally, parameterized models to fit data

• Characterizing activation tree traces most challenging

Contributions: Trace analysis, Models, Classification scheme, Benchmark suite

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Simulator Extension

• Extend my existing simulator to support arbitrary activation trees and realistic communication

• Communication time model

Contributions: Simulator infrastructure for fulldynamic mapping problem

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Algorithm Development

• Use approaches described earlier

• Extend RangeCounter(W) with a separate algorithm to recursively divide bounds among subtrees

• Iterative development using simulator and benchmarks

Contributions: Algorithm(s)

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Evaluation• Evaluate algorithm in simulation

– Draw connections between benchmark characteristics and algorithm performance

• Compare with other approaches– Load monitor with simple heuristic– Greater degrees of information sharing

• Incorporate into distributed object system as proof of concept

Contributions: Evaluation, Working system

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Thesis Timeline

Activity Artifacts DurationTrace collection Measurement tools, traces 3 monthsTrace characterization Models, benchmarks 3 monthsSimulation extension Simulator for full problem 2 monthsAlgorithm development Mapping algorithm 5 monthsEvaluation Benchmark results, LDOS impl. 2 monthsWriting/defense Defended thesis 3 monthsTotal: 18 months